diff --git "a/generating-meiling-han-s-words-from-heaven-ii.ipynb" "b/generating-meiling-han-s-words-from-heaven-ii.ipynb" new file mode 100644--- /dev/null +++ "b/generating-meiling-han-s-words-from-heaven-ii.ipynb" @@ -0,0 +1,7175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2MbKJY38Puy9" + }, + "source": [ + "# Meiling Han's Words from Heaven" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.kaggle.com/artgor/dcgan-baseline\n", + "from __future__ import print_function\n", + "#%matplotlib inline\n", + "import argparse\n", + "import os\n", + "import random\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.parallel\n", + "import torch.backends.cudnn as cudnn\n", + "import torch.optim as optim\n", + "import torch.utils.data\n", + "import torchvision.datasets as dset\n", + "import torchvision.transforms as transforms\n", + "import torchvision.utils as vutils\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.animation as animation\n", + "from IPython.display import HTML\n", + "from torchvision.utils import save_image\n", + "from torch.optim.lr_scheduler import StepLR, ReduceLROnPlateau, CosineAnnealingLR\n", + "\n", + "def seed_everything(seed=42):\n", + " random.seed(seed)\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " np.random.seed(seed)\n", + " torch.manual_seed(seed)\n", + " torch.cuda.manual_seed(seed)\n", + " torch.backends.cudnn.deterministic = True\n", + "seed_everything()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(os.listdir('../input/daqian-oracle'))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Setting parameters\n", + "dataroot = \"../input\"\n", + "workers = 2\n", + "\n", + "batch_size = 128\n", + "image_size = 64\n", + "\n", + "# Number of channels\n", + "nc = 3\n", + "# Latent vector (i.e. size of generator input)\n", + "nz = 100\n", + "# Size of feature maps in generator\n", + "ngf = 64\n", + "# Size of feature maps in discriminator\n", + "ndf = 64\n", + "\n", + "# Number of training epochs\n", + "epochs = 1000\n", + "# Learning rate for optimizers\n", + "lr_gen = 0.001\n", + "lr_dis = 0.00001\n", + "# Beta1 hyperparam for Adam optimizers\n", + "beta1 = 0.3\n", + "ngpu = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = dset.ImageFolder(root=dataroot,\n", + " transform=transforms.Compose([\n", + " transforms.Resize((image_size+30, image_size+30)),\n", + " transforms.RandomCrop((image_size, image_size), padding=None, pad_if_needed=False, fill=1, padding_mode='constant'),\n", + " transforms.RandomHorizontalFlip(p=0.5), # could be improved\n", + " transforms.RandomVerticalFlip(p=0.5),\n", + "# transforms.RandomPerspective(distortion_scale=0.5, p=0.5, interpolation=3),\n", + "# transforms.RandomRotation((-20, +20), resample=False, expand=False, center=None, fill=1),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n", + " ]))\n", + "# Create the dataloader\n", + "dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,shuffle=True, num_workers=workers)\n", + "\n", + "# Decide which device we want to run on\n", + "device = torch.device(\"cuda:0\" if (torch.cuda.is_available() and ngpu > 0) else \"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "real_batch = next(iter(dataloader))\n", + "plt.figure(figsize=(10,10))\n", + "plt.axis(\"off\")\n", + "plt.title(\"Training Images\");\n", + "plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)));" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# def weights_init(m):\n", + "# classname = m.__class__.__name__\n", + "# if classname.find('Conv') != -1:\n", + "# nn.init.normal_(m.weight.data, 0.0, 0.02)\n", + "# elif classname.find('BatchNorm') != -1:\n", + "# nn.init.normal_(m.weight.data, 1.0, 0.02)\n", + "# nn.init.constant_(m.bias.data, 0)\n", + "\n", + "# ----------------------------------------------------------------------------\n", + "# Pixelwise feature vector normalization.\n", + "# reference: https://github.com/tkarras/progressive_growing_of_gans/blob/master/networks.py#L120\n", + "# ----------------------------------------------------------------------------\n", + "class PixelwiseNorm(nn.Module):\n", + " def __init__(self):\n", + " super(PixelwiseNorm, self).__init__()\n", + "\n", + " def forward(self, x, alpha=1e-8):\n", + " \"\"\"\n", + " forward pass of the module\n", + " :param x: input activations volume\n", + " :param alpha: small number for numerical stability\n", + " :return: y => pixel normalized activations\n", + " \"\"\"\n", + " y = x.pow(2.).mean(dim=1, keepdim=True).add(alpha).sqrt() # [N1HW]\n", + " y = x / y # normalize the input x volume\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.kaggle.com/phoenix9032/gan-dogs-starter-24-jul-custom-layers\n", + "def show_generated_img_all():\n", + " gen_z = torch.randn(32, nz, 1, 1, device=device)\n", + " gen_images = netG(gen_z).to(\"cpu\").clone().detach()\n", + " gen_images = gen_images.numpy().transpose(0, 2, 3, 1)\n", + " gen_images = (gen_images+1.0)/2.0\n", + " fig = plt.figure(figsize=(25, 16))\n", + " for ii, img in enumerate(gen_images):\n", + " ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])\n", + " plt.imshow(img)\n", + " #plt.savefig(filename) \n", + " \n", + "### This is to show one sample image for iteration of chosing\n", + "def show_generated_img():\n", + " noise = torch.randn(1, nz, 1, 1, device=device)\n", + " gen_image = netG(noise).to(\"cpu\").clone().detach().squeeze(0)\n", + " gen_image = gen_image.numpy().transpose(1, 2, 0)\n", + " gen_image = ((gen_image+1.0)/2.0)\n", + " plt.imshow(gen_image)\n", + " plt.show()\n", + "\n", + "class MinibatchStdDev(torch.nn.Module):\n", + " \"\"\"\n", + " Minibatch standard deviation layer for the discriminator\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " \"\"\"\n", + " derived class constructor\n", + " \"\"\"\n", + " super(MinibatchStdDev, self).__init__()\n", + "\n", + " def forward(self, x, alpha=1e-8):\n", + " \"\"\"\n", + " forward pass of the layer\n", + " :param x: input activation volume\n", + " :param alpha: small number for numerical stability\n", + " :return: y => x appended with standard deviation constant map\n", + " \"\"\"\n", + " batch_size, _, height, width = x.shape\n", + " # [B x C x H x W] Subtract mean over batch.\n", + " y = x - x.mean(dim=0, keepdim=True)\n", + " # [1 x C x H x W] Calc standard deviation over batch\n", + " y = torch.sqrt(y.pow(2.).mean(dim=0, keepdim=False) + alpha)\n", + "\n", + " # [1] Take average over feature_maps and pixels.\n", + " y = y.mean().view(1, 1, 1, 1)\n", + "\n", + " # [B x 1 x H x W] Replicate over group and pixels.\n", + " y = y.repeat(batch_size,1, height, width)\n", + "\n", + " # [B x C x H x W] Append as new feature_map.\n", + " y = torch.cat([x, y], 1)\n", + " # return the computed values:\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.nn.utils import spectral_norm\n", + "from torch.optim import lr_scheduler\n", + "\n", + "class Generator(nn.Module):\n", + " def __init__(self, nz, nfeats, nchannels):\n", + " super(Generator, self).__init__()\n", + "\n", + " # input is Z, going into a convolution\n", + " self.conv1 = spectral_norm(nn.ConvTranspose2d(nz, nfeats * 8, 4, 1, 0, bias=False))\n", + " #self.bn1 = nn.BatchNorm2d(nfeats * 8)\n", + " # state size. (nfeats*8) x 4 x 4\n", + " \n", + " self.conv2 = spectral_norm(nn.ConvTranspose2d(nfeats * 8, nfeats * 8, 4, 2, 1, bias=False))\n", + " #self.bn2 = nn.BatchNorm2d(nfeats * 8)\n", + " # state size. (nfeats*8) x 8 x 8\n", + " \n", + " self.conv3 = spectral_norm(nn.ConvTranspose2d(nfeats * 8, nfeats * 4, 4, 2, 1, bias=False))\n", + " #self.bn3 = nn.BatchNorm2d(nfeats * 4)\n", + " # state size. (nfeats*4) x 16 x 16\n", + " \n", + " self.conv4 = spectral_norm(nn.ConvTranspose2d(nfeats * 4, nfeats * 2, 4, 2, 1, bias=False))\n", + " #self.bn4 = nn.BatchNorm2d(nfeats * 2)\n", + " # state size. (nfeats * 2) x 32 x 32\n", + " \n", + " self.conv5 = spectral_norm(nn.ConvTranspose2d(nfeats * 2, nfeats, 4, 2, 1, bias=False))\n", + " #self.bn5 = nn.BatchNorm2d(nfeats)\n", + " # state size. (nfeats) x 64 x 64\n", + " \n", + " self.conv6 = spectral_norm(nn.ConvTranspose2d(nfeats, nchannels, 3, 1, 1, bias=False))\n", + " # state size. (nchannels) x 64 x 64\n", + " self.pixnorm = PixelwiseNorm()\n", + " def forward(self, x):\n", + " #x = F.leaky_relu(self.bn1(self.conv1(x)))\n", + " #x = F.leaky_relu(self.bn2(self.conv2(x)))\n", + " #x = F.leaky_relu(self.bn3(self.conv3(x)))\n", + " #x = F.leaky_relu(self.bn4(self.conv4(x)))\n", + " #x = F.leaky_relu(self.bn5(self.conv5(x)))\n", + " x = F.leaky_relu(self.conv1(x))\n", + " x = F.leaky_relu(self.conv2(x))\n", + " x = self.pixnorm(x)\n", + " x = F.leaky_relu(self.conv3(x))\n", + " x = self.pixnorm(x)\n", + " x = F.leaky_relu(self.conv4(x))\n", + " x = self.pixnorm(x)\n", + " x = F.leaky_relu(self.conv5(x))\n", + " x = self.pixnorm(x)\n", + " x = torch.tanh(self.conv6(x))\n", + " \n", + " return x\n", + "\n", + "\n", + "\n", + "class Discriminator(nn.Module):\n", + " def __init__(self, nchannels, nfeats):\n", + " super(Discriminator, self).__init__()\n", + "\n", + " # input is (nchannels) x 64 x 64\n", + " self.conv1 = nn.Conv2d(nchannels, nfeats, 4, 2, 1, bias=False)\n", + " # state size. (nfeats) x 32 x 32\n", + " \n", + " self.conv2 = spectral_norm(nn.Conv2d(nfeats, nfeats * 2, 4, 2, 1, bias=False))\n", + " self.bn2 = nn.BatchNorm2d(nfeats * 2)\n", + " # state size. (nfeats*2) x 16 x 16\n", + " \n", + " self.conv3 = spectral_norm(nn.Conv2d(nfeats * 2, nfeats * 4, 4, 2, 1, bias=False))\n", + " self.bn3 = nn.BatchNorm2d(nfeats * 4)\n", + " # state size. (nfeats*4) x 8 x 8\n", + " \n", + " self.conv4 = spectral_norm(nn.Conv2d(nfeats * 4, nfeats * 8, 4, 2, 1, bias=False))\n", + " self.bn4 = nn.MaxPool2d(2)\n", + " # state size. (nfeats*8) x 4 x 4\n", + " self.batch_discriminator = MinibatchStdDev()\n", + " self.pixnorm = PixelwiseNorm()\n", + " self.conv5 = spectral_norm(nn.Conv2d(nfeats * 8 +1, 1, 2, 1, 0, bias=False))\n", + " # state size. 1 x 1 x 1\n", + " \n", + " def forward(self, x):\n", + " x = F.leaky_relu(self.conv1(x), 0.2)\n", + " x = F.leaky_relu(self.bn2(self.conv2(x)), 0.2)\n", + " # x = self.pixnorm(x)\n", + " x = F.leaky_relu(self.bn3(self.conv3(x)), 0.2)\n", + " # x = self.pixnorm(x)\n", + " x = F.leaky_relu(self.bn4(self.conv4(x)), 0.2)\n", + " # x = self.pixnorm(x)\n", + " x = self.batch_discriminator(x)\n", + " x = torch.sigmoid(self.conv5(x))\n", + " #x= self.conv5(x)\n", + " return x.view(-1, 1)\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "lr = 0.0002\n", + "lr_d = 0.0002\n", + "beta1 = 0.5\n", + "epochs = 1600\n", + "netG = Generator(100, 32, 3).to(device)\n", + "netD = Discriminator(3, 48).to(device)\n", + "\n", + "criterion = nn.BCELoss()\n", + "#criterion = nn.MSELoss()\n", + "\n", + "optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))\n", + "optimizerG = optim.Adam(netG.parameters(), lr=lr_d, betas=(beta1, 0.999))\n", + "# lr_schedulerG = lr_scheduler.CosineAnnealingWarmRestarts(optimizerG, T_0=epochs//200, eta_min=0.00005)\n", + "# lr_schedulerD = lr_scheduler.CosineAnnealingWarmRestarts(optimizerD, T_0=epochs//200, eta_min=0.00005)\n", + "lr_schedulerG = lr_scheduler.MultiStepLR(optimizerG, milestones=[150, 200], gamma=0.1)\n", + "lr_schedulerD = lr_scheduler.MultiStepLR(optimizerD, milestones=[150, 200], gamma=0.1)\n", + "\n", + "nz = 100\n", + "fixed_noise = torch.randn(25, nz, 1, 1, device=device)\n", + "\n", + "real_label = 0.7\n", + "fake_label = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1/1600][0/8] Loss_D: 1.3745 Loss_G: 1.1584 D(x): 0.2953 D(G(z)): 0.3451 / 0.2136\n", + "[1/1600][2/8] Loss_D: 1.3876 Loss_G: 1.5426 D(x): 0.4108 D(G(z)): 0.4630 / 0.1174\n", + "[1/1600][4/8] Loss_D: 1.3883 Loss_G: 1.6096 D(x): 0.3125 D(G(z)): 0.3534 / 0.1058\n", + "[1/1600][6/8] Loss_D: 1.4643 Loss_G: 1.7134 D(x): 0.3552 D(G(z)): 0.4244 / 0.0907\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2/1600][0/8] Loss_D: 1.2696 Loss_G: 1.9354 D(x): 0.3918 D(G(z)): 0.3740 / 0.0651\n", + "[2/1600][2/8] Loss_D: 1.0612 Loss_G: 2.3136 D(x): 0.4585 D(G(z)): 0.2656 / 0.0375\n", + "[2/1600][4/8] Loss_D: 1.1479 Loss_G: 2.9112 D(x): 0.4905 D(G(z)): 0.3480 / 0.0158\n", + "[2/1600][6/8] Loss_D: 1.0354 Loss_G: 3.0998 D(x): 0.4180 D(G(z)): 0.2064 / 0.0120\n", + "[3/1600][0/8] Loss_D: 1.1398 Loss_G: 3.2646 D(x): 0.5395 D(G(z)): 0.3483 / 0.0095\n", + "[3/1600][2/8] Loss_D: 1.0883 Loss_G: 3.0464 D(x): 0.5141 D(G(z)): 0.3096 / 0.0130\n", + "[3/1600][4/8] Loss_D: 0.9389 Loss_G: 3.2485 D(x): 0.5384 D(G(z)): 0.1984 / 0.0097\n", + "[3/1600][6/8] Loss_D: 0.9550 Loss_G: 3.3348 D(x): 0.4527 D(G(z)): 0.1744 / 0.0086\n", + "[4/1600][0/8] Loss_D: 0.8545 Loss_G: 3.3595 D(x): 0.5203 D(G(z)): 0.1272 / 0.0083\n", + "[4/1600][2/8] Loss_D: 0.7871 Loss_G: 3.4227 D(x): 0.6409 D(G(z)): 0.1559 / 0.0076\n", + "[4/1600][4/8] Loss_D: 0.8161 Loss_G: 3.5808 D(x): 0.6380 D(G(z)): 0.0974 / 0.0060\n", + "[4/1600][6/8] Loss_D: 0.8480 Loss_G: 3.0416 D(x): 0.4866 D(G(z)): 0.0957 / 0.0131\n", + "[5/1600][0/8] Loss_D: 0.8263 Loss_G: 3.6083 D(x): 0.7093 D(G(z)): 0.1218 / 0.0058\n", + "[5/1600][2/8] Loss_D: 0.8021 Loss_G: 3.3541 D(x): 0.5016 D(G(z)): 0.0748 / 0.0083\n", + "[5/1600][4/8] Loss_D: 0.8987 Loss_G: 3.6839 D(x): 0.6493 D(G(z)): 0.1680 / 0.0052\n", + "[5/1600][6/8] Loss_D: 0.7245 Loss_G: 3.2933 D(x): 0.6654 D(G(z)): 0.1127 / 0.0091\n", + "[6/1600][0/8] Loss_D: 0.6609 Loss_G: 3.4226 D(x): 0.6367 D(G(z)): 0.0547 / 0.0076\n", + "[6/1600][2/8] Loss_D: 0.7488 Loss_G: 3.5163 D(x): 0.6509 D(G(z)): 0.0801 / 0.0066\n", + "[6/1600][4/8] Loss_D: 0.7116 Loss_G: 3.4650 D(x): 0.6661 D(G(z)): 0.1217 / 0.0071\n", + "[6/1600][6/8] Loss_D: 0.6665 Loss_G: 3.5114 D(x): 0.6080 D(G(z)): 0.0383 / 0.0067\n", + "[7/1600][0/8] Loss_D: 0.7028 Loss_G: 3.9324 D(x): 0.6776 D(G(z)): 0.0705 / 0.0036\n", + "[7/1600][2/8] Loss_D: 0.7790 Loss_G: 3.4968 D(x): 0.4949 D(G(z)): 0.0545 / 0.0068\n", + "[7/1600][4/8] Loss_D: 0.7147 Loss_G: 3.4625 D(x): 0.6383 D(G(z)): 0.1058 / 0.0071\n", + "[7/1600][6/8] Loss_D: 0.7409 Loss_G: 3.7767 D(x): 0.7081 D(G(z)): 0.0400 / 0.0045\n", + "[8/1600][0/8] Loss_D: 0.7295 Loss_G: 3.0641 D(x): 0.5814 D(G(z)): 0.0862 / 0.0126\n", + "[8/1600][2/8] Loss_D: 0.6038 Loss_G: 3.8505 D(x): 0.7315 D(G(z)): 0.0270 / 0.0041\n", + "[8/1600][4/8] Loss_D: 0.7081 Loss_G: 3.9102 D(x): 0.7478 D(G(z)): 0.1224 / 0.0038\n", + "[8/1600][6/8] Loss_D: 0.7171 Loss_G: 3.5051 D(x): 0.5922 D(G(z)): 0.0384 / 0.0067\n", + "[9/1600][0/8] Loss_D: 0.8522 Loss_G: 4.3000 D(x): 0.7405 D(G(z)): 0.0960 / 0.0022\n", + "[9/1600][2/8] Loss_D: 0.7239 Loss_G: 3.3994 D(x): 0.6528 D(G(z)): 0.0390 / 0.0078\n", + "[9/1600][4/8] Loss_D: 0.7237 Loss_G: 3.8405 D(x): 0.7511 D(G(z)): 0.0805 / 0.0042\n", + "[9/1600][6/8] Loss_D: 0.6399 Loss_G: 3.0667 D(x): 0.5981 D(G(z)): 0.0272 / 0.0126\n", + "[10/1600][0/8] Loss_D: 0.7016 Loss_G: 3.7688 D(x): 0.6994 D(G(z)): 0.0337 / 0.0046\n", + "[10/1600][2/8] Loss_D: 0.8211 Loss_G: 3.6606 D(x): 0.7256 D(G(z)): 0.0908 / 0.0054\n", + "[10/1600][4/8] Loss_D: 0.7995 Loss_G: 3.6909 D(x): 0.7768 D(G(z)): 0.0558 / 0.0051\n", + "[10/1600][6/8] Loss_D: 0.6585 Loss_G: 3.4540 D(x): 0.7531 D(G(z)): 0.0580 / 0.0072\n", + "[11/1600][0/8] Loss_D: 0.7165 Loss_G: 3.5138 D(x): 0.5352 D(G(z)): 0.0234 / 0.0066\n", + "[11/1600][2/8] Loss_D: 0.8115 Loss_G: 3.9016 D(x): 0.7423 D(G(z)): 0.0767 / 0.0038\n", + "[11/1600][4/8] Loss_D: 0.7253 Loss_G: 3.2801 D(x): 0.5678 D(G(z)): 0.0316 / 0.0093\n", + "[11/1600][6/8] Loss_D: 0.7061 Loss_G: 3.6478 D(x): 0.7307 D(G(z)): 0.0630 / 0.0055\n", + "[12/1600][0/8] Loss_D: 0.6588 Loss_G: 3.2936 D(x): 0.6932 D(G(z)): 0.0449 / 0.0091\n", + "[12/1600][2/8] Loss_D: 0.7401 Loss_G: 3.9940 D(x): 0.7397 D(G(z)): 0.0606 / 0.0033\n", + "[12/1600][4/8] Loss_D: 0.8528 Loss_G: 3.9164 D(x): 0.7940 D(G(z)): 0.0932 / 0.0037\n", + "[12/1600][6/8] Loss_D: 0.6822 Loss_G: 3.2263 D(x): 0.7109 D(G(z)): 0.0349 / 0.0100\n", + "[13/1600][0/8] Loss_D: 0.7663 Loss_G: 2.3725 D(x): 0.5028 D(G(z)): 0.0354 / 0.0343\n", + "[13/1600][2/8] Loss_D: 0.7460 Loss_G: 4.0865 D(x): 0.4851 D(G(z)): 0.0054 / 0.0029\n", + "[13/1600][4/8] Loss_D: 0.6985 Loss_G: 3.2865 D(x): 0.8109 D(G(z)): 0.0504 / 0.0092\n", + "[13/1600][6/8] Loss_D: 0.7451 Loss_G: 2.6728 D(x): 0.5038 D(G(z)): 0.0141 / 0.0222\n", + "[14/1600][0/8] Loss_D: 0.7490 Loss_G: 3.5229 D(x): 0.5712 D(G(z)): 0.0294 / 0.0065\n", + "[14/1600][2/8] Loss_D: 0.7384 Loss_G: 3.2284 D(x): 0.7383 D(G(z)): 0.1007 / 0.0100\n", + "[14/1600][4/8] Loss_D: 0.7203 Loss_G: 3.5079 D(x): 0.6543 D(G(z)): 0.0322 / 0.0067\n", + "[14/1600][6/8] Loss_D: 0.7154 Loss_G: 3.6060 D(x): 0.6690 D(G(z)): 0.0795 / 0.0058\n", + "[15/1600][0/8] Loss_D: 0.8182 Loss_G: 3.1526 D(x): 0.4845 D(G(z)): 0.0235 / 0.0111\n", + "[15/1600][2/8] Loss_D: 0.6746 Loss_G: 4.3814 D(x): 0.8433 D(G(z)): 0.0897 / 0.0019\n", + "[15/1600][4/8] Loss_D: 0.6454 Loss_G: 3.5925 D(x): 0.7095 D(G(z)): 0.0166 / 0.0059\n", + "[15/1600][6/8] Loss_D: 0.7583 Loss_G: 2.9399 D(x): 0.5155 D(G(z)): 0.0350 / 0.0151\n", + "[16/1600][0/8] Loss_D: 0.5841 Loss_G: 3.5209 D(x): 0.7748 D(G(z)): 0.0459 / 0.0066\n", + "[16/1600][2/8] Loss_D: 0.7745 Loss_G: 4.0067 D(x): 0.7513 D(G(z)): 0.0218 / 0.0033\n", + "[16/1600][4/8] Loss_D: 0.6079 Loss_G: 3.2196 D(x): 0.7579 D(G(z)): 0.0759 / 0.0101\n", + "[16/1600][6/8] Loss_D: 0.7271 Loss_G: 3.3740 D(x): 0.5016 D(G(z)): 0.0139 / 0.0081\n", + "[17/1600][0/8] Loss_D: 0.6342 Loss_G: 3.3805 D(x): 0.6115 D(G(z)): 0.0285 / 0.0080\n", + "[17/1600][2/8] Loss_D: 0.6535 Loss_G: 3.5478 D(x): 0.7164 D(G(z)): 0.0435 / 0.0063\n", + "[17/1600][4/8] Loss_D: 0.7090 Loss_G: 2.9519 D(x): 0.5365 D(G(z)): 0.0288 / 0.0148\n", + "[17/1600][6/8] Loss_D: 0.7066 Loss_G: 4.1828 D(x): 0.7627 D(G(z)): 0.0214 / 0.0025\n", + "[18/1600][0/8] Loss_D: 0.6082 Loss_G: 2.9727 D(x): 0.7024 D(G(z)): 0.0303 / 0.0144\n", + "[18/1600][2/8] Loss_D: 0.6283 Loss_G: 3.7489 D(x): 0.6490 D(G(z)): 0.0165 / 0.0047\n", + "[18/1600][4/8] Loss_D: 0.5939 Loss_G: 3.3425 D(x): 0.7750 D(G(z)): 0.0395 / 0.0085\n", + "[18/1600][6/8] Loss_D: 0.6988 Loss_G: 3.2823 D(x): 0.5666 D(G(z)): 0.0205 / 0.0092\n", + "[19/1600][0/8] Loss_D: 0.7029 Loss_G: 3.7807 D(x): 0.6632 D(G(z)): 0.0327 / 0.0045\n", + "[19/1600][2/8] Loss_D: 0.7660 Loss_G: 2.2604 D(x): 0.4738 D(G(z)): 0.0186 / 0.0403\n", + "[19/1600][4/8] Loss_D: 0.7532 Loss_G: 4.1231 D(x): 0.4953 D(G(z)): 0.0043 / 0.0028\n", + "[19/1600][6/8] Loss_D: 0.7950 Loss_G: 3.5547 D(x): 0.7761 D(G(z)): 0.0253 / 0.0063\n", + "[20/1600][0/8] Loss_D: 0.6228 Loss_G: 2.8079 D(x): 0.6917 D(G(z)): 0.0364 / 0.0183\n", + "[20/1600][2/8] Loss_D: 0.6077 Loss_G: 3.2469 D(x): 0.7015 D(G(z)): 0.0337 / 0.0097\n", + "[20/1600][4/8] Loss_D: 0.7505 Loss_G: 3.7522 D(x): 0.6324 D(G(z)): 0.0458 / 0.0047\n", + "[20/1600][6/8] Loss_D: 0.8973 Loss_G: 5.4691 D(x): 0.7635 D(G(z)): 0.2024 / 0.0004\n", + "[21/1600][0/8] Loss_D: 0.7378 Loss_G: 3.3850 D(x): 0.5131 D(G(z)): 0.0039 / 0.0080\n", + "[21/1600][2/8] Loss_D: 0.5895 Loss_G: 3.2425 D(x): 0.7632 D(G(z)): 0.0441 / 0.0098\n", + "[21/1600][4/8] Loss_D: 0.6724 Loss_G: 3.3525 D(x): 0.6281 D(G(z)): 0.0181 / 0.0084\n", + "[21/1600][6/8] Loss_D: 0.7684 Loss_G: 3.4690 D(x): 0.6816 D(G(z)): 0.0478 / 0.0071\n", + "[22/1600][0/8] Loss_D: 0.6667 Loss_G: 2.5495 D(x): 0.5853 D(G(z)): 0.0295 / 0.0266\n", + "[22/1600][2/8] Loss_D: 0.6845 Loss_G: 4.1145 D(x): 0.6738 D(G(z)): 0.0112 / 0.0028\n", + "[22/1600][4/8] Loss_D: 0.7132 Loss_G: 2.5867 D(x): 0.5928 D(G(z)): 0.0228 / 0.0256\n", + "[22/1600][6/8] Loss_D: 0.7019 Loss_G: 3.6939 D(x): 0.6411 D(G(z)): 0.0277 / 0.0056\n", + "[23/1600][0/8] Loss_D: 0.7184 Loss_G: 3.4029 D(x): 0.7193 D(G(z)): 0.0899 / 0.0091\n", + "[23/1600][2/8] Loss_D: 0.8567 Loss_G: 2.3561 D(x): 0.4125 D(G(z)): 0.0145 / 0.0405\n", + "[23/1600][4/8] Loss_D: 0.8752 Loss_G: 3.2181 D(x): 0.6136 D(G(z)): 0.1341 / 0.0271\n", + "[23/1600][6/8] Loss_D: 0.9883 Loss_G: 2.7924 D(x): 0.6880 D(G(z)): 0.2763 / 0.0494\n", + "[24/1600][0/8] Loss_D: 1.1506 Loss_G: 3.5215 D(x): 0.7362 D(G(z)): 0.2758 / 0.0177\n", + "[24/1600][2/8] Loss_D: 1.0310 Loss_G: 1.8555 D(x): 0.5781 D(G(z)): 0.2193 / 0.1231\n", + "[24/1600][4/8] Loss_D: 1.0474 Loss_G: 1.3722 D(x): 0.3846 D(G(z)): 0.1171 / 0.1994\n", + "[24/1600][6/8] Loss_D: 0.9614 Loss_G: 2.3015 D(x): 0.5740 D(G(z)): 0.2119 / 0.0525\n", + "[25/1600][0/8] Loss_D: 0.8802 Loss_G: 1.6575 D(x): 0.5989 D(G(z)): 0.2053 / 0.1195\n", + "[25/1600][2/8] Loss_D: 0.9866 Loss_G: 1.6100 D(x): 0.3766 D(G(z)): 0.0643 / 0.1209\n", + "[25/1600][4/8] Loss_D: 0.8670 Loss_G: 2.8141 D(x): 0.6545 D(G(z)): 0.1716 / 0.0214\n", + "[25/1600][6/8] Loss_D: 0.8357 Loss_G: 1.8258 D(x): 0.6477 D(G(z)): 0.1822 / 0.0929\n", + "[26/1600][0/8] Loss_D: 0.9215 Loss_G: 2.0360 D(x): 0.4427 D(G(z)): 0.0964 / 0.0686\n", + "[26/1600][2/8] Loss_D: 0.8976 Loss_G: 2.9611 D(x): 0.6437 D(G(z)): 0.1841 / 0.0185\n", + "[26/1600][4/8] Loss_D: 0.9621 Loss_G: 3.0018 D(x): 0.8192 D(G(z)): 0.3146 / 0.0189\n", + "[26/1600][6/8] Loss_D: 0.7423 Loss_G: 1.5725 D(x): 0.6653 D(G(z)): 0.1137 / 0.1363\n", + "[27/1600][0/8] Loss_D: 0.8343 Loss_G: 2.4209 D(x): 0.4738 D(G(z)): 0.0357 / 0.0397\n", + "[27/1600][2/8] Loss_D: 0.7901 Loss_G: 2.1640 D(x): 0.7583 D(G(z)): 0.1548 / 0.0528\n", + "[27/1600][4/8] Loss_D: 0.8880 Loss_G: 2.0638 D(x): 0.4087 D(G(z)): 0.0381 / 0.0655\n", + "[27/1600][6/8] Loss_D: 0.9445 Loss_G: 2.6937 D(x): 0.5455 D(G(z)): 0.1848 / 0.0253\n", + "[28/1600][0/8] Loss_D: 0.7603 Loss_G: 2.4354 D(x): 0.6033 D(G(z)): 0.0814 / 0.0334\n", + "[28/1600][2/8] Loss_D: 0.8675 Loss_G: 3.0334 D(x): 0.4006 D(G(z)): 0.0153 / 0.0137\n", + "[28/1600][4/8] Loss_D: 1.1018 Loss_G: 4.1000 D(x): 0.7682 D(G(z)): 0.2553 / 0.0031\n", + "[28/1600][6/8] Loss_D: 0.7704 Loss_G: 1.6762 D(x): 0.6144 D(G(z)): 0.1044 / 0.1027\n", + "[29/1600][0/8] Loss_D: 0.7939 Loss_G: 2.9568 D(x): 0.5626 D(G(z)): 0.0698 / 0.0170\n", + "[29/1600][2/8] Loss_D: 1.2311 Loss_G: 3.6940 D(x): 0.7363 D(G(z)): 0.4055 / 0.0060\n", + "[29/1600][4/8] Loss_D: 0.8753 Loss_G: 1.8667 D(x): 0.5121 D(G(z)): 0.1207 / 0.0865\n", + "[29/1600][6/8] Loss_D: 0.9167 Loss_G: 1.9763 D(x): 0.3741 D(G(z)): 0.0687 / 0.0707\n", + "[30/1600][0/8] Loss_D: 0.7517 Loss_G: 2.2184 D(x): 0.7405 D(G(z)): 0.1825 / 0.0504\n", + "[30/1600][2/8] Loss_D: 0.8000 Loss_G: 2.4264 D(x): 0.4753 D(G(z)): 0.0330 / 0.0380\n", + "[30/1600][4/8] Loss_D: 0.7933 Loss_G: 2.6470 D(x): 0.5899 D(G(z)): 0.0677 / 0.0291\n", + "[30/1600][6/8] Loss_D: 0.9436 Loss_G: 2.7983 D(x): 0.6959 D(G(z)): 0.1859 / 0.0221\n", + "[31/1600][0/8] Loss_D: 1.0081 Loss_G: 2.6749 D(x): 0.7937 D(G(z)): 0.3031 / 0.0262\n", + "[31/1600][2/8] Loss_D: 0.7987 Loss_G: 1.8833 D(x): 0.5500 D(G(z)): 0.0852 / 0.0790\n", + "[31/1600][4/8] Loss_D: 0.9769 Loss_G: 1.7210 D(x): 0.4218 D(G(z)): 0.0982 / 0.0951\n", + "[31/1600][6/8] Loss_D: 0.8854 Loss_G: 2.5300 D(x): 0.4405 D(G(z)): 0.0650 / 0.0317\n", + "[32/1600][0/8] Loss_D: 0.9400 Loss_G: 2.6706 D(x): 0.6309 D(G(z)): 0.2259 / 0.0254\n", + "[32/1600][2/8] Loss_D: 0.9795 Loss_G: 3.1619 D(x): 0.7526 D(G(z)): 0.2793 / 0.0149\n", + "[32/1600][4/8] Loss_D: 0.9194 Loss_G: 2.0319 D(x): 0.5524 D(G(z)): 0.1635 / 0.0657\n", + "[32/1600][6/8] Loss_D: 0.9157 Loss_G: 1.9284 D(x): 0.4617 D(G(z)): 0.0618 / 0.0782\n", + "[33/1600][0/8] Loss_D: 0.8975 Loss_G: 2.0848 D(x): 0.4562 D(G(z)): 0.0329 / 0.0650\n", + "[33/1600][2/8] Loss_D: 0.8486 Loss_G: 2.5849 D(x): 0.4639 D(G(z)): 0.0384 / 0.0270\n", + "[33/1600][4/8] Loss_D: 0.9313 Loss_G: 2.4966 D(x): 0.7639 D(G(z)): 0.2252 / 0.0316\n", + "[33/1600][6/8] Loss_D: 0.8018 Loss_G: 2.2156 D(x): 0.6868 D(G(z)): 0.1561 / 0.0459\n", + "[34/1600][0/8] Loss_D: 0.9731 Loss_G: 2.1839 D(x): 0.4387 D(G(z)): 0.1291 / 0.0473\n", + "[34/1600][2/8] Loss_D: 0.9778 Loss_G: 2.7173 D(x): 0.6762 D(G(z)): 0.3072 / 0.0231\n", + "[34/1600][4/8] Loss_D: 0.9951 Loss_G: 3.0018 D(x): 0.7927 D(G(z)): 0.3114 / 0.0151\n", + "[34/1600][6/8] Loss_D: 0.8175 Loss_G: 1.8186 D(x): 0.6359 D(G(z)): 0.1725 / 0.0834\n", + "[35/1600][0/8] Loss_D: 1.3869 Loss_G: 1.5440 D(x): 0.2094 D(G(z)): 0.0237 / 0.1316\n", + "[35/1600][2/8] Loss_D: 1.0079 Loss_G: 2.2322 D(x): 0.3331 D(G(z)): 0.0303 / 0.0457\n", + "[35/1600][4/8] Loss_D: 0.8361 Loss_G: 2.3287 D(x): 0.6944 D(G(z)): 0.1702 / 0.0395\n", + "[35/1600][6/8] Loss_D: 0.9575 Loss_G: 2.7514 D(x): 0.6024 D(G(z)): 0.1961 / 0.0218\n", + "[36/1600][0/8] Loss_D: 0.8775 Loss_G: 2.0726 D(x): 0.6215 D(G(z)): 0.1825 / 0.0584\n", + "[36/1600][2/8] Loss_D: 0.9216 Loss_G: 2.1289 D(x): 0.4225 D(G(z)): 0.0566 / 0.0515\n", + "[36/1600][4/8] Loss_D: 0.8186 Loss_G: 2.0098 D(x): 0.6145 D(G(z)): 0.1490 / 0.0611\n", + "[36/1600][6/8] Loss_D: 0.8421 Loss_G: 2.2954 D(x): 0.5401 D(G(z)): 0.1092 / 0.0416\n", + "[37/1600][0/8] Loss_D: 1.0151 Loss_G: 3.0891 D(x): 0.6220 D(G(z)): 0.2273 / 0.0141\n", + "[37/1600][2/8] Loss_D: 1.0724 Loss_G: 2.8211 D(x): 0.6365 D(G(z)): 0.3192 / 0.0193\n", + "[37/1600][4/8] Loss_D: 0.8452 Loss_G: 2.2087 D(x): 0.6440 D(G(z)): 0.1818 / 0.0486\n", + "[37/1600][6/8] Loss_D: 1.0197 Loss_G: 1.8388 D(x): 0.3093 D(G(z)): 0.0440 / 0.0834\n", + "[38/1600][0/8] Loss_D: 0.8780 Loss_G: 2.2578 D(x): 0.4653 D(G(z)): 0.1028 / 0.0446\n", + "[38/1600][2/8] Loss_D: 1.0230 Loss_G: 3.2766 D(x): 0.7145 D(G(z)): 0.3136 / 0.0098\n", + "[38/1600][4/8] Loss_D: 0.8412 Loss_G: 2.2129 D(x): 0.5822 D(G(z)): 0.1251 / 0.0459\n", + "[38/1600][6/8] Loss_D: 0.9418 Loss_G: 2.1813 D(x): 0.3220 D(G(z)): 0.0391 / 0.0471\n", + "[39/1600][0/8] Loss_D: 0.7912 Loss_G: 2.4564 D(x): 0.5416 D(G(z)): 0.0762 / 0.0324\n", + "[39/1600][2/8] Loss_D: 0.8149 Loss_G: 2.0868 D(x): 0.4765 D(G(z)): 0.0553 / 0.0543\n", + "[39/1600][4/8] Loss_D: 0.8242 Loss_G: 3.3970 D(x): 0.6706 D(G(z)): 0.1012 / 0.0086\n", + "[39/1600][6/8] Loss_D: 1.0859 Loss_G: 3.1492 D(x): 0.8039 D(G(z)): 0.2554 / 0.0118\n", + "[40/1600][0/8] Loss_D: 0.7765 Loss_G: 1.8399 D(x): 0.5661 D(G(z)): 0.0701 / 0.0799\n", + "[40/1600][2/8] Loss_D: 0.7979 Loss_G: 2.9493 D(x): 0.5883 D(G(z)): 0.0599 / 0.0161\n", + "[40/1600][4/8] Loss_D: 0.8485 Loss_G: 2.1732 D(x): 0.7185 D(G(z)): 0.1747 / 0.0505\n", + "[40/1600][6/8] Loss_D: 0.9049 Loss_G: 2.0009 D(x): 0.4069 D(G(z)): 0.0496 / 0.0638\n", + "[41/1600][0/8] Loss_D: 0.8231 Loss_G: 2.5663 D(x): 0.5682 D(G(z)): 0.1037 / 0.0279\n", + "[41/1600][2/8] Loss_D: 0.8628 Loss_G: 2.3887 D(x): 0.6132 D(G(z)): 0.1464 / 0.0359\n", + "[41/1600][4/8] Loss_D: 0.8493 Loss_G: 2.2833 D(x): 0.6658 D(G(z)): 0.2119 / 0.0445\n", + "[41/1600][6/8] Loss_D: 0.9368 Loss_G: 1.5351 D(x): 0.4089 D(G(z)): 0.0586 / 0.1320\n", + "[42/1600][0/8] Loss_D: 0.9254 Loss_G: 2.2709 D(x): 0.3730 D(G(z)): 0.0391 / 0.0455\n", + "[42/1600][2/8] Loss_D: 0.9503 Loss_G: 3.0774 D(x): 0.6906 D(G(z)): 0.2149 / 0.0144\n", + "[42/1600][4/8] Loss_D: 0.9620 Loss_G: 2.9010 D(x): 0.6807 D(G(z)): 0.2023 / 0.0187\n", + "[42/1600][6/8] Loss_D: 0.7925 Loss_G: 2.4246 D(x): 0.5966 D(G(z)): 0.0762 / 0.0383\n", + "[43/1600][0/8] Loss_D: 0.8584 Loss_G: 2.6018 D(x): 0.4963 D(G(z)): 0.0966 / 0.0267\n", + "[43/1600][2/8] Loss_D: 0.9351 Loss_G: 2.8887 D(x): 0.5542 D(G(z)): 0.1818 / 0.0197\n", + "[43/1600][4/8] Loss_D: 1.0242 Loss_G: 3.3255 D(x): 0.7976 D(G(z)): 0.3339 / 0.0118\n", + "[43/1600][6/8] Loss_D: 0.8945 Loss_G: 2.1604 D(x): 0.4861 D(G(z)): 0.0246 / 0.0629\n", + "[44/1600][0/8] Loss_D: 0.7933 Loss_G: 2.5627 D(x): 0.7330 D(G(z)): 0.1262 / 0.0337\n", + "[44/1600][2/8] Loss_D: 0.8586 Loss_G: 1.9574 D(x): 0.5248 D(G(z)): 0.0732 / 0.0685\n", + "[44/1600][4/8] Loss_D: 0.9526 Loss_G: 2.1783 D(x): 0.4937 D(G(z)): 0.1031 / 0.0483\n", + "[44/1600][6/8] Loss_D: 1.0249 Loss_G: 2.0770 D(x): 0.5318 D(G(z)): 0.2274 / 0.0556\n", + "[45/1600][0/8] Loss_D: 0.8890 Loss_G: 2.5339 D(x): 0.5557 D(G(z)): 0.1423 / 0.0295\n", + "[45/1600][2/8] Loss_D: 0.9312 Loss_G: 2.5759 D(x): 0.6046 D(G(z)): 0.1895 / 0.0284\n", + "[45/1600][4/8] Loss_D: 0.9596 Loss_G: 2.9634 D(x): 0.7601 D(G(z)): 0.2432 / 0.0155\n", + "[45/1600][6/8] Loss_D: 0.7817 Loss_G: 1.7641 D(x): 0.6207 D(G(z)): 0.1063 / 0.0992\n", + "[46/1600][0/8] Loss_D: 1.1247 Loss_G: 1.8995 D(x): 0.3175 D(G(z)): 0.0203 / 0.0782\n", + "[46/1600][2/8] Loss_D: 0.8565 Loss_G: 1.9918 D(x): 0.4880 D(G(z)): 0.0624 / 0.0660\n", + "[46/1600][4/8] Loss_D: 0.8717 Loss_G: 2.1987 D(x): 0.5047 D(G(z)): 0.0979 / 0.0496\n", + "[46/1600][6/8] Loss_D: 0.9311 Loss_G: 1.7542 D(x): 0.5253 D(G(z)): 0.1668 / 0.0911\n", + "[47/1600][0/8] Loss_D: 0.9509 Loss_G: 1.9466 D(x): 0.4177 D(G(z)): 0.1045 / 0.0683\n", + "[47/1600][2/8] Loss_D: 0.8547 Loss_G: 2.3290 D(x): 0.5416 D(G(z)): 0.1291 / 0.0385\n", + "[47/1600][4/8] Loss_D: 0.8116 Loss_G: 3.0467 D(x): 0.6873 D(G(z)): 0.1083 / 0.0139\n", + "[47/1600][6/8] Loss_D: 0.6781 Loss_G: 2.2962 D(x): 0.7257 D(G(z)): 0.1132 / 0.0404\n", + "[48/1600][0/8] Loss_D: 0.9300 Loss_G: 1.9455 D(x): 0.3784 D(G(z)): 0.0186 / 0.0697\n", + "[48/1600][2/8] Loss_D: 0.8739 Loss_G: 2.3249 D(x): 0.4258 D(G(z)): 0.0316 / 0.0408\n", + "[48/1600][4/8] Loss_D: 0.9254 Loss_G: 3.3054 D(x): 0.7441 D(G(z)): 0.1506 / 0.0099\n", + "[48/1600][6/8] Loss_D: 0.8688 Loss_G: 2.2534 D(x): 0.5904 D(G(z)): 0.1071 / 0.0440\n", + "[49/1600][0/8] Loss_D: 0.9178 Loss_G: 2.7657 D(x): 0.6762 D(G(z)): 0.1724 / 0.0200\n", + "[49/1600][2/8] Loss_D: 0.8422 Loss_G: 2.3197 D(x): 0.5043 D(G(z)): 0.0717 / 0.0381\n", + "[49/1600][4/8] Loss_D: 0.9619 Loss_G: 3.0635 D(x): 0.6149 D(G(z)): 0.2106 / 0.0132\n", + "[49/1600][6/8] Loss_D: 0.9124 Loss_G: 2.6720 D(x): 0.6755 D(G(z)): 0.2408 / 0.0242\n", + "[50/1600][0/8] Loss_D: 0.8547 Loss_G: 2.5840 D(x): 0.4904 D(G(z)): 0.0770 / 0.0296\n", + "[50/1600][2/8] Loss_D: 0.8716 Loss_G: 3.0320 D(x): 0.6500 D(G(z)): 0.1751 / 0.0165\n", + "[50/1600][4/8] Loss_D: 0.7636 Loss_G: 2.4475 D(x): 0.5471 D(G(z)): 0.0525 / 0.0328\n", + "[50/1600][6/8] Loss_D: 0.7678 Loss_G: 2.3701 D(x): 0.5129 D(G(z)): 0.0444 / 0.0353\n", + "[51/1600][0/8] Loss_D: 0.7184 Loss_G: 3.0019 D(x): 0.6414 D(G(z)): 0.0652 / 0.0142\n", + "[51/1600][2/8] Loss_D: 0.8067 Loss_G: 2.9806 D(x): 0.6723 D(G(z)): 0.1137 / 0.0148\n", + 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"[54/1600][2/8] Loss_D: 0.8983 Loss_G: 1.7891 D(x): 0.3828 D(G(z)): 0.0443 / 0.0903\n", + "[54/1600][4/8] Loss_D: 0.9149 Loss_G: 2.2648 D(x): 0.3408 D(G(z)): 0.0179 / 0.0445\n", + "[54/1600][6/8] Loss_D: 0.8560 Loss_G: 2.9681 D(x): 0.7367 D(G(z)): 0.2142 / 0.0161\n", + "[55/1600][0/8] Loss_D: 0.8867 Loss_G: 2.1492 D(x): 0.5972 D(G(z)): 0.1512 / 0.0549\n", + "[55/1600][2/8] Loss_D: 1.0335 Loss_G: 0.9640 D(x): 0.3713 D(G(z)): 0.0599 / 0.3199\n", + "[55/1600][4/8] Loss_D: 2.1667 Loss_G: 3.0242 D(x): 0.0709 D(G(z)): 0.0022 / 0.0166\n", + "[55/1600][6/8] Loss_D: 0.7417 Loss_G: 1.6684 D(x): 0.6790 D(G(z)): 0.1129 / 0.1180\n", + "[56/1600][0/8] Loss_D: 0.7844 Loss_G: 2.4646 D(x): 0.5582 D(G(z)): 0.0674 / 0.0338\n", + "[56/1600][2/8] Loss_D: 0.7846 Loss_G: 2.0349 D(x): 0.5105 D(G(z)): 0.0437 / 0.0609\n", + "[56/1600][4/8] Loss_D: 0.8821 Loss_G: 2.3723 D(x): 0.7047 D(G(z)): 0.1330 / 0.0370\n", + "[56/1600][6/8] Loss_D: 0.7694 Loss_G: 1.9375 D(x): 0.5832 D(G(z)): 0.1019 / 0.0689\n", + "[57/1600][0/8] Loss_D: 0.8249 Loss_G: 2.5503 D(x): 0.5929 D(G(z)): 0.1077 / 0.0295\n", + "[57/1600][2/8] Loss_D: 0.9349 Loss_G: 1.8850 D(x): 0.4697 D(G(z)): 0.1433 / 0.0786\n", + "[57/1600][4/8] Loss_D: 0.9348 Loss_G: 1.9100 D(x): 0.4244 D(G(z)): 0.0885 / 0.0757\n", + "[57/1600][6/8] Loss_D: 0.8977 Loss_G: 2.6791 D(x): 0.6897 D(G(z)): 0.1699 / 0.0250\n", + "[58/1600][0/8] Loss_D: 0.7761 Loss_G: 2.0196 D(x): 0.6327 D(G(z)): 0.1178 / 0.0644\n", + "[58/1600][2/8] Loss_D: 0.7741 Loss_G: 2.6212 D(x): 0.5944 D(G(z)): 0.0759 / 0.0265\n", + "[58/1600][4/8] Loss_D: 0.7907 Loss_G: 2.5201 D(x): 0.7852 D(G(z)): 0.1896 / 0.0314\n", + "[58/1600][6/8] Loss_D: 0.7683 Loss_G: 2.7282 D(x): 0.6970 D(G(z)): 0.0847 / 0.0239\n", + "[59/1600][0/8] Loss_D: 0.7671 Loss_G: 2.0632 D(x): 0.5319 D(G(z)): 0.0377 / 0.0640\n", + "[59/1600][2/8] Loss_D: 0.8159 Loss_G: 2.1148 D(x): 0.4779 D(G(z)): 0.0298 / 0.0591\n", + "[59/1600][4/8] Loss_D: 0.7970 Loss_G: 3.0225 D(x): 0.5769 D(G(z)): 0.0525 / 0.0150\n", + 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"[65/1600][2/8] Loss_D: 0.7876 Loss_G: 2.9386 D(x): 0.6625 D(G(z)): 0.0742 / 0.0187\n", + "[65/1600][4/8] Loss_D: 0.8863 Loss_G: 2.5825 D(x): 0.6279 D(G(z)): 0.1576 / 0.0288\n", + "[65/1600][6/8] Loss_D: 0.7815 Loss_G: 2.2965 D(x): 0.5916 D(G(z)): 0.0895 / 0.0400\n", + "[66/1600][0/8] Loss_D: 0.7775 Loss_G: 1.6833 D(x): 0.5095 D(G(z)): 0.0582 / 0.0988\n", + "[66/1600][2/8] Loss_D: 1.0703 Loss_G: 1.2781 D(x): 0.3094 D(G(z)): 0.0192 / 0.1881\n", + "[66/1600][4/8] Loss_D: 1.6631 Loss_G: 1.6025 D(x): 0.1427 D(G(z)): 0.0061 / 0.1129\n", + "[66/1600][6/8] Loss_D: 0.7333 Loss_G: 2.4407 D(x): 0.7879 D(G(z)): 0.1691 / 0.0328\n", + "[67/1600][0/8] Loss_D: 0.7139 Loss_G: 1.8661 D(x): 0.6506 D(G(z)): 0.0802 / 0.0758\n", + "[67/1600][2/8] Loss_D: 0.7083 Loss_G: 2.0231 D(x): 0.5821 D(G(z)): 0.0569 / 0.0603\n", + "[67/1600][4/8] Loss_D: 0.7688 Loss_G: 2.4403 D(x): 0.6530 D(G(z)): 0.0949 / 0.0329\n", + "[67/1600][6/8] Loss_D: 0.7532 Loss_G: 2.1710 D(x): 0.5730 D(G(z)): 0.0560 / 0.0491\n", + 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"[70/1600][6/8] Loss_D: 0.8266 Loss_G: 2.3352 D(x): 0.5637 D(G(z)): 0.1019 / 0.0391\n", + "[71/1600][0/8] Loss_D: 0.9230 Loss_G: 1.6550 D(x): 0.4653 D(G(z)): 0.1205 / 0.1036\n", + "[71/1600][2/8] Loss_D: 0.8445 Loss_G: 2.0458 D(x): 0.4514 D(G(z)): 0.0499 / 0.0584\n", + "[71/1600][4/8] Loss_D: 0.8979 Loss_G: 1.9143 D(x): 0.3739 D(G(z)): 0.0339 / 0.0713\n", + "[71/1600][6/8] Loss_D: 0.7945 Loss_G: 3.0269 D(x): 0.6305 D(G(z)): 0.0936 / 0.0145\n", + "[72/1600][0/8] Loss_D: 0.7543 Loss_G: 2.3266 D(x): 0.7748 D(G(z)): 0.1780 / 0.0388\n", + "[72/1600][2/8] Loss_D: 0.7644 Loss_G: 1.7413 D(x): 0.4921 D(G(z)): 0.0282 / 0.0921\n", + "[72/1600][4/8] Loss_D: 0.7426 Loss_G: 2.8603 D(x): 0.5982 D(G(z)): 0.0471 / 0.0188\n", + "[72/1600][6/8] Loss_D: 0.6892 Loss_G: 2.2646 D(x): 0.7678 D(G(z)): 0.1150 / 0.0440\n", + "[73/1600][0/8] Loss_D: 0.7006 Loss_G: 2.3124 D(x): 0.6071 D(G(z)): 0.0464 / 0.0409\n", + "[73/1600][2/8] Loss_D: 0.7947 Loss_G: 2.9653 D(x): 0.6562 D(G(z)): 0.0786 / 0.0169\n", + "[73/1600][4/8] Loss_D: 1.0527 Loss_G: 2.9800 D(x): 0.8833 D(G(z)): 0.3245 / 0.0161\n", + "[73/1600][6/8] Loss_D: 0.7701 Loss_G: 2.5753 D(x): 0.4950 D(G(z)): 0.0231 / 0.0293\n", + "[74/1600][0/8] Loss_D: 0.9508 Loss_G: 2.8258 D(x): 0.7828 D(G(z)): 0.1157 / 0.0207\n", + "[74/1600][2/8] Loss_D: 0.7801 Loss_G: 1.8191 D(x): 0.5282 D(G(z)): 0.0439 / 0.0875\n", + "[74/1600][4/8] Loss_D: 0.7270 Loss_G: 2.2131 D(x): 0.6396 D(G(z)): 0.0844 / 0.0484\n", + "[74/1600][6/8] Loss_D: 0.6703 Loss_G: 2.0598 D(x): 0.7055 D(G(z)): 0.0933 / 0.0596\n", + "[75/1600][0/8] Loss_D: 0.8001 Loss_G: 2.8157 D(x): 0.6280 D(G(z)): 0.0647 / 0.0204\n", + "[75/1600][2/8] Loss_D: 0.9781 Loss_G: 2.7096 D(x): 0.7920 D(G(z)): 0.1956 / 0.0238\n", + "[75/1600][4/8] Loss_D: 0.8424 Loss_G: 1.8369 D(x): 0.4590 D(G(z)): 0.0412 / 0.0825\n", + "[75/1600][6/8] Loss_D: 0.8471 Loss_G: 1.7026 D(x): 0.5025 D(G(z)): 0.0696 / 0.1032\n", + "[76/1600][0/8] Loss_D: 0.7210 Loss_G: 2.5899 D(x): 0.6438 D(G(z)): 0.0732 / 0.0283\n", + "[76/1600][2/8] Loss_D: 0.8179 Loss_G: 2.2570 D(x): 0.4775 D(G(z)): 0.0254 / 0.0446\n", + "[76/1600][4/8] Loss_D: 0.8531 Loss_G: 3.1075 D(x): 0.7431 D(G(z)): 0.1118 / 0.0129\n", + "[76/1600][6/8] Loss_D: 0.7867 Loss_G: 2.5391 D(x): 0.6315 D(G(z)): 0.0525 / 0.0297\n", + "[77/1600][0/8] Loss_D: 0.8501 Loss_G: 3.1792 D(x): 0.7038 D(G(z)): 0.0980 / 0.0116\n", + "[77/1600][2/8] Loss_D: 0.6846 Loss_G: 2.2792 D(x): 0.7319 D(G(z)): 0.1167 / 0.0428\n", + "[77/1600][4/8] Loss_D: 0.7987 Loss_G: 2.8293 D(x): 0.4694 D(G(z)): 0.0217 / 0.0190\n", + "[77/1600][6/8] Loss_D: 0.7272 Loss_G: 2.7064 D(x): 0.6657 D(G(z)): 0.1406 / 0.0222\n", + "[78/1600][0/8] Loss_D: 0.7645 Loss_G: 3.5449 D(x): 0.7300 D(G(z)): 0.0484 / 0.0072\n", + "[78/1600][2/8] Loss_D: 0.6106 Loss_G: 2.5041 D(x): 0.7108 D(G(z)): 0.0287 / 0.0326\n", + "[78/1600][4/8] Loss_D: 0.6135 Loss_G: 3.0778 D(x): 0.7698 D(G(z)): 0.0371 / 0.0142\n", + "[78/1600][6/8] Loss_D: 0.6440 Loss_G: 2.6898 D(x): 0.6625 D(G(z)): 0.0222 / 0.0241\n", + "[79/1600][0/8] Loss_D: 0.7103 Loss_G: 2.2156 D(x): 0.5454 D(G(z)): 0.0208 / 0.0451\n", + "[79/1600][2/8] Loss_D: 0.8399 Loss_G: 3.5238 D(x): 0.7362 D(G(z)): 0.0846 / 0.0070\n", + "[79/1600][4/8] Loss_D: 0.8739 Loss_G: 2.7070 D(x): 0.7374 D(G(z)): 0.1318 / 0.0232\n", + "[79/1600][6/8] Loss_D: 0.8987 Loss_G: 2.3406 D(x): 0.3912 D(G(z)): 0.0175 / 0.0414\n", + "[80/1600][0/8] Loss_D: 0.8188 Loss_G: 2.9673 D(x): 0.6874 D(G(z)): 0.0800 / 0.0164\n", + "[80/1600][2/8] Loss_D: 0.7428 Loss_G: 1.9491 D(x): 0.6255 D(G(z)): 0.0724 / 0.0703\n", + "[80/1600][4/8] Loss_D: 0.6921 Loss_G: 2.4674 D(x): 0.6578 D(G(z)): 0.0732 / 0.0334\n", + "[80/1600][6/8] Loss_D: 0.7475 Loss_G: 2.8699 D(x): 0.6115 D(G(z)): 0.0421 / 0.0191\n", + "[81/1600][0/8] Loss_D: 0.7177 Loss_G: 2.2844 D(x): 0.6582 D(G(z)): 0.0777 / 0.0435\n", + "[81/1600][2/8] Loss_D: 0.7803 Loss_G: 2.3818 D(x): 0.5439 D(G(z)): 0.0616 / 0.0371\n", + "[81/1600][4/8] Loss_D: 0.8997 Loss_G: 1.8397 D(x): 0.3716 D(G(z)): 0.0450 / 0.0810\n", + "[81/1600][6/8] Loss_D: 0.7837 Loss_G: 2.2643 D(x): 0.5070 D(G(z)): 0.0483 / 0.0447\n", + "[82/1600][0/8] Loss_D: 0.8946 Loss_G: 3.3988 D(x): 0.8007 D(G(z)): 0.1102 / 0.0107\n", + "[82/1600][2/8] Loss_D: 0.8544 Loss_G: 2.5768 D(x): 0.6988 D(G(z)): 0.1104 / 0.0304\n", + "[82/1600][4/8] Loss_D: 0.7860 Loss_G: 1.9085 D(x): 0.4971 D(G(z)): 0.0487 / 0.0727\n", + "[82/1600][6/8] Loss_D: 0.7628 Loss_G: 3.1358 D(x): 0.7020 D(G(z)): 0.0743 / 0.0122\n", + "[83/1600][0/8] Loss_D: 0.7304 Loss_G: 2.2473 D(x): 0.6527 D(G(z)): 0.0711 / 0.0448\n", + "[83/1600][2/8] Loss_D: 0.8030 Loss_G: 2.1605 D(x): 0.4863 D(G(z)): 0.0496 / 0.0546\n", + "[83/1600][4/8] Loss_D: 0.8229 Loss_G: 2.8578 D(x): 0.4571 D(G(z)): 0.0202 / 0.0183\n", + "[83/1600][6/8] Loss_D: 0.6628 Loss_G: 2.9880 D(x): 0.8202 D(G(z)): 0.1057 / 0.0164\n", + "[84/1600][0/8] Loss_D: 0.7893 Loss_G: 3.1234 D(x): 0.4823 D(G(z)): 0.0056 / 0.0131\n", + "[84/1600][2/8] Loss_D: 0.7617 Loss_G: 3.2234 D(x): 0.7628 D(G(z)): 0.0340 / 0.0108\n", + "[84/1600][4/8] Loss_D: 0.7895 Loss_G: 2.8121 D(x): 0.7554 D(G(z)): 0.0622 / 0.0191\n", + "[84/1600][6/8] Loss_D: 0.7573 Loss_G: 2.3221 D(x): 0.5237 D(G(z)): 0.0331 / 0.0386\n", + "[85/1600][0/8] Loss_D: 0.7820 Loss_G: 2.6946 D(x): 0.7669 D(G(z)): 0.1603 / 0.0230\n", + "[85/1600][2/8] Loss_D: 0.7341 Loss_G: 1.5964 D(x): 0.5535 D(G(z)): 0.0392 / 0.1146\n", + "[85/1600][4/8] Loss_D: 0.7504 Loss_G: 3.0616 D(x): 0.6302 D(G(z)): 0.0442 / 0.0141\n", + "[85/1600][6/8] Loss_D: 0.6744 Loss_G: 2.2837 D(x): 0.7583 D(G(z)): 0.0952 / 0.0420\n", + "[86/1600][0/8] Loss_D: 0.9053 Loss_G: 1.4150 D(x): 0.4261 D(G(z)): 0.0320 / 0.1490\n", + "[86/1600][2/8] Loss_D: 0.8104 Loss_G: 2.4227 D(x): 0.4638 D(G(z)): 0.0243 / 0.0351\n", + "[86/1600][4/8] Loss_D: 0.8376 Loss_G: 3.1978 D(x): 0.7520 D(G(z)): 0.0984 / 0.0116\n", + "[86/1600][6/8] Loss_D: 0.8337 Loss_G: 2.6803 D(x): 0.8178 D(G(z)): 0.2144 / 0.0240\n", + "[87/1600][0/8] Loss_D: 0.8415 Loss_G: 1.9301 D(x): 0.4791 D(G(z)): 0.0704 / 0.0730\n", + "[87/1600][2/8] Loss_D: 0.9689 Loss_G: 2.4081 D(x): 0.3211 D(G(z)): 0.0197 / 0.0363\n", + "[87/1600][4/8] Loss_D: 0.8345 Loss_G: 3.3084 D(x): 0.8269 D(G(z)): 0.1560 / 0.0098\n", + "[87/1600][6/8] Loss_D: 0.6223 Loss_G: 2.3897 D(x): 0.6950 D(G(z)): 0.0359 / 0.0383\n", + "[88/1600][0/8] Loss_D: 0.7848 Loss_G: 3.0669 D(x): 0.5274 D(G(z)): 0.0213 / 0.0142\n", + "[88/1600][2/8] Loss_D: 0.8153 Loss_G: 2.5647 D(x): 0.7402 D(G(z)): 0.1137 / 0.0301\n", + "[88/1600][4/8] Loss_D: 0.7607 Loss_G: 2.9430 D(x): 0.6247 D(G(z)): 0.0372 / 0.0164\n", + "[88/1600][6/8] Loss_D: 0.7254 Loss_G: 2.1186 D(x): 0.5737 D(G(z)): 0.0303 / 0.0549\n", + "[89/1600][0/8] Loss_D: 0.7441 Loss_G: 3.0415 D(x): 0.7279 D(G(z)): 0.0922 / 0.0137\n", + "[89/1600][2/8] Loss_D: 0.7784 Loss_G: 2.7499 D(x): 0.6369 D(G(z)): 0.0840 / 0.0208\n", + "[89/1600][4/8] Loss_D: 0.8939 Loss_G: 3.6207 D(x): 0.7057 D(G(z)): 0.1471 / 0.0062\n", + "[89/1600][6/8] Loss_D: 0.9747 Loss_G: 3.2483 D(x): 0.8194 D(G(z)): 0.1973 / 0.0104\n", + "[90/1600][0/8] Loss_D: 0.7902 Loss_G: 1.7767 D(x): 0.4766 D(G(z)): 0.0235 / 0.0945\n", + "[90/1600][2/8] Loss_D: 0.8877 Loss_G: 2.3877 D(x): 0.3937 D(G(z)): 0.0217 / 0.0380\n", + "[90/1600][4/8] Loss_D: 0.7281 Loss_G: 2.6698 D(x): 0.7018 D(G(z)): 0.0800 / 0.0244\n", + "[90/1600][6/8] Loss_D: 0.7672 Loss_G: 2.3892 D(x): 0.5008 D(G(z)): 0.0301 / 0.0354\n", + "[91/1600][0/8] Loss_D: 0.7271 Loss_G: 1.8914 D(x): 0.5691 D(G(z)): 0.0698 / 0.0740\n", + "[91/1600][2/8] Loss_D: 0.6822 Loss_G: 2.4965 D(x): 0.6068 D(G(z)): 0.0538 / 0.0315\n", + "[91/1600][4/8] Loss_D: 0.6682 Loss_G: 2.3159 D(x): 0.6831 D(G(z)): 0.0899 / 0.0421\n", + "[91/1600][6/8] Loss_D: 0.7340 Loss_G: 1.9488 D(x): 0.5705 D(G(z)): 0.0604 / 0.0720\n", + "[92/1600][0/8] Loss_D: 1.0151 Loss_G: 1.6378 D(x): 0.3437 D(G(z)): 0.0378 / 0.1100\n", + "[92/1600][2/8] Loss_D: 0.7551 Loss_G: 2.6883 D(x): 0.5380 D(G(z)): 0.0480 / 0.0236\n", + "[92/1600][4/8] Loss_D: 0.6860 Loss_G: 1.9645 D(x): 0.5943 D(G(z)): 0.0505 / 0.0705\n", + "[92/1600][6/8] Loss_D: 0.6670 Loss_G: 2.7251 D(x): 0.7835 D(G(z)): 0.0863 / 0.0224\n", + "[93/1600][0/8] Loss_D: 0.6588 Loss_G: 2.3677 D(x): 0.6491 D(G(z)): 0.0457 / 0.0405\n", + "[93/1600][2/8] Loss_D: 0.6929 Loss_G: 2.3366 D(x): 0.6212 D(G(z)): 0.0533 / 0.0429\n", + "[93/1600][4/8] Loss_D: 0.7086 Loss_G: 2.3065 D(x): 0.6840 D(G(z)): 0.0830 / 0.0466\n", + "[93/1600][6/8] Loss_D: 0.7580 Loss_G: 2.5894 D(x): 0.6089 D(G(z)): 0.0586 / 0.0340\n", + "[94/1600][0/8] Loss_D: 0.7716 Loss_G: 2.4566 D(x): 0.6382 D(G(z)): 0.0888 / 0.0369\n", + "[94/1600][2/8] Loss_D: 0.7981 Loss_G: 2.0366 D(x): 0.5304 D(G(z)): 0.0697 / 0.0601\n", + "[94/1600][4/8] Loss_D: 0.7911 Loss_G: 1.5388 D(x): 0.4827 D(G(z)): 0.0462 / 0.1226\n", + "[94/1600][6/8] Loss_D: 1.1200 Loss_G: 2.1215 D(x): 0.2651 D(G(z)): 0.0076 / 0.0516\n", + "[95/1600][0/8] Loss_D: 0.8449 Loss_G: 1.8810 D(x): 0.4062 D(G(z)): 0.0307 / 0.0730\n", + "[95/1600][2/8] Loss_D: 0.7144 Loss_G: 2.5432 D(x): 0.6793 D(G(z)): 0.0844 / 0.0283\n", + "[95/1600][4/8] Loss_D: 0.6578 Loss_G: 1.9564 D(x): 0.6868 D(G(z)): 0.0816 / 0.0666\n", + "[95/1600][6/8] Loss_D: 0.9403 Loss_G: 1.7945 D(x): 0.3608 D(G(z)): 0.0123 / 0.0836\n", + "[96/1600][0/8] Loss_D: 0.7500 Loss_G: 3.1178 D(x): 0.6017 D(G(z)): 0.0453 / 0.0129\n", + "[96/1600][2/8] Loss_D: 0.8184 Loss_G: 2.5876 D(x): 0.7353 D(G(z)): 0.1437 / 0.0277\n", + "[96/1600][4/8] Loss_D: 0.7924 Loss_G: 1.6771 D(x): 0.5053 D(G(z)): 0.0426 / 0.1018\n", + "[96/1600][6/8] Loss_D: 1.0065 Loss_G: 1.9744 D(x): 0.3383 D(G(z)): 0.0205 / 0.0680\n", + "[97/1600][0/8] Loss_D: 0.7493 Loss_G: 3.4526 D(x): 0.5810 D(G(z)): 0.0414 / 0.0080\n", + "[97/1600][2/8] Loss_D: 0.6904 Loss_G: 2.2010 D(x): 0.6474 D(G(z)): 0.0513 / 0.0474\n", + "[97/1600][4/8] Loss_D: 0.6633 Loss_G: 2.4598 D(x): 0.6496 D(G(z)): 0.0527 / 0.0325\n", + "[97/1600][6/8] Loss_D: 0.8138 Loss_G: 3.3171 D(x): 0.7011 D(G(z)): 0.0625 / 0.0095\n", + "[98/1600][0/8] Loss_D: 0.7306 Loss_G: 2.2535 D(x): 0.7462 D(G(z)): 0.1264 / 0.0438\n", + "[98/1600][2/8] Loss_D: 0.7939 Loss_G: 2.5043 D(x): 0.4817 D(G(z)): 0.0246 / 0.0308\n", + "[98/1600][4/8] Loss_D: 0.9504 Loss_G: 3.3924 D(x): 0.7975 D(G(z)): 0.1864 / 0.0087\n", + "[98/1600][6/8] Loss_D: 0.7241 Loss_G: 2.2729 D(x): 0.6942 D(G(z)): 0.1234 / 0.0414\n", + "[99/1600][0/8] Loss_D: 0.8175 Loss_G: 1.9653 D(x): 0.4720 D(G(z)): 0.0436 / 0.0656\n", + "[99/1600][2/8] Loss_D: 0.8170 Loss_G: 2.0709 D(x): 0.4642 D(G(z)): 0.0330 / 0.0572\n", + "[99/1600][4/8] Loss_D: 0.8716 Loss_G: 2.5109 D(x): 0.3592 D(G(z)): 0.0323 / 0.0314\n", + "[99/1600][6/8] Loss_D: 0.8194 Loss_G: 3.0615 D(x): 0.6500 D(G(z)): 0.1209 / 0.0144\n", + "[100/1600][0/8] Loss_D: 0.6382 Loss_G: 2.3087 D(x): 0.7792 D(G(z)): 0.0988 / 0.0403\n", + "[100/1600][2/8] Loss_D: 0.7187 Loss_G: 3.0747 D(x): 0.6465 D(G(z)): 0.0323 / 0.0147\n", + "[100/1600][4/8] Loss_D: 0.8211 Loss_G: 2.6481 D(x): 0.6985 D(G(z)): 0.0783 / 0.0277\n", + "[100/1600][6/8] Loss_D: 0.7848 Loss_G: 2.6681 D(x): 0.6752 D(G(z)): 0.0734 / 0.0257\n", + "[101/1600][0/8] Loss_D: 0.7729 Loss_G: 2.5022 D(x): 0.5362 D(G(z)): 0.0329 / 0.0307\n", + "[101/1600][2/8] Loss_D: 0.7551 Loss_G: 2.8126 D(x): 0.6240 D(G(z)): 0.0485 / 0.0194\n", + "[101/1600][4/8] Loss_D: 0.7731 Loss_G: 2.2797 D(x): 0.5966 D(G(z)): 0.0751 / 0.0429\n", + "[101/1600][6/8] Loss_D: 0.8237 Loss_G: 2.8214 D(x): 0.7296 D(G(z)): 0.1480 / 0.0208\n", + "[102/1600][0/8] Loss_D: 0.7512 Loss_G: 1.4769 D(x): 0.5691 D(G(z)): 0.0562 / 0.1467\n", + "[102/1600][2/8] Loss_D: 0.8699 Loss_G: 2.5727 D(x): 0.4340 D(G(z)): 0.0224 / 0.0331\n", + "[102/1600][4/8] Loss_D: 0.9044 Loss_G: 2.7130 D(x): 0.7657 D(G(z)): 0.1301 / 0.0254\n", + "[102/1600][6/8] Loss_D: 0.8157 Loss_G: 1.9107 D(x): 0.4719 D(G(z)): 0.0487 / 0.0745\n", + "[103/1600][0/8] Loss_D: 0.7434 Loss_G: 2.5458 D(x): 0.6961 D(G(z)): 0.1125 / 0.0302\n", + "[103/1600][2/8] Loss_D: 0.9128 Loss_G: 3.0849 D(x): 0.7754 D(G(z)): 0.1821 / 0.0149\n", + "[103/1600][4/8] Loss_D: 0.9437 Loss_G: 3.0298 D(x): 0.8535 D(G(z)): 0.1991 / 0.0159\n", + "[103/1600][6/8] Loss_D: 0.7694 Loss_G: 2.8477 D(x): 0.6865 D(G(z)): 0.0571 / 0.0196\n", + "[104/1600][0/8] Loss_D: 0.7051 Loss_G: 1.8055 D(x): 0.5558 D(G(z)): 0.0347 / 0.0824\n", + "[104/1600][2/8] Loss_D: 0.7698 Loss_G: 2.6467 D(x): 0.4742 D(G(z)): 0.0234 / 0.0250\n", + "[104/1600][4/8] Loss_D: 0.6895 Loss_G: 2.2487 D(x): 0.6669 D(G(z)): 0.0581 / 0.0437\n", + "[104/1600][6/8] Loss_D: 0.7088 Loss_G: 2.3037 D(x): 0.5497 D(G(z)): 0.0294 / 0.0401\n", + "[105/1600][0/8] Loss_D: 0.7027 Loss_G: 2.6633 D(x): 0.7979 D(G(z)): 0.1549 / 0.0240\n", + "[105/1600][2/8] Loss_D: 0.7929 Loss_G: 2.3592 D(x): 0.5002 D(G(z)): 0.0359 / 0.0374\n", + "[105/1600][4/8] Loss_D: 0.7155 Loss_G: 1.9833 D(x): 0.6145 D(G(z)): 0.0800 / 0.0663\n", + "[105/1600][6/8] Loss_D: 0.7530 Loss_G: 3.3404 D(x): 0.6213 D(G(z)): 0.0398 / 0.0092\n", + "[106/1600][0/8] Loss_D: 0.7280 Loss_G: 2.3596 D(x): 0.6391 D(G(z)): 0.0523 / 0.0378\n", + "[106/1600][2/8] Loss_D: 0.7841 Loss_G: 2.9642 D(x): 0.6574 D(G(z)): 0.0793 / 0.0157\n", + "[106/1600][4/8] Loss_D: 0.9006 Loss_G: 3.2028 D(x): 0.8174 D(G(z)): 0.2052 / 0.0109\n", + "[106/1600][6/8] Loss_D: 0.7777 Loss_G: 2.5008 D(x): 0.4800 D(G(z)): 0.0145 / 0.0310\n", + "[107/1600][0/8] Loss_D: 0.7802 Loss_G: 3.2696 D(x): 0.6479 D(G(z)): 0.0625 / 0.0100\n", + "[107/1600][2/8] Loss_D: 0.8477 Loss_G: 3.2706 D(x): 0.8334 D(G(z)): 0.2622 / 0.0102\n", + "[107/1600][4/8] Loss_D: 0.7896 Loss_G: 2.1723 D(x): 0.4603 D(G(z)): 0.0189 / 0.0505\n", + "[107/1600][6/8] Loss_D: 0.6910 Loss_G: 3.0273 D(x): 0.6797 D(G(z)): 0.0612 / 0.0150\n", + "[108/1600][0/8] Loss_D: 0.6900 Loss_G: 2.0173 D(x): 0.6342 D(G(z)): 0.0655 / 0.0636\n", + "[108/1600][2/8] Loss_D: 0.9843 Loss_G: 1.7216 D(x): 0.2757 D(G(z)): 0.0115 / 0.0950\n", + "[108/1600][4/8] Loss_D: 0.8432 Loss_G: 3.3907 D(x): 0.6612 D(G(z)): 0.1306 / 0.0091\n", + "[108/1600][6/8] Loss_D: 0.9854 Loss_G: 3.1946 D(x): 0.8227 D(G(z)): 0.2947 / 0.0113\n", + "[109/1600][0/8] Loss_D: 0.7538 Loss_G: 2.1068 D(x): 0.7967 D(G(z)): 0.1569 / 0.0535\n", + "[109/1600][2/8] Loss_D: 0.8194 Loss_G: 1.7997 D(x): 0.4519 D(G(z)): 0.0330 / 0.0859\n", + "[109/1600][4/8] Loss_D: 0.7248 Loss_G: 2.5960 D(x): 0.6124 D(G(z)): 0.0538 / 0.0276\n", + "[109/1600][6/8] Loss_D: 0.6933 Loss_G: 1.8523 D(x): 0.6802 D(G(z)): 0.0906 / 0.0824\n", + "[110/1600][0/8] Loss_D: 0.7462 Loss_G: 2.4567 D(x): 0.5794 D(G(z)): 0.0527 / 0.0355\n", + "[110/1600][2/8] Loss_D: 0.8069 Loss_G: 1.5160 D(x): 0.4779 D(G(z)): 0.0346 / 0.1366\n", + "[110/1600][4/8] Loss_D: 0.7722 Loss_G: 3.0100 D(x): 0.5776 D(G(z)): 0.0456 / 0.0155\n", + "[110/1600][6/8] Loss_D: 0.7954 Loss_G: 1.9971 D(x): 0.8385 D(G(z)): 0.2087 / 0.0666\n", + "[111/1600][0/8] Loss_D: 0.8961 Loss_G: 2.4427 D(x): 0.4028 D(G(z)): 0.0155 / 0.0355\n", + "[111/1600][2/8] Loss_D: 0.7936 Loss_G: 2.4026 D(x): 0.6416 D(G(z)): 0.0655 / 0.0372\n", + "[111/1600][4/8] Loss_D: 0.8158 Loss_G: 2.2974 D(x): 0.7103 D(G(z)): 0.1216 / 0.0431\n", + "[111/1600][6/8] Loss_D: 0.7429 Loss_G: 1.6432 D(x): 0.5882 D(G(z)): 0.0738 / 0.1103\n", + "[112/1600][0/8] Loss_D: 0.7405 Loss_G: 2.3143 D(x): 0.6251 D(G(z)): 0.0797 / 0.0423\n", + "[112/1600][2/8] Loss_D: 0.8113 Loss_G: 2.3341 D(x): 0.5132 D(G(z)): 0.0436 / 0.0401\n", + "[112/1600][4/8] Loss_D: 0.8317 Loss_G: 2.5520 D(x): 0.7529 D(G(z)): 0.1831 / 0.0294\n", + "[112/1600][6/8] Loss_D: 0.8737 Loss_G: 2.6296 D(x): 0.6818 D(G(z)): 0.1328 / 0.0260\n", + "[113/1600][0/8] Loss_D: 0.8032 Loss_G: 1.6915 D(x): 0.5298 D(G(z)): 0.0649 / 0.1044\n", + "[113/1600][2/8] Loss_D: 0.9771 Loss_G: 1.8689 D(x): 0.3620 D(G(z)): 0.0320 / 0.0783\n", + "[113/1600][4/8] Loss_D: 0.7621 Loss_G: 2.0421 D(x): 0.5515 D(G(z)): 0.0720 / 0.0604\n", + "[113/1600][6/8] Loss_D: 0.7463 Loss_G: 2.6940 D(x): 0.5635 D(G(z)): 0.0417 / 0.0231\n", + "[114/1600][0/8] Loss_D: 0.8877 Loss_G: 2.7713 D(x): 0.7505 D(G(z)): 0.1319 / 0.0218\n", + "[114/1600][2/8] Loss_D: 0.8229 Loss_G: 2.5498 D(x): 0.6814 D(G(z)): 0.1207 / 0.0306\n", + "[114/1600][4/8] Loss_D: 0.8537 Loss_G: 2.0537 D(x): 0.4465 D(G(z)): 0.0436 / 0.0598\n", + "[114/1600][6/8] Loss_D: 0.8130 Loss_G: 3.0149 D(x): 0.6638 D(G(z)): 0.0995 / 0.0152\n", + "[115/1600][0/8] Loss_D: 0.7666 Loss_G: 2.4012 D(x): 0.7439 D(G(z)): 0.1264 / 0.0366\n", + "[115/1600][2/8] Loss_D: 0.9632 Loss_G: 1.5597 D(x): 0.3825 D(G(z)): 0.0283 / 0.1241\n", + "[115/1600][4/8] Loss_D: 0.7215 Loss_G: 2.9163 D(x): 0.6500 D(G(z)): 0.0609 / 0.0171\n", + "[115/1600][6/8] Loss_D: 0.8804 Loss_G: 3.2733 D(x): 0.7616 D(G(z)): 0.1042 / 0.0102\n", + "[116/1600][0/8] Loss_D: 1.0318 Loss_G: 3.6224 D(x): 0.7454 D(G(z)): 0.2800 / 0.0063\n", + "[116/1600][2/8] Loss_D: 0.7041 Loss_G: 1.9342 D(x): 0.6627 D(G(z)): 0.0901 / 0.0693\n", + "[116/1600][4/8] Loss_D: 1.4058 Loss_G: 1.6646 D(x): 0.2223 D(G(z)): 0.0133 / 0.1095\n", + "[116/1600][6/8] Loss_D: 0.9404 Loss_G: 2.1858 D(x): 0.4269 D(G(z)): 0.0637 / 0.0500\n", + "[117/1600][0/8] Loss_D: 0.8870 Loss_G: 2.8764 D(x): 0.7611 D(G(z)): 0.2368 / 0.0196\n", + "[117/1600][2/8] Loss_D: 1.0008 Loss_G: 1.7987 D(x): 0.4878 D(G(z)): 0.1935 / 0.0878\n", + "[117/1600][4/8] Loss_D: 1.0278 Loss_G: 1.2456 D(x): 0.3736 D(G(z)): 0.0629 / 0.1965\n", + "[117/1600][6/8] Loss_D: 0.9139 Loss_G: 2.5393 D(x): 0.3601 D(G(z)): 0.0429 / 0.0312\n", + "[118/1600][0/8] Loss_D: 1.0195 Loss_G: 1.9902 D(x): 0.7345 D(G(z)): 0.3073 / 0.0637\n", + "[118/1600][2/8] Loss_D: 0.8816 Loss_G: 1.6721 D(x): 0.4552 D(G(z)): 0.0789 / 0.1005\n", + "[118/1600][4/8] Loss_D: 0.8622 Loss_G: 1.9159 D(x): 0.5027 D(G(z)): 0.0979 / 0.0707\n", + "[118/1600][6/8] Loss_D: 0.8749 Loss_G: 2.6697 D(x): 0.6005 D(G(z)): 0.1490 / 0.0243\n", + "[119/1600][0/8] Loss_D: 0.8240 Loss_G: 1.2114 D(x): 0.5021 D(G(z)): 0.0792 / 0.2063\n", + "[119/1600][2/8] Loss_D: 1.5741 Loss_G: 1.8710 D(x): 0.1556 D(G(z)): 0.0063 / 0.0767\n", + "[119/1600][4/8] Loss_D: 0.8080 Loss_G: 2.3302 D(x): 0.7289 D(G(z)): 0.1619 / 0.0388\n", + "[119/1600][6/8] Loss_D: 0.8338 Loss_G: 1.6566 D(x): 0.5132 D(G(z)): 0.0930 / 0.1038\n", + "[120/1600][0/8] Loss_D: 0.7985 Loss_G: 2.1427 D(x): 0.6496 D(G(z)): 0.1456 / 0.0520\n", + "[120/1600][2/8] Loss_D: 0.8083 Loss_G: 2.5304 D(x): 0.6826 D(G(z)): 0.1076 / 0.0295\n", + "[120/1600][4/8] Loss_D: 0.8735 Loss_G: 1.1479 D(x): 0.4655 D(G(z)): 0.0507 / 0.2337\n", + "[120/1600][6/8] Loss_D: 0.8184 Loss_G: 2.3327 D(x): 0.4757 D(G(z)): 0.0339 / 0.0428\n", + "[121/1600][0/8] Loss_D: 0.7446 Loss_G: 2.1887 D(x): 0.6995 D(G(z)): 0.1153 / 0.0537\n", + "[121/1600][2/8] Loss_D: 0.7411 Loss_G: 1.9203 D(x): 0.6143 D(G(z)): 0.0881 / 0.0779\n", + "[121/1600][4/8] Loss_D: 0.7544 Loss_G: 2.0747 D(x): 0.6060 D(G(z)): 0.0871 / 0.0593\n", + "[121/1600][6/8] Loss_D: 0.8019 Loss_G: 1.9818 D(x): 0.5778 D(G(z)): 0.0958 / 0.0710\n", + "[122/1600][0/8] Loss_D: 0.8520 Loss_G: 2.5321 D(x): 0.6143 D(G(z)): 0.1307 / 0.0303\n", + "[122/1600][2/8] Loss_D: 1.1240 Loss_G: 3.2882 D(x): 0.7700 D(G(z)): 0.2716 / 0.0099\n", + "[122/1600][4/8] Loss_D: 0.8600 Loss_G: 1.8635 D(x): 0.7109 D(G(z)): 0.2244 / 0.0804\n", + "[122/1600][6/8] Loss_D: 0.8382 Loss_G: 2.1063 D(x): 0.4563 D(G(z)): 0.0568 / 0.0565\n", + "[123/1600][0/8] Loss_D: 0.7723 Loss_G: 1.9986 D(x): 0.5850 D(G(z)): 0.0965 / 0.0653\n", + "[123/1600][2/8] Loss_D: 0.8295 Loss_G: 2.5414 D(x): 0.7474 D(G(z)): 0.1864 / 0.0309\n", + "[123/1600][4/8] Loss_D: 0.8015 Loss_G: 2.4295 D(x): 0.6094 D(G(z)): 0.0930 / 0.0397\n", + "[123/1600][6/8] Loss_D: 0.9025 Loss_G: 2.4974 D(x): 0.7595 D(G(z)): 0.2084 / 0.0342\n", + "[124/1600][0/8] Loss_D: 0.8613 Loss_G: 1.8488 D(x): 0.5391 D(G(z)): 0.1189 / 0.0859\n", + "[124/1600][2/8] Loss_D: 0.9332 Loss_G: 1.6506 D(x): 0.4156 D(G(z)): 0.0959 / 0.1108\n", + "[124/1600][4/8] Loss_D: 0.8618 Loss_G: 2.0015 D(x): 0.4297 D(G(z)): 0.0607 / 0.0657\n", + "[124/1600][6/8] Loss_D: 0.9713 Loss_G: 3.3876 D(x): 0.7562 D(G(z)): 0.2032 / 0.0097\n", + "[125/1600][0/8] Loss_D: 0.8660 Loss_G: 2.2474 D(x): 0.7006 D(G(z)): 0.1149 / 0.0468\n", + "[125/1600][2/8] Loss_D: 0.7919 Loss_G: 2.3364 D(x): 0.6575 D(G(z)): 0.1131 / 0.0415\n", + "[125/1600][4/8] Loss_D: 0.9269 Loss_G: 2.5066 D(x): 0.6685 D(G(z)): 0.2370 / 0.0338\n", + "[125/1600][6/8] Loss_D: 0.8133 Loss_G: 2.3574 D(x): 0.7032 D(G(z)): 0.2078 / 0.0383\n", + "[126/1600][0/8] Loss_D: 1.0625 Loss_G: 1.6628 D(x): 0.2886 D(G(z)): 0.0419 / 0.1163\n", + "[126/1600][2/8] Loss_D: 0.8412 Loss_G: 3.1066 D(x): 0.6291 D(G(z)): 0.1312 / 0.0135\n", + "[126/1600][4/8] Loss_D: 0.8713 Loss_G: 2.7875 D(x): 0.7183 D(G(z)): 0.1096 / 0.0212\n", + "[126/1600][6/8] Loss_D: 0.9136 Loss_G: 1.4970 D(x): 0.4347 D(G(z)): 0.0659 / 0.1386\n", + "[127/1600][0/8] Loss_D: 0.8836 Loss_G: 2.6520 D(x): 0.4011 D(G(z)): 0.0400 / 0.0295\n", + "[127/1600][2/8] Loss_D: 0.7988 Loss_G: 1.6152 D(x): 0.5721 D(G(z)): 0.1257 / 0.1169\n", + "[127/1600][4/8] Loss_D: 0.7997 Loss_G: 2.5927 D(x): 0.5908 D(G(z)): 0.1078 / 0.0304\n", + "[127/1600][6/8] Loss_D: 0.9913 Loss_G: 2.9113 D(x): 0.7412 D(G(z)): 0.2280 / 0.0178\n", + "[128/1600][0/8] Loss_D: 0.7583 Loss_G: 2.1059 D(x): 0.6545 D(G(z)): 0.0953 / 0.0554\n", + "[128/1600][2/8] Loss_D: 0.7670 Loss_G: 2.7170 D(x): 0.6099 D(G(z)): 0.0620 / 0.0235\n", + 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"[131/1600][2/8] Loss_D: 0.9189 Loss_G: 1.9222 D(x): 0.5054 D(G(z)): 0.1561 / 0.0721\n", + "[131/1600][4/8] Loss_D: 0.8073 Loss_G: 1.9727 D(x): 0.4941 D(G(z)): 0.0541 / 0.0686\n", + "[131/1600][6/8] Loss_D: 0.8258 Loss_G: 3.1294 D(x): 0.6872 D(G(z)): 0.1019 / 0.0129\n", + "[132/1600][0/8] Loss_D: 0.7777 Loss_G: 1.8625 D(x): 0.5516 D(G(z)): 0.0649 / 0.0845\n", + "[132/1600][2/8] Loss_D: 0.7394 Loss_G: 2.3460 D(x): 0.5736 D(G(z)): 0.0680 / 0.0420\n", + "[132/1600][4/8] Loss_D: 0.8590 Loss_G: 1.9817 D(x): 0.4585 D(G(z)): 0.0468 / 0.0696\n", + "[132/1600][6/8] Loss_D: 0.9043 Loss_G: 2.5584 D(x): 0.4247 D(G(z)): 0.0657 / 0.0294\n", + "[133/1600][0/8] Loss_D: 0.8704 Loss_G: 2.5320 D(x): 0.6743 D(G(z)): 0.1369 / 0.0322\n", + "[133/1600][2/8] Loss_D: 0.8407 Loss_G: 2.4011 D(x): 0.6554 D(G(z)): 0.1491 / 0.0394\n", + "[133/1600][4/8] Loss_D: 1.0899 Loss_G: 0.9762 D(x): 0.3498 D(G(z)): 0.0810 / 0.3164\n", + "[133/1600][6/8] Loss_D: 1.2177 Loss_G: 1.5845 D(x): 0.2674 D(G(z)): 0.0181 / 0.1256\n", + "[134/1600][0/8] Loss_D: 0.8158 Loss_G: 1.8708 D(x): 0.4956 D(G(z)): 0.0773 / 0.0774\n", + "[134/1600][2/8] Loss_D: 0.8451 Loss_G: 1.7251 D(x): 0.4758 D(G(z)): 0.0846 / 0.0969\n", + "[134/1600][4/8] Loss_D: 0.9059 Loss_G: 2.3181 D(x): 0.5471 D(G(z)): 0.1574 / 0.0405\n", + "[134/1600][6/8] Loss_D: 0.8871 Loss_G: 1.3802 D(x): 0.5483 D(G(z)): 0.1782 / 0.1653\n", + "[135/1600][0/8] Loss_D: 1.0048 Loss_G: 1.7694 D(x): 0.3214 D(G(z)): 0.0266 / 0.0939\n", + "[135/1600][2/8] Loss_D: 0.8501 Loss_G: 2.4601 D(x): 0.6541 D(G(z)): 0.1703 / 0.0337\n", + "[135/1600][4/8] Loss_D: 0.8026 Loss_G: 1.5562 D(x): 0.5752 D(G(z)): 0.1146 / 0.1271\n", + "[135/1600][6/8] Loss_D: 1.0705 Loss_G: 1.8424 D(x): 0.2560 D(G(z)): 0.0191 / 0.0823\n", + "[136/1600][0/8] Loss_D: 0.8273 Loss_G: 2.2809 D(x): 0.7372 D(G(z)): 0.1862 / 0.0424\n", + "[136/1600][2/8] Loss_D: 0.8327 Loss_G: 1.6331 D(x): 0.4700 D(G(z)): 0.0475 / 0.1123\n", + "[136/1600][4/8] Loss_D: 0.7192 Loss_G: 2.3163 D(x): 0.6659 D(G(z)): 0.0819 / 0.0417\n", + "[136/1600][6/8] Loss_D: 0.8216 Loss_G: 2.5605 D(x): 0.6682 D(G(z)): 0.0947 / 0.0295\n", + "[137/1600][0/8] Loss_D: 0.8167 Loss_G: 2.0543 D(x): 0.6285 D(G(z)): 0.1140 / 0.0599\n", + "[137/1600][2/8] Loss_D: 0.8366 Loss_G: 2.1610 D(x): 0.5718 D(G(z)): 0.1177 / 0.0518\n", + "[137/1600][4/8] Loss_D: 0.9963 Loss_G: 1.0649 D(x): 0.3753 D(G(z)): 0.0688 / 0.2626\n", + "[137/1600][6/8] Loss_D: 0.9741 Loss_G: 1.8217 D(x): 0.3862 D(G(z)): 0.0511 / 0.0870\n", + "[138/1600][0/8] Loss_D: 0.7443 Loss_G: 2.2142 D(x): 0.6394 D(G(z)): 0.1027 / 0.0496\n", + "[138/1600][2/8] Loss_D: 0.7719 Loss_G: 2.1014 D(x): 0.5877 D(G(z)): 0.0906 / 0.0576\n", + "[138/1600][4/8] Loss_D: 0.7085 Loss_G: 1.9509 D(x): 0.6689 D(G(z)): 0.1216 / 0.0735\n", + "[138/1600][6/8] Loss_D: 0.8245 Loss_G: 1.7710 D(x): 0.4696 D(G(z)): 0.0446 / 0.0950\n", + "[139/1600][0/8] Loss_D: 0.7500 Loss_G: 2.7917 D(x): 0.5939 D(G(z)): 0.0520 / 0.0209\n", + "[139/1600][2/8] Loss_D: 0.9737 Loss_G: 2.7978 D(x): 0.7774 D(G(z)): 0.1561 / 0.0207\n", + "[139/1600][4/8] Loss_D: 0.7033 Loss_G: 1.9007 D(x): 0.6764 D(G(z)): 0.1039 / 0.0729\n", + "[139/1600][6/8] Loss_D: 0.8035 Loss_G: 2.0470 D(x): 0.5824 D(G(z)): 0.1261 / 0.0620\n", + "[140/1600][0/8] Loss_D: 0.9143 Loss_G: 1.8985 D(x): 0.3619 D(G(z)): 0.0543 / 0.0753\n", + "[140/1600][2/8] Loss_D: 0.8461 Loss_G: 2.0370 D(x): 0.5118 D(G(z)): 0.1097 / 0.0609\n", + "[140/1600][4/8] Loss_D: 0.7214 Loss_G: 2.2275 D(x): 0.7083 D(G(z)): 0.1292 / 0.0480\n", + "[140/1600][6/8] Loss_D: 0.7513 Loss_G: 1.8113 D(x): 0.5470 D(G(z)): 0.0466 / 0.0881\n", + "[141/1600][0/8] Loss_D: 1.0756 Loss_G: 1.4790 D(x): 0.3210 D(G(z)): 0.0319 / 0.1431\n", + "[141/1600][2/8] Loss_D: 0.7900 Loss_G: 1.9639 D(x): 0.5112 D(G(z)): 0.0713 / 0.0680\n", + "[141/1600][4/8] Loss_D: 0.8812 Loss_G: 1.6519 D(x): 0.4258 D(G(z)): 0.0620 / 0.1086\n", + "[141/1600][6/8] Loss_D: 0.9146 Loss_G: 1.5768 D(x): 0.4211 D(G(z)): 0.0712 / 0.1173\n", + "[142/1600][0/8] Loss_D: 0.7984 Loss_G: 2.5286 D(x): 0.6313 D(G(z)): 0.1329 / 0.0293\n", + "[142/1600][2/8] Loss_D: 0.7252 Loss_G: 1.8239 D(x): 0.6317 D(G(z)): 0.0890 / 0.0823\n", + "[142/1600][4/8] Loss_D: 0.7416 Loss_G: 2.3756 D(x): 0.6159 D(G(z)): 0.0744 / 0.0367\n", + "[142/1600][6/8] Loss_D: 0.8378 Loss_G: 2.7199 D(x): 0.6591 D(G(z)): 0.1291 / 0.0233\n", + "[143/1600][0/8] Loss_D: 0.8014 Loss_G: 1.8369 D(x): 0.5814 D(G(z)): 0.0820 / 0.0806\n", + "[143/1600][2/8] Loss_D: 0.7345 Loss_G: 1.9848 D(x): 0.5923 D(G(z)): 0.0705 / 0.0655\n", + "[143/1600][4/8] Loss_D: 0.7855 Loss_G: 2.5127 D(x): 0.5876 D(G(z)): 0.0732 / 0.0314\n", + "[143/1600][6/8] Loss_D: 0.8914 Loss_G: 2.9215 D(x): 0.6982 D(G(z)): 0.1490 / 0.0178\n", + "[144/1600][0/8] Loss_D: 1.3033 Loss_G: 2.8717 D(x): 0.8742 D(G(z)): 0.3821 / 0.0187\n", + "[144/1600][2/8] Loss_D: 0.7950 Loss_G: 2.0124 D(x): 0.5459 D(G(z)): 0.0586 / 0.0675\n", + "[144/1600][4/8] Loss_D: 0.8447 Loss_G: 2.0323 D(x): 0.4893 D(G(z)): 0.0736 / 0.0615\n", + "[144/1600][6/8] Loss_D: 0.7891 Loss_G: 1.7892 D(x): 0.5747 D(G(z)): 0.1038 / 0.0876\n", + "[145/1600][0/8] Loss_D: 0.7346 Loss_G: 1.9313 D(x): 0.5544 D(G(z)): 0.0467 / 0.0739\n", + "[145/1600][2/8] Loss_D: 0.7638 Loss_G: 2.9519 D(x): 0.5211 D(G(z)): 0.0267 / 0.0173\n", + "[145/1600][4/8] Loss_D: 0.9534 Loss_G: 2.6191 D(x): 0.7204 D(G(z)): 0.2430 / 0.0277\n", + "[145/1600][6/8] Loss_D: 0.9437 Loss_G: 2.9481 D(x): 0.7573 D(G(z)): 0.1963 / 0.0183\n", + "[146/1600][0/8] Loss_D: 0.7214 Loss_G: 1.7629 D(x): 0.6035 D(G(z)): 0.0682 / 0.0968\n", + "[146/1600][2/8] Loss_D: 0.7111 Loss_G: 2.3068 D(x): 0.6004 D(G(z)): 0.0544 / 0.0433\n", + "[146/1600][4/8] Loss_D: 0.7629 Loss_G: 2.3069 D(x): 0.6411 D(G(z)): 0.1101 / 0.0419\n", + "[146/1600][6/8] Loss_D: 0.8141 Loss_G: 1.7825 D(x): 0.4857 D(G(z)): 0.0568 / 0.0893\n", + "[147/1600][0/8] Loss_D: 0.9022 Loss_G: 1.8673 D(x): 0.3916 D(G(z)): 0.0269 / 0.0778\n", + "[147/1600][2/8] Loss_D: 0.7557 Loss_G: 1.9114 D(x): 0.5204 D(G(z)): 0.0428 / 0.0739\n", + "[147/1600][4/8] Loss_D: 0.9145 Loss_G: 1.4453 D(x): 0.4038 D(G(z)): 0.0303 / 0.1547\n", + "[147/1600][6/8] Loss_D: 0.7462 Loss_G: 2.4042 D(x): 0.5911 D(G(z)): 0.0672 / 0.0361\n", + "[148/1600][0/8] Loss_D: 0.7890 Loss_G: 2.2961 D(x): 0.6874 D(G(z)): 0.1406 / 0.0425\n", + "[148/1600][2/8] Loss_D: 0.7175 Loss_G: 1.7767 D(x): 0.6137 D(G(z)): 0.0790 / 0.0878\n", + "[148/1600][4/8] Loss_D: 0.7787 Loss_G: 2.8402 D(x): 0.5132 D(G(z)): 0.0314 / 0.0190\n", + "[148/1600][6/8] Loss_D: 0.7914 Loss_G: 2.3651 D(x): 0.7407 D(G(z)): 0.1317 / 0.0394\n", + "[149/1600][0/8] Loss_D: 0.8606 Loss_G: 1.9258 D(x): 0.4326 D(G(z)): 0.0281 / 0.0721\n", + "[149/1600][2/8] Loss_D: 0.7701 Loss_G: 2.3554 D(x): 0.7077 D(G(z)): 0.1366 / 0.0400\n", + "[149/1600][4/8] Loss_D: 0.7958 Loss_G: 2.4065 D(x): 0.6538 D(G(z)): 0.0877 / 0.0381\n", + "[149/1600][6/8] Loss_D: 0.7678 Loss_G: 1.6607 D(x): 0.6080 D(G(z)): 0.1022 / 0.1130\n", + "[150/1600][0/8] Loss_D: 0.8234 Loss_G: 2.4855 D(x): 0.5973 D(G(z)): 0.1043 / 0.0334\n", + "[150/1600][2/8] Loss_D: 0.8043 Loss_G: 2.1982 D(x): 0.7112 D(G(z)): 0.1603 / 0.0508\n", + "[150/1600][4/8] Loss_D: 0.7676 Loss_G: 2.0570 D(x): 0.5730 D(G(z)): 0.0658 / 0.0615\n", + "[150/1600][6/8] Loss_D: 0.9071 Loss_G: 3.2252 D(x): 0.7283 D(G(z)): 0.1553 / 0.0128\n", + "[151/1600][0/8] Loss_D: 1.3053 Loss_G: 3.1307 D(x): 0.8674 D(G(z)): 0.3781 / 0.0128\n", + "[151/1600][2/8] Loss_D: 1.0457 Loss_G: 2.4476 D(x): 0.3201 D(G(z)): 0.0241 / 0.0344\n", + "[151/1600][4/8] Loss_D: 0.8191 Loss_G: 1.9467 D(x): 0.4857 D(G(z)): 0.0529 / 0.0705\n", + "[151/1600][6/8] Loss_D: 0.7819 Loss_G: 1.6414 D(x): 0.6290 D(G(z)): 0.1057 / 0.1091\n", + "[152/1600][0/8] Loss_D: 0.7843 Loss_G: 1.7363 D(x): 0.6500 D(G(z)): 0.1031 / 0.0940\n", + "[152/1600][2/8] Loss_D: 0.8056 Loss_G: 1.8732 D(x): 0.5611 D(G(z)): 0.0878 / 0.0786\n", + "[152/1600][4/8] Loss_D: 0.7878 Loss_G: 1.8449 D(x): 0.6009 D(G(z)): 0.0850 / 0.0812\n", + "[152/1600][6/8] Loss_D: 0.7898 Loss_G: 1.8660 D(x): 0.5792 D(G(z)): 0.0837 / 0.0786\n", + "[153/1600][0/8] Loss_D: 0.7222 Loss_G: 1.8257 D(x): 0.5833 D(G(z)): 0.0797 / 0.0849\n", + "[153/1600][2/8] Loss_D: 0.7688 Loss_G: 1.7836 D(x): 0.6284 D(G(z)): 0.0889 / 0.0883\n", + "[153/1600][4/8] Loss_D: 0.7663 Loss_G: 1.7428 D(x): 0.6353 D(G(z)): 0.1017 / 0.0955\n", + "[153/1600][6/8] Loss_D: 0.7815 Loss_G: 1.7534 D(x): 0.5973 D(G(z)): 0.0948 / 0.0918\n", + "[154/1600][0/8] Loss_D: 0.8062 Loss_G: 1.8541 D(x): 0.6378 D(G(z)): 0.0921 / 0.0790\n", + "[154/1600][2/8] Loss_D: 0.7798 Loss_G: 1.9456 D(x): 0.5485 D(G(z)): 0.0724 / 0.0708\n", + "[154/1600][4/8] Loss_D: 0.7894 Loss_G: 1.8158 D(x): 0.5582 D(G(z)): 0.0848 / 0.0866\n", + "[154/1600][6/8] Loss_D: 0.7117 Loss_G: 1.8052 D(x): 0.6007 D(G(z)): 0.0812 / 0.0851\n", + "[155/1600][0/8] Loss_D: 0.7713 Loss_G: 1.7224 D(x): 0.5966 D(G(z)): 0.0986 / 0.0982\n", + "[155/1600][2/8] Loss_D: 0.7527 Loss_G: 1.8502 D(x): 0.6524 D(G(z)): 0.0901 / 0.0819\n", + "[155/1600][4/8] Loss_D: 0.7649 Loss_G: 1.8412 D(x): 0.5555 D(G(z)): 0.0805 / 0.0834\n", + "[155/1600][6/8] Loss_D: 0.8041 Loss_G: 1.8651 D(x): 0.6519 D(G(z)): 0.0885 / 0.0796\n", + "[156/1600][0/8] Loss_D: 0.7724 Loss_G: 1.9979 D(x): 0.6052 D(G(z)): 0.0743 / 0.0670\n", + "[156/1600][2/8] Loss_D: 0.7690 Loss_G: 1.8199 D(x): 0.5917 D(G(z)): 0.0832 / 0.0862\n", + "[156/1600][4/8] Loss_D: 0.7444 Loss_G: 1.8224 D(x): 0.6347 D(G(z)): 0.0870 / 0.0851\n", + "[156/1600][6/8] Loss_D: 0.7736 Loss_G: 1.8060 D(x): 0.5561 D(G(z)): 0.0838 / 0.0858\n", + "[157/1600][0/8] Loss_D: 0.7794 Loss_G: 1.7861 D(x): 0.5760 D(G(z)): 0.0894 / 0.0902\n", + "[157/1600][2/8] Loss_D: 0.7120 Loss_G: 1.7661 D(x): 0.6588 D(G(z)): 0.0917 / 0.0906\n", + "[157/1600][4/8] Loss_D: 0.8099 Loss_G: 1.7778 D(x): 0.6396 D(G(z)): 0.1050 / 0.0917\n", + "[157/1600][6/8] Loss_D: 0.7121 Loss_G: 1.7943 D(x): 0.6420 D(G(z)): 0.0893 / 0.0899\n", + "[158/1600][0/8] Loss_D: 0.7682 Loss_G: 1.8525 D(x): 0.6347 D(G(z)): 0.0903 / 0.0810\n", + "[158/1600][2/8] Loss_D: 0.8083 Loss_G: 1.8949 D(x): 0.5659 D(G(z)): 0.0835 / 0.0782\n", + "[158/1600][4/8] Loss_D: 0.7948 Loss_G: 1.7492 D(x): 0.5465 D(G(z)): 0.0876 / 0.0935\n", + "[158/1600][6/8] Loss_D: 0.7731 Loss_G: 1.6847 D(x): 0.5866 D(G(z)): 0.1015 / 0.1044\n", + "[159/1600][0/8] Loss_D: 0.8180 Loss_G: 1.8174 D(x): 0.5987 D(G(z)): 0.0985 / 0.0856\n", + "[159/1600][2/8] Loss_D: 0.8100 Loss_G: 1.8402 D(x): 0.5578 D(G(z)): 0.0855 / 0.0837\n", + "[159/1600][4/8] Loss_D: 0.7608 Loss_G: 1.7319 D(x): 0.5901 D(G(z)): 0.0918 / 0.0965\n", + "[159/1600][6/8] Loss_D: 0.7510 Loss_G: 1.6558 D(x): 0.6758 D(G(z)): 0.1129 / 0.1080\n", + "[160/1600][0/8] Loss_D: 0.8436 Loss_G: 1.7004 D(x): 0.6315 D(G(z)): 0.1163 / 0.1014\n", + "[160/1600][2/8] Loss_D: 0.7796 Loss_G: 1.8805 D(x): 0.5930 D(G(z)): 0.0891 / 0.0793\n", + "[160/1600][4/8] Loss_D: 0.7635 Loss_G: 1.8106 D(x): 0.5559 D(G(z)): 0.0810 / 0.0859\n", + "[160/1600][6/8] Loss_D: 0.7934 Loss_G: 1.7733 D(x): 0.5407 D(G(z)): 0.0865 / 0.0912\n", + "[161/1600][0/8] Loss_D: 0.7647 Loss_G: 1.7111 D(x): 0.5742 D(G(z)): 0.0967 / 0.1004\n", + "[161/1600][2/8] Loss_D: 0.8134 Loss_G: 1.7585 D(x): 0.6022 D(G(z)): 0.1002 / 0.0936\n", + "[161/1600][4/8] Loss_D: 0.7676 Loss_G: 1.7646 D(x): 0.5743 D(G(z)): 0.0885 / 0.0900\n", + "[161/1600][6/8] Loss_D: 0.7881 Loss_G: 1.8025 D(x): 0.5916 D(G(z)): 0.0932 / 0.0877\n", + "[162/1600][0/8] Loss_D: 0.8008 Loss_G: 1.6701 D(x): 0.5444 D(G(z)): 0.0985 / 0.1073\n", + "[162/1600][2/8] Loss_D: 0.8339 Loss_G: 1.6826 D(x): 0.5783 D(G(z)): 0.1177 / 0.1070\n", + "[162/1600][4/8] Loss_D: 0.7715 Loss_G: 1.7600 D(x): 0.6001 D(G(z)): 0.0991 / 0.0945\n", + "[162/1600][6/8] Loss_D: 0.8551 Loss_G: 1.7632 D(x): 0.5013 D(G(z)): 0.0932 / 0.0922\n", + "[163/1600][0/8] Loss_D: 0.7812 Loss_G: 1.6826 D(x): 0.6062 D(G(z)): 0.1020 / 0.1031\n", + "[163/1600][2/8] Loss_D: 0.7986 Loss_G: 1.7851 D(x): 0.5943 D(G(z)): 0.0962 / 0.0869\n", + "[163/1600][4/8] Loss_D: 0.8117 Loss_G: 1.7366 D(x): 0.5365 D(G(z)): 0.0931 / 0.0964\n", + "[163/1600][6/8] Loss_D: 0.8393 Loss_G: 1.6999 D(x): 0.5875 D(G(z)): 0.1074 / 0.0992\n", + "[164/1600][0/8] Loss_D: 0.7978 Loss_G: 1.7255 D(x): 0.5494 D(G(z)): 0.0940 / 0.0954\n", + "[164/1600][2/8] Loss_D: 0.8143 Loss_G: 1.6555 D(x): 0.6085 D(G(z)): 0.1144 / 0.1064\n", + "[164/1600][4/8] Loss_D: 0.7970 Loss_G: 1.7910 D(x): 0.5629 D(G(z)): 0.0930 / 0.0879\n", + "[164/1600][6/8] Loss_D: 0.7681 Loss_G: 1.6349 D(x): 0.5840 D(G(z)): 0.1030 / 0.1117\n", + "[165/1600][0/8] Loss_D: 0.8002 Loss_G: 1.6703 D(x): 0.6148 D(G(z)): 0.1182 / 0.1055\n", + "[165/1600][2/8] Loss_D: 0.7573 Loss_G: 1.8810 D(x): 0.5905 D(G(z)): 0.0847 / 0.0769\n", + "[165/1600][4/8] Loss_D: 0.8317 Loss_G: 1.8322 D(x): 0.5309 D(G(z)): 0.0838 / 0.0819\n", + "[165/1600][6/8] Loss_D: 0.7693 Loss_G: 1.6891 D(x): 0.5660 D(G(z)): 0.0888 / 0.1026\n", + "[166/1600][0/8] Loss_D: 0.8072 Loss_G: 1.6889 D(x): 0.6272 D(G(z)): 0.1103 / 0.1028\n", + "[166/1600][2/8] Loss_D: 0.8391 Loss_G: 1.7357 D(x): 0.5946 D(G(z)): 0.1128 / 0.0959\n", + "[166/1600][4/8] Loss_D: 0.7707 Loss_G: 1.7875 D(x): 0.6447 D(G(z)): 0.0952 / 0.0882\n", + "[166/1600][6/8] Loss_D: 0.7774 Loss_G: 1.9664 D(x): 0.6139 D(G(z)): 0.0764 / 0.0693\n", + "[167/1600][0/8] Loss_D: 0.7310 Loss_G: 1.7547 D(x): 0.6171 D(G(z)): 0.0875 / 0.0933\n", + "[167/1600][2/8] Loss_D: 0.7874 Loss_G: 1.8050 D(x): 0.5652 D(G(z)): 0.0860 / 0.0859\n", + "[167/1600][4/8] Loss_D: 0.7296 Loss_G: 1.9430 D(x): 0.6355 D(G(z)): 0.0758 / 0.0712\n", + "[167/1600][6/8] Loss_D: 0.7281 Loss_G: 1.9034 D(x): 0.6026 D(G(z)): 0.0735 / 0.0743\n", + "[168/1600][0/8] Loss_D: 0.7778 Loss_G: 1.6716 D(x): 0.5703 D(G(z)): 0.0925 / 0.1056\n", + "[168/1600][2/8] Loss_D: 0.7984 Loss_G: 1.7047 D(x): 0.6616 D(G(z)): 0.1128 / 0.1000\n", + "[168/1600][4/8] Loss_D: 0.7999 Loss_G: 1.9026 D(x): 0.5682 D(G(z)): 0.0853 / 0.0741\n", + "[168/1600][6/8] Loss_D: 0.7757 Loss_G: 1.9637 D(x): 0.5781 D(G(z)): 0.0724 / 0.0685\n", + "[169/1600][0/8] Loss_D: 0.8037 Loss_G: 1.9497 D(x): 0.5145 D(G(z)): 0.0634 / 0.0699\n", + "[169/1600][2/8] Loss_D: 0.8185 Loss_G: 1.9654 D(x): 0.6427 D(G(z)): 0.0790 / 0.0686\n", + "[169/1600][4/8] Loss_D: 0.7765 Loss_G: 1.7289 D(x): 0.5642 D(G(z)): 0.0852 / 0.0959\n", + "[169/1600][6/8] Loss_D: 0.7763 Loss_G: 1.6800 D(x): 0.6317 D(G(z)): 0.1066 / 0.1040\n", + "[170/1600][0/8] Loss_D: 0.7289 Loss_G: 1.8206 D(x): 0.6264 D(G(z)): 0.0879 / 0.0826\n", + "[170/1600][2/8] Loss_D: 0.8039 Loss_G: 2.0302 D(x): 0.5823 D(G(z)): 0.0731 / 0.0633\n", + "[170/1600][4/8] Loss_D: 0.7870 Loss_G: 1.8937 D(x): 0.5469 D(G(z)): 0.0706 / 0.0754\n", + "[170/1600][6/8] Loss_D: 0.7260 Loss_G: 1.9501 D(x): 0.6074 D(G(z)): 0.0690 / 0.0696\n", + "[171/1600][0/8] Loss_D: 0.7263 Loss_G: 1.7481 D(x): 0.5933 D(G(z)): 0.0811 / 0.0930\n", + "[171/1600][2/8] Loss_D: 0.7713 Loss_G: 1.7606 D(x): 0.6515 D(G(z)): 0.1017 / 0.0922\n", + "[171/1600][4/8] Loss_D: 0.7895 Loss_G: 1.9617 D(x): 0.5856 D(G(z)): 0.0789 / 0.0684\n", + "[171/1600][6/8] Loss_D: 0.7973 Loss_G: 2.0021 D(x): 0.5477 D(G(z)): 0.0647 / 0.0648\n", + "[172/1600][0/8] Loss_D: 0.7189 Loss_G: 1.6960 D(x): 0.6020 D(G(z)): 0.0856 / 0.1002\n", + "[172/1600][2/8] Loss_D: 0.7932 Loss_G: 1.7231 D(x): 0.6985 D(G(z)): 0.1081 / 0.0960\n", + "[172/1600][4/8] Loss_D: 0.7452 Loss_G: 1.8700 D(x): 0.6227 D(G(z)): 0.0873 / 0.0791\n", + "[172/1600][6/8] Loss_D: 0.6995 Loss_G: 1.9218 D(x): 0.6221 D(G(z)): 0.0704 / 0.0727\n", + "[173/1600][0/8] Loss_D: 0.7715 Loss_G: 1.9721 D(x): 0.6291 D(G(z)): 0.0745 / 0.0679\n", + "[173/1600][2/8] Loss_D: 0.7601 Loss_G: 1.8574 D(x): 0.6315 D(G(z)): 0.0813 / 0.0821\n", + "[173/1600][4/8] Loss_D: 0.8288 Loss_G: 1.7751 D(x): 0.6659 D(G(z)): 0.1017 / 0.0912\n", + "[173/1600][6/8] Loss_D: 0.7869 Loss_G: 2.0015 D(x): 0.6560 D(G(z)): 0.0789 / 0.0651\n", + "[174/1600][0/8] Loss_D: 0.7721 Loss_G: 1.9601 D(x): 0.5662 D(G(z)): 0.0646 / 0.0687\n", + "[174/1600][2/8] Loss_D: 0.7222 Loss_G: 1.8988 D(x): 0.5957 D(G(z)): 0.0707 / 0.0760\n", + "[174/1600][4/8] Loss_D: 0.8022 Loss_G: 1.9079 D(x): 0.6028 D(G(z)): 0.0811 / 0.0744\n", + "[174/1600][6/8] Loss_D: 0.7963 Loss_G: 2.0573 D(x): 0.6060 D(G(z)): 0.0678 / 0.0594\n", + "[175/1600][0/8] Loss_D: 0.7420 Loss_G: 1.7916 D(x): 0.5798 D(G(z)): 0.0750 / 0.0878\n", + "[175/1600][2/8] Loss_D: 0.7082 Loss_G: 1.6536 D(x): 0.6457 D(G(z)): 0.0992 / 0.1082\n", + "[175/1600][4/8] Loss_D: 0.7583 Loss_G: 1.7764 D(x): 0.6732 D(G(z)): 0.1033 / 0.0909\n", + "[175/1600][6/8] Loss_D: 0.8186 Loss_G: 2.0744 D(x): 0.6858 D(G(z)): 0.0777 / 0.0582\n", + "[176/1600][0/8] Loss_D: 0.7160 Loss_G: 2.0139 D(x): 0.5992 D(G(z)): 0.0571 / 0.0634\n", + "[176/1600][2/8] Loss_D: 0.7444 Loss_G: 1.7902 D(x): 0.6319 D(G(z)): 0.0818 / 0.0872\n", + "[176/1600][4/8] Loss_D: 0.7541 Loss_G: 1.8327 D(x): 0.6977 D(G(z)): 0.0938 / 0.0831\n", + "[176/1600][6/8] Loss_D: 0.7203 Loss_G: 2.0025 D(x): 0.6240 D(G(z)): 0.0676 / 0.0641\n", + "[177/1600][0/8] Loss_D: 0.7524 Loss_G: 1.8994 D(x): 0.6303 D(G(z)): 0.0772 / 0.0746\n", + "[177/1600][2/8] Loss_D: 0.7290 Loss_G: 2.0080 D(x): 0.5754 D(G(z)): 0.0592 / 0.0646\n", + "[177/1600][4/8] Loss_D: 0.7655 Loss_G: 1.8976 D(x): 0.6541 D(G(z)): 0.0804 / 0.0756\n", + "[177/1600][6/8] Loss_D: 0.7482 Loss_G: 1.9279 D(x): 0.6568 D(G(z)): 0.0754 / 0.0714\n", + "[178/1600][0/8] Loss_D: 0.7940 Loss_G: 2.0622 D(x): 0.6944 D(G(z)): 0.0745 / 0.0606\n", + "[178/1600][2/8] Loss_D: 0.7326 Loss_G: 1.9442 D(x): 0.5558 D(G(z)): 0.0583 / 0.0696\n", + "[178/1600][4/8] Loss_D: 0.7616 Loss_G: 1.7738 D(x): 0.6414 D(G(z)): 0.0887 / 0.0905\n", + "[178/1600][6/8] Loss_D: 0.7618 Loss_G: 1.7502 D(x): 0.6494 D(G(z)): 0.0996 / 0.0941\n", + "[179/1600][0/8] Loss_D: 0.7465 Loss_G: 1.7922 D(x): 0.6691 D(G(z)): 0.0948 / 0.0876\n", + "[179/1600][2/8] Loss_D: 0.8041 Loss_G: 1.9289 D(x): 0.6363 D(G(z)): 0.0845 / 0.0715\n", + "[179/1600][4/8] Loss_D: 0.7713 Loss_G: 2.0534 D(x): 0.5333 D(G(z)): 0.0626 / 0.0608\n", + "[179/1600][6/8] Loss_D: 0.7450 Loss_G: 1.9679 D(x): 0.5550 D(G(z)): 0.0592 / 0.0691\n", + "[180/1600][0/8] Loss_D: 0.7436 Loss_G: 1.8475 D(x): 0.6345 D(G(z)): 0.0777 / 0.0807\n", + "[180/1600][2/8] Loss_D: 0.7840 Loss_G: 1.9241 D(x): 0.6461 D(G(z)): 0.0848 / 0.0735\n", + "[180/1600][4/8] Loss_D: 0.7298 Loss_G: 1.7904 D(x): 0.6023 D(G(z)): 0.0779 / 0.0876\n", + "[180/1600][6/8] Loss_D: 0.7576 Loss_G: 1.8059 D(x): 0.6722 D(G(z)): 0.0932 / 0.0866\n", + "[181/1600][0/8] Loss_D: 0.7689 Loss_G: 1.9314 D(x): 0.6471 D(G(z)): 0.0847 / 0.0733\n", + "[181/1600][2/8] Loss_D: 0.7884 Loss_G: 2.0775 D(x): 0.6325 D(G(z)): 0.0709 / 0.0586\n", + "[181/1600][4/8] Loss_D: 0.7695 Loss_G: 1.9436 D(x): 0.5315 D(G(z)): 0.0562 / 0.0702\n", + "[181/1600][6/8] Loss_D: 0.7196 Loss_G: 1.8208 D(x): 0.6567 D(G(z)): 0.0822 / 0.0843\n", + "[182/1600][0/8] Loss_D: 0.7618 Loss_G: 1.9429 D(x): 0.6029 D(G(z)): 0.0721 / 0.0701\n", + "[182/1600][2/8] Loss_D: 0.7889 Loss_G: 1.9832 D(x): 0.6163 D(G(z)): 0.0760 / 0.0667\n", + "[182/1600][4/8] Loss_D: 0.7781 Loss_G: 2.0638 D(x): 0.5340 D(G(z)): 0.0570 / 0.0610\n", + "[182/1600][6/8] Loss_D: 0.7821 Loss_G: 1.7689 D(x): 0.6759 D(G(z)): 0.0910 / 0.0923\n", + "[183/1600][0/8] Loss_D: 0.7385 Loss_G: 1.8403 D(x): 0.6033 D(G(z)): 0.0809 / 0.0807\n", + "[183/1600][2/8] Loss_D: 0.7274 Loss_G: 1.8656 D(x): 0.6004 D(G(z)): 0.0794 / 0.0818\n", + "[183/1600][4/8] Loss_D: 0.7701 Loss_G: 1.8931 D(x): 0.6406 D(G(z)): 0.0848 / 0.0759\n", + "[183/1600][6/8] Loss_D: 0.7550 Loss_G: 1.9673 D(x): 0.6567 D(G(z)): 0.0769 / 0.0699\n", + "[184/1600][0/8] Loss_D: 0.6995 Loss_G: 1.9400 D(x): 0.6382 D(G(z)): 0.0700 / 0.0727\n", + "[184/1600][2/8] Loss_D: 0.8028 Loss_G: 1.8973 D(x): 0.6194 D(G(z)): 0.0874 / 0.0750\n", + "[184/1600][4/8] Loss_D: 0.7274 Loss_G: 1.9108 D(x): 0.5910 D(G(z)): 0.0661 / 0.0733\n", + "[184/1600][6/8] Loss_D: 0.7894 Loss_G: 1.7729 D(x): 0.6530 D(G(z)): 0.0972 / 0.0919\n", + "[185/1600][0/8] Loss_D: 0.7727 Loss_G: 1.7562 D(x): 0.5737 D(G(z)): 0.0912 / 0.0937\n", + "[185/1600][2/8] Loss_D: 0.7836 Loss_G: 1.8285 D(x): 0.6509 D(G(z)): 0.0959 / 0.0838\n", + "[185/1600][4/8] Loss_D: 0.7181 Loss_G: 1.8257 D(x): 0.6649 D(G(z)): 0.0863 / 0.0845\n", + "[185/1600][6/8] Loss_D: 0.7346 Loss_G: 1.7387 D(x): 0.6347 D(G(z)): 0.0927 / 0.0959\n", + "[186/1600][0/8] Loss_D: 0.8000 Loss_G: 1.8388 D(x): 0.5994 D(G(z)): 0.0943 / 0.0819\n", + "[186/1600][2/8] Loss_D: 0.7387 Loss_G: 1.8807 D(x): 0.5722 D(G(z)): 0.0705 / 0.0755\n", + "[186/1600][4/8] Loss_D: 0.7155 Loss_G: 1.6790 D(x): 0.6222 D(G(z)): 0.0891 / 0.1021\n", + "[186/1600][6/8] Loss_D: 0.7006 Loss_G: 1.8001 D(x): 0.6631 D(G(z)): 0.0936 / 0.0868\n", + "[187/1600][0/8] Loss_D: 0.7129 Loss_G: 1.8925 D(x): 0.6286 D(G(z)): 0.0786 / 0.0764\n", + "[187/1600][2/8] Loss_D: 0.7730 Loss_G: 1.8032 D(x): 0.5909 D(G(z)): 0.0846 / 0.0864\n", + "[187/1600][4/8] Loss_D: 0.7545 Loss_G: 1.7789 D(x): 0.6436 D(G(z)): 0.0963 / 0.0897\n", + "[187/1600][6/8] Loss_D: 0.7397 Loss_G: 1.9377 D(x): 0.5735 D(G(z)): 0.0675 / 0.0697\n", + "[188/1600][0/8] Loss_D: 0.7722 Loss_G: 1.8406 D(x): 0.5710 D(G(z)): 0.0785 / 0.0814\n", + "[188/1600][2/8] Loss_D: 0.7941 Loss_G: 2.0056 D(x): 0.5874 D(G(z)): 0.0717 / 0.0655\n", + "[188/1600][4/8] Loss_D: 0.7591 Loss_G: 1.9047 D(x): 0.5815 D(G(z)): 0.0730 / 0.0770\n", + "[188/1600][6/8] Loss_D: 0.7678 Loss_G: 1.7608 D(x): 0.5953 D(G(z)): 0.0867 / 0.0916\n", + "[189/1600][0/8] Loss_D: 0.7136 Loss_G: 1.7836 D(x): 0.6788 D(G(z)): 0.0912 / 0.0892\n", + "[189/1600][2/8] Loss_D: 0.7800 Loss_G: 1.9150 D(x): 0.6029 D(G(z)): 0.0811 / 0.0731\n", + "[189/1600][4/8] Loss_D: 0.7276 Loss_G: 1.8092 D(x): 0.6290 D(G(z)): 0.0881 / 0.0867\n", + "[189/1600][6/8] Loss_D: 0.8038 Loss_G: 1.9575 D(x): 0.5592 D(G(z)): 0.0726 / 0.0688\n", + "[190/1600][0/8] Loss_D: 0.7832 Loss_G: 2.0120 D(x): 0.5919 D(G(z)): 0.0702 / 0.0650\n", + "[190/1600][2/8] Loss_D: 0.7306 Loss_G: 1.7296 D(x): 0.6636 D(G(z)): 0.0960 / 0.0970\n", + "[190/1600][4/8] Loss_D: 0.7721 Loss_G: 1.8261 D(x): 0.5552 D(G(z)): 0.0778 / 0.0830\n", + "[190/1600][6/8] Loss_D: 0.7660 Loss_G: 1.8792 D(x): 0.6517 D(G(z)): 0.0863 / 0.0759\n", + "[191/1600][0/8] Loss_D: 0.8079 Loss_G: 2.0171 D(x): 0.5099 D(G(z)): 0.0673 / 0.0632\n", + "[191/1600][2/8] Loss_D: 0.7444 Loss_G: 1.9475 D(x): 0.5666 D(G(z)): 0.0604 / 0.0692\n", + "[191/1600][4/8] Loss_D: 0.7648 Loss_G: 1.8742 D(x): 0.6521 D(G(z)): 0.0804 / 0.0772\n", + "[191/1600][6/8] Loss_D: 0.7747 Loss_G: 1.9684 D(x): 0.5765 D(G(z)): 0.0738 / 0.0680\n", + "[192/1600][0/8] Loss_D: 0.7402 Loss_G: 1.8675 D(x): 0.6340 D(G(z)): 0.0770 / 0.0778\n", + "[192/1600][2/8] Loss_D: 0.7732 Loss_G: 1.9897 D(x): 0.6021 D(G(z)): 0.0737 / 0.0662\n", + "[192/1600][4/8] Loss_D: 0.7183 Loss_G: 1.9227 D(x): 0.6318 D(G(z)): 0.0686 / 0.0718\n", + "[192/1600][6/8] Loss_D: 0.7838 Loss_G: 1.9295 D(x): 0.5625 D(G(z)): 0.0754 / 0.0718\n", + "[193/1600][0/8] Loss_D: 0.7889 Loss_G: 1.8786 D(x): 0.5941 D(G(z)): 0.0832 / 0.0789\n", + "[193/1600][2/8] Loss_D: 0.6766 Loss_G: 1.7927 D(x): 0.6573 D(G(z)): 0.0827 / 0.0870\n", + "[193/1600][4/8] Loss_D: 0.7352 Loss_G: 1.8629 D(x): 0.6137 D(G(z)): 0.0820 / 0.0798\n", + "[193/1600][6/8] Loss_D: 0.7292 Loss_G: 1.8983 D(x): 0.6063 D(G(z)): 0.0772 / 0.0752\n", + "[194/1600][0/8] Loss_D: 0.7434 Loss_G: 1.9400 D(x): 0.5781 D(G(z)): 0.0667 / 0.0704\n", + "[194/1600][2/8] Loss_D: 0.7485 Loss_G: 1.9594 D(x): 0.6581 D(G(z)): 0.0777 / 0.0687\n", + "[194/1600][4/8] Loss_D: 0.7053 Loss_G: 1.9289 D(x): 0.5979 D(G(z)): 0.0632 / 0.0712\n", + "[194/1600][6/8] Loss_D: 0.7830 Loss_G: 1.9936 D(x): 0.5817 D(G(z)): 0.0757 / 0.0654\n", + "[195/1600][0/8] Loss_D: 0.7682 Loss_G: 1.8762 D(x): 0.6182 D(G(z)): 0.0769 / 0.0763\n", + "[195/1600][2/8] Loss_D: 0.7436 Loss_G: 1.8366 D(x): 0.6133 D(G(z)): 0.0797 / 0.0816\n", + "[195/1600][4/8] Loss_D: 0.7212 Loss_G: 1.7699 D(x): 0.6607 D(G(z)): 0.0931 / 0.0901\n", + "[195/1600][6/8] Loss_D: 0.7433 Loss_G: 1.8166 D(x): 0.6181 D(G(z)): 0.0906 / 0.0852\n", + "[196/1600][0/8] Loss_D: 0.7514 Loss_G: 2.0771 D(x): 0.5971 D(G(z)): 0.0625 / 0.0570\n", + "[196/1600][2/8] Loss_D: 0.7348 Loss_G: 1.9667 D(x): 0.5750 D(G(z)): 0.0645 / 0.0682\n", + "[196/1600][4/8] Loss_D: 0.7331 Loss_G: 1.7617 D(x): 0.5851 D(G(z)): 0.0764 / 0.0926\n", + "[196/1600][6/8] Loss_D: 0.6787 Loss_G: 1.7325 D(x): 0.6947 D(G(z)): 0.0980 / 0.0971\n", + "[197/1600][0/8] Loss_D: 0.7104 Loss_G: 1.9345 D(x): 0.6557 D(G(z)): 0.0813 / 0.0707\n", + "[197/1600][2/8] Loss_D: 0.7427 Loss_G: 2.0204 D(x): 0.6027 D(G(z)): 0.0672 / 0.0631\n", + "[197/1600][4/8] Loss_D: 0.7674 Loss_G: 1.9886 D(x): 0.6742 D(G(z)): 0.0766 / 0.0650\n", + "[197/1600][6/8] Loss_D: 0.6818 Loss_G: 1.9581 D(x): 0.6778 D(G(z)): 0.0659 / 0.0678\n", + "[198/1600][0/8] Loss_D: 0.6968 Loss_G: 1.9325 D(x): 0.6807 D(G(z)): 0.0732 / 0.0709\n", + "[198/1600][2/8] Loss_D: 0.7335 Loss_G: 1.8806 D(x): 0.5772 D(G(z)): 0.0694 / 0.0777\n", + "[198/1600][4/8] Loss_D: 0.7527 Loss_G: 1.9174 D(x): 0.6785 D(G(z)): 0.0798 / 0.0722\n", + "[198/1600][6/8] Loss_D: 0.7840 Loss_G: 2.1463 D(x): 0.6346 D(G(z)): 0.0679 / 0.0536\n", + "[199/1600][0/8] Loss_D: 0.6860 Loss_G: 2.0136 D(x): 0.6336 D(G(z)): 0.0571 / 0.0641\n", + "[199/1600][2/8] Loss_D: 0.7861 Loss_G: 2.0181 D(x): 0.6680 D(G(z)): 0.0736 / 0.0633\n", + "[199/1600][4/8] Loss_D: 0.7495 Loss_G: 2.1049 D(x): 0.5546 D(G(z)): 0.0518 / 0.0550\n", + "[199/1600][6/8] Loss_D: 0.7870 Loss_G: 2.0258 D(x): 0.6327 D(G(z)): 0.0662 / 0.0617\n", + "[200/1600][0/8] Loss_D: 0.7209 Loss_G: 2.1226 D(x): 0.6306 D(G(z)): 0.0581 / 0.0536\n", + "[200/1600][2/8] Loss_D: 0.7078 Loss_G: 1.9709 D(x): 0.5927 D(G(z)): 0.0578 / 0.0681\n", + "[200/1600][4/8] Loss_D: 0.6869 Loss_G: 1.8918 D(x): 0.6237 D(G(z)): 0.0669 / 0.0755\n", + "[200/1600][6/8] Loss_D: 0.6745 Loss_G: 1.8553 D(x): 0.6708 D(G(z)): 0.0814 / 0.0810\n", + "[201/1600][0/8] Loss_D: 0.7144 Loss_G: 2.0624 D(x): 0.6765 D(G(z)): 0.0714 / 0.0590\n", + "[201/1600][2/8] Loss_D: 0.7269 Loss_G: 2.0813 D(x): 0.5931 D(G(z)): 0.0578 / 0.0581\n", + "[201/1600][4/8] Loss_D: 0.7435 Loss_G: 2.0126 D(x): 0.6069 D(G(z)): 0.0621 / 0.0628\n", + "[201/1600][6/8] Loss_D: 0.7553 Loss_G: 2.0649 D(x): 0.6075 D(G(z)): 0.0612 / 0.0607\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[202/1600][0/8] Loss_D: 0.7692 Loss_G: 2.0235 D(x): 0.6403 D(G(z)): 0.0640 / 0.0630\n", + "[202/1600][2/8] Loss_D: 0.7514 Loss_G: 2.0164 D(x): 0.5720 D(G(z)): 0.0619 / 0.0625\n", + "[202/1600][4/8] Loss_D: 0.7728 Loss_G: 1.9768 D(x): 0.6116 D(G(z)): 0.0677 / 0.0671\n", + "[202/1600][6/8] Loss_D: 0.7712 Loss_G: 2.0782 D(x): 0.5799 D(G(z)): 0.0576 / 0.0571\n", + "[203/1600][0/8] Loss_D: 0.7589 Loss_G: 2.0526 D(x): 0.5770 D(G(z)): 0.0603 / 0.0602\n", + "[203/1600][2/8] Loss_D: 0.7661 Loss_G: 2.0103 D(x): 0.6111 D(G(z)): 0.0643 / 0.0640\n", + "[203/1600][4/8] Loss_D: 0.7384 Loss_G: 2.0374 D(x): 0.6304 D(G(z)): 0.0618 / 0.0617\n", + "[203/1600][6/8] Loss_D: 0.7149 Loss_G: 2.0270 D(x): 0.6104 D(G(z)): 0.0609 / 0.0615\n", + "[204/1600][0/8] Loss_D: 0.6816 Loss_G: 2.1033 D(x): 0.6221 D(G(z)): 0.0546 / 0.0553\n", + "[204/1600][2/8] Loss_D: 0.7334 Loss_G: 1.9749 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D(x): 0.6098 D(G(z)): 0.0628 / 0.0628\n", + "[234/1600][6/8] Loss_D: 0.6855 Loss_G: 2.0154 D(x): 0.6153 D(G(z)): 0.0621 / 0.0634\n", + "[235/1600][0/8] Loss_D: 0.7806 Loss_G: 2.0695 D(x): 0.6441 D(G(z)): 0.0596 / 0.0589\n", + "[235/1600][2/8] Loss_D: 0.7267 Loss_G: 2.0374 D(x): 0.6623 D(G(z)): 0.0623 / 0.0617\n", + "[235/1600][4/8] Loss_D: 0.7520 Loss_G: 2.0005 D(x): 0.6396 D(G(z)): 0.0665 / 0.0659\n", + "[235/1600][6/8] Loss_D: 0.7689 Loss_G: 2.0238 D(x): 0.6304 D(G(z)): 0.0639 / 0.0629\n", + "[236/1600][0/8] Loss_D: 0.7762 Loss_G: 2.0278 D(x): 0.6312 D(G(z)): 0.0635 / 0.0623\n", + "[236/1600][2/8] Loss_D: 0.7501 Loss_G: 2.0141 D(x): 0.5942 D(G(z)): 0.0634 / 0.0633\n", + "[236/1600][4/8] Loss_D: 0.7550 Loss_G: 2.0870 D(x): 0.6443 D(G(z)): 0.0574 / 0.0565\n", + "[236/1600][6/8] Loss_D: 0.7294 Loss_G: 2.1228 D(x): 0.6619 D(G(z)): 0.0562 / 0.0556\n", + "[237/1600][0/8] Loss_D: 0.6797 Loss_G: 2.0931 D(x): 0.6276 D(G(z)): 0.0568 / 0.0576\n", + "[237/1600][2/8] Loss_D: 0.7291 Loss_G: 2.1497 D(x): 0.6509 D(G(z)): 0.0525 / 0.0527\n", + "[237/1600][4/8] Loss_D: 0.6963 Loss_G: 2.0609 D(x): 0.6137 D(G(z)): 0.0591 / 0.0594\n", + "[237/1600][6/8] Loss_D: 0.7727 Loss_G: 2.0402 D(x): 0.6567 D(G(z)): 0.0624 / 0.0615\n", + "[238/1600][0/8] Loss_D: 0.7153 Loss_G: 2.1617 D(x): 0.6167 D(G(z)): 0.0530 / 0.0528\n", + "[238/1600][2/8] Loss_D: 0.7606 Loss_G: 2.0265 D(x): 0.5642 D(G(z)): 0.0628 / 0.0630\n", + "[238/1600][4/8] Loss_D: 0.7494 Loss_G: 2.0409 D(x): 0.6483 D(G(z)): 0.0607 / 0.0598\n", + "[238/1600][6/8] Loss_D: 0.7609 Loss_G: 2.0678 D(x): 0.6254 D(G(z)): 0.0590 / 0.0586\n", + "[239/1600][0/8] Loss_D: 0.7706 Loss_G: 2.0377 D(x): 0.6242 D(G(z)): 0.0631 / 0.0619\n", + "[239/1600][2/8] Loss_D: 0.7452 Loss_G: 2.1601 D(x): 0.6098 D(G(z)): 0.0524 / 0.0522\n", + "[239/1600][4/8] Loss_D: 0.7515 Loss_G: 2.0860 D(x): 0.6085 D(G(z)): 0.0583 / 0.0580\n", + "[239/1600][6/8] Loss_D: 0.7318 Loss_G: 2.1633 D(x): 0.6157 D(G(z)): 0.0517 / 0.0518\n", + "[240/1600][0/8] Loss_D: 0.7607 Loss_G: 2.1406 D(x): 0.6248 D(G(z)): 0.0535 / 0.0535\n", + "[240/1600][2/8] Loss_D: 0.7314 Loss_G: 2.0975 D(x): 0.5643 D(G(z)): 0.0550 / 0.0561\n", + "[240/1600][4/8] Loss_D: 0.6866 Loss_G: 2.0523 D(x): 0.6316 D(G(z)): 0.0599 / 0.0608\n", + "[240/1600][6/8] Loss_D: 0.6586 Loss_G: 2.0146 D(x): 0.6598 D(G(z)): 0.0626 / 0.0632\n", + "[241/1600][0/8] Loss_D: 0.7195 Loss_G: 2.0246 D(x): 0.6477 D(G(z)): 0.0623 / 0.0625\n", + "[241/1600][2/8] Loss_D: 0.7748 Loss_G: 2.0382 D(x): 0.6367 D(G(z)): 0.0630 / 0.0622\n", + "[241/1600][4/8] Loss_D: 0.6908 Loss_G: 2.0020 D(x): 0.6488 D(G(z)): 0.0647 / 0.0648\n", + "[241/1600][6/8] Loss_D: 0.7201 Loss_G: 2.0189 D(x): 0.6192 D(G(z)): 0.0639 / 0.0639\n", + "[242/1600][0/8] Loss_D: 0.7338 Loss_G: 2.0535 D(x): 0.6590 D(G(z)): 0.0616 / 0.0611\n", + "[242/1600][2/8] Loss_D: 0.6810 Loss_G: 2.0158 D(x): 0.6431 D(G(z)): 0.0625 / 0.0628\n", + "[242/1600][4/8] Loss_D: 0.7890 Loss_G: 2.0117 D(x): 0.6388 D(G(z)): 0.0656 / 0.0644\n", + "[242/1600][6/8] Loss_D: 0.6973 Loss_G: 2.0016 D(x): 0.6640 D(G(z)): 0.0664 / 0.0657\n", + "[243/1600][0/8] Loss_D: 0.7632 Loss_G: 2.0678 D(x): 0.6168 D(G(z)): 0.0600 / 0.0592\n", + "[243/1600][2/8] Loss_D: 0.7628 Loss_G: 2.0832 D(x): 0.6493 D(G(z)): 0.0590 / 0.0576\n", + "[243/1600][4/8] Loss_D: 0.7427 Loss_G: 2.0279 D(x): 0.5887 D(G(z)): 0.0632 / 0.0634\n", + "[243/1600][6/8] Loss_D: 0.6766 Loss_G: 2.0654 D(x): 0.6322 D(G(z)): 0.0588 / 0.0600\n", + "[244/1600][0/8] Loss_D: 0.7128 Loss_G: 2.0252 D(x): 0.6279 D(G(z)): 0.0618 / 0.0628\n", + "[244/1600][2/8] Loss_D: 0.7090 Loss_G: 2.0487 D(x): 0.6303 D(G(z)): 0.0598 / 0.0606\n", + "[244/1600][4/8] Loss_D: 0.7709 Loss_G: 1.9936 D(x): 0.6475 D(G(z)): 0.0665 / 0.0658\n", + "[244/1600][6/8] Loss_D: 0.7240 Loss_G: 2.0619 D(x): 0.6548 D(G(z)): 0.0600 / 0.0594\n", + "[245/1600][0/8] Loss_D: 0.6750 Loss_G: 2.0266 D(x): 0.6570 D(G(z)): 0.0621 / 0.0626\n", + "[245/1600][2/8] Loss_D: 0.7128 Loss_G: 2.0092 D(x): 0.6490 D(G(z)): 0.0646 / 0.0644\n", + "[245/1600][4/8] Loss_D: 0.7897 Loss_G: 2.0171 D(x): 0.6578 D(G(z)): 0.0653 / 0.0633\n", + "[245/1600][6/8] Loss_D: 0.6930 Loss_G: 1.9885 D(x): 0.6373 D(G(z)): 0.0656 / 0.0657\n", + "[246/1600][0/8] Loss_D: 0.6850 Loss_G: 2.0706 D(x): 0.6196 D(G(z)): 0.0596 / 0.0606\n", + "[246/1600][2/8] Loss_D: 0.6820 Loss_G: 2.0497 D(x): 0.6085 D(G(z)): 0.0596 / 0.0611\n", + "[246/1600][4/8] Loss_D: 0.7003 Loss_G: 1.9643 D(x): 0.6601 D(G(z)): 0.0684 / 0.0686\n", + "[246/1600][6/8] Loss_D: 0.7124 Loss_G: 1.9830 D(x): 0.6596 D(G(z)): 0.0674 / 0.0677\n", + "[247/1600][0/8] Loss_D: 0.7391 Loss_G: 1.9803 D(x): 0.6839 D(G(z)): 0.0683 / 0.0669\n", + "[247/1600][2/8] Loss_D: 0.6941 Loss_G: 2.0057 D(x): 0.6569 D(G(z)): 0.0658 / 0.0649\n", + "[247/1600][4/8] Loss_D: 0.7766 Loss_G: 2.0193 D(x): 0.6060 D(G(z)): 0.0653 / 0.0639\n", + "[247/1600][6/8] Loss_D: 0.7643 Loss_G: 2.0598 D(x): 0.6114 D(G(z)): 0.0615 / 0.0609\n", + "[248/1600][0/8] Loss_D: 0.6496 Loss_G: 2.1224 D(x): 0.6489 D(G(z)): 0.0543 / 0.0544\n", + "[248/1600][2/8] Loss_D: 0.6596 Loss_G: 2.0590 D(x): 0.6364 D(G(z)): 0.0602 / 0.0606\n", + "[248/1600][4/8] Loss_D: 0.7062 Loss_G: 2.0813 D(x): 0.6507 D(G(z)): 0.0576 / 0.0572\n", + "[248/1600][6/8] Loss_D: 0.7855 Loss_G: 2.0800 D(x): 0.6267 D(G(z)): 0.0589 / 0.0575\n", + "[249/1600][0/8] Loss_D: 0.7374 Loss_G: 2.0894 D(x): 0.5744 D(G(z)): 0.0559 / 0.0562\n", + "[249/1600][2/8] Loss_D: 0.7443 Loss_G: 2.0515 D(x): 0.6462 D(G(z)): 0.0609 / 0.0608\n", + "[249/1600][4/8] Loss_D: 0.7399 Loss_G: 2.1242 D(x): 0.6249 D(G(z)): 0.0537 / 0.0533\n", + "[249/1600][6/8] Loss_D: 0.7122 Loss_G: 2.0903 D(x): 0.5958 D(G(z)): 0.0566 / 0.0571\n", + "[250/1600][0/8] Loss_D: 0.6910 Loss_G: 2.0964 D(x): 0.6099 D(G(z)): 0.0558 / 0.0567\n", + "[250/1600][2/8] Loss_D: 0.7436 Loss_G: 2.0237 D(x): 0.6167 D(G(z)): 0.0622 / 0.0616\n", + "[250/1600][4/8] Loss_D: 0.7198 Loss_G: 2.0581 D(x): 0.5952 D(G(z)): 0.0591 / 0.0595\n", + "[250/1600][6/8] Loss_D: 0.7358 Loss_G: 2.0847 D(x): 0.6200 D(G(z)): 0.0578 / 0.0578\n", + "[251/1600][0/8] Loss_D: 0.7213 Loss_G: 2.0519 D(x): 0.6090 D(G(z)): 0.0596 / 0.0599\n", + "[251/1600][2/8] Loss_D: 0.7680 Loss_G: 2.0212 D(x): 0.6432 D(G(z)): 0.0644 / 0.0637\n", + "[251/1600][4/8] Loss_D: 0.7026 Loss_G: 2.1016 D(x): 0.6183 D(G(z)): 0.0564 / 0.0565\n", + "[251/1600][6/8] Loss_D: 0.7124 Loss_G: 2.0812 D(x): 0.6097 D(G(z)): 0.0575 / 0.0577\n", + "[252/1600][0/8] Loss_D: 0.6803 Loss_G: 2.0535 D(x): 0.6375 D(G(z)): 0.0592 / 0.0600\n", + "[252/1600][2/8] Loss_D: 0.7279 Loss_G: 2.0665 D(x): 0.6655 D(G(z)): 0.0584 / 0.0580\n", + "[252/1600][4/8] Loss_D: 0.7043 Loss_G: 2.0446 D(x): 0.6586 D(G(z)): 0.0613 / 0.0615\n", + "[252/1600][6/8] Loss_D: 0.6844 Loss_G: 2.1231 D(x): 0.6390 D(G(z)): 0.0522 / 0.0527\n", + "[253/1600][0/8] Loss_D: 0.7597 Loss_G: 2.0807 D(x): 0.6425 D(G(z)): 0.0587 / 0.0579\n", + "[253/1600][2/8] Loss_D: 0.6748 Loss_G: 2.0503 D(x): 0.6205 D(G(z)): 0.0601 / 0.0607\n", + "[253/1600][4/8] Loss_D: 0.6566 Loss_G: 2.0484 D(x): 0.6732 D(G(z)): 0.0615 / 0.0621\n", + "[253/1600][6/8] Loss_D: 0.7345 Loss_G: 2.0125 D(x): 0.6700 D(G(z)): 0.0658 / 0.0651\n", + "[254/1600][0/8] Loss_D: 0.6768 Loss_G: 1.9805 D(x): 0.6511 D(G(z)): 0.0668 / 0.0668\n", + "[254/1600][2/8] Loss_D: 0.6745 Loss_G: 2.0126 D(x): 0.6642 D(G(z)): 0.0641 / 0.0648\n", + "[254/1600][4/8] Loss_D: 0.6702 Loss_G: 2.0201 D(x): 0.6596 D(G(z)): 0.0640 / 0.0646\n", + "[254/1600][6/8] Loss_D: 0.7588 Loss_G: 1.9790 D(x): 0.6650 D(G(z)): 0.0677 / 0.0669\n", + "[255/1600][0/8] Loss_D: 0.6558 Loss_G: 2.0465 D(x): 0.6856 D(G(z)): 0.0612 / 0.0614\n", + "[255/1600][2/8] Loss_D: 0.7103 Loss_G: 2.0117 D(x): 0.6434 D(G(z)): 0.0645 / 0.0641\n", + "[255/1600][4/8] Loss_D: 0.7173 Loss_G: 1.9918 D(x): 0.6344 D(G(z)): 0.0672 / 0.0667\n", + "[255/1600][6/8] Loss_D: 0.7395 Loss_G: 1.9995 D(x): 0.6252 D(G(z)): 0.0662 / 0.0658\n", + "[256/1600][0/8] Loss_D: 0.7165 Loss_G: 2.0044 D(x): 0.6162 D(G(z)): 0.0648 / 0.0653\n", + "[256/1600][2/8] Loss_D: 0.7557 Loss_G: 1.9962 D(x): 0.6578 D(G(z)): 0.0678 / 0.0668\n", + "[256/1600][4/8] Loss_D: 0.6705 Loss_G: 2.0429 D(x): 0.6663 D(G(z)): 0.0615 / 0.0610\n", + "[256/1600][6/8] Loss_D: 0.7427 Loss_G: 1.9421 D(x): 0.6132 D(G(z)): 0.0715 / 0.0713\n", + "[257/1600][0/8] Loss_D: 0.7848 Loss_G: 2.0503 D(x): 0.6370 D(G(z)): 0.0621 / 0.0606\n", + "[257/1600][2/8] Loss_D: 0.6856 Loss_G: 2.1248 D(x): 0.6640 D(G(z)): 0.0547 / 0.0545\n", + "[257/1600][4/8] Loss_D: 0.7525 Loss_G: 2.0879 D(x): 0.6285 D(G(z)): 0.0576 / 0.0571\n", + "[257/1600][6/8] Loss_D: 0.6794 Loss_G: 2.0093 D(x): 0.6361 D(G(z)): 0.0642 / 0.0654\n", + "[258/1600][0/8] Loss_D: 0.7924 Loss_G: 2.0163 D(x): 0.6895 D(G(z)): 0.0652 / 0.0638\n", + "[258/1600][2/8] Loss_D: 0.7002 Loss_G: 1.9735 D(x): 0.6088 D(G(z)): 0.0664 / 0.0674\n", + "[258/1600][4/8] Loss_D: 0.7509 Loss_G: 2.0396 D(x): 0.6742 D(G(z)): 0.0642 / 0.0629\n", + "[258/1600][6/8] Loss_D: 0.7137 Loss_G: 2.0147 D(x): 0.5922 D(G(z)): 0.0626 / 0.0634\n", + "[259/1600][0/8] Loss_D: 0.7482 Loss_G: 2.0163 D(x): 0.6550 D(G(z)): 0.0637 / 0.0635\n", + "[259/1600][2/8] Loss_D: 0.6890 Loss_G: 2.0186 D(x): 0.6719 D(G(z)): 0.0641 / 0.0634\n", + "[259/1600][4/8] Loss_D: 0.7072 Loss_G: 2.0068 D(x): 0.6194 D(G(z)): 0.0642 / 0.0648\n", + "[259/1600][6/8] Loss_D: 0.7401 Loss_G: 2.0900 D(x): 0.6024 D(G(z)): 0.0566 / 0.0569\n", + "[260/1600][0/8] Loss_D: 0.6930 Loss_G: 2.0045 D(x): 0.6208 D(G(z)): 0.0641 / 0.0646\n", + "[260/1600][2/8] Loss_D: 0.6927 Loss_G: 2.0279 D(x): 0.6380 D(G(z)): 0.0618 / 0.0617\n", + "[260/1600][4/8] Loss_D: 0.7007 Loss_G: 2.0057 D(x): 0.6262 D(G(z)): 0.0639 / 0.0638\n", + "[260/1600][6/8] Loss_D: 0.7197 Loss_G: 2.0309 D(x): 0.6396 D(G(z)): 0.0629 / 0.0625\n", + "[261/1600][0/8] Loss_D: 0.7175 Loss_G: 2.0277 D(x): 0.6713 D(G(z)): 0.0612 / 0.0611\n", + "[261/1600][2/8] Loss_D: 0.6970 Loss_G: 2.0057 D(x): 0.6821 D(G(z)): 0.0655 / 0.0651\n", + "[261/1600][4/8] Loss_D: 0.7429 Loss_G: 2.0885 D(x): 0.6161 D(G(z)): 0.0595 / 0.0589\n", + "[261/1600][6/8] Loss_D: 0.7356 Loss_G: 2.1143 D(x): 0.6039 D(G(z)): 0.0558 / 0.0555\n", + "[262/1600][0/8] Loss_D: 0.7126 Loss_G: 2.0512 D(x): 0.6496 D(G(z)): 0.0590 / 0.0593\n", + "[262/1600][2/8] Loss_D: 0.7231 Loss_G: 2.1074 D(x): 0.6280 D(G(z)): 0.0557 / 0.0554\n", + "[262/1600][4/8] Loss_D: 0.7725 Loss_G: 2.0870 D(x): 0.6394 D(G(z)): 0.0591 / 0.0583\n", + "[262/1600][6/8] Loss_D: 0.7051 Loss_G: 2.0504 D(x): 0.6249 D(G(z)): 0.0607 / 0.0606\n", + "[263/1600][0/8] Loss_D: 0.7427 Loss_G: 1.9727 D(x): 0.6191 D(G(z)): 0.0673 / 0.0677\n", + "[263/1600][2/8] Loss_D: 0.7678 Loss_G: 2.1369 D(x): 0.6126 D(G(z)): 0.0549 / 0.0541\n", + "[263/1600][4/8] Loss_D: 0.6679 Loss_G: 2.0510 D(x): 0.6489 D(G(z)): 0.0606 / 0.0608\n", + "[263/1600][6/8] Loss_D: 0.7557 Loss_G: 2.0928 D(x): 0.6134 D(G(z)): 0.0591 / 0.0583\n", + "[264/1600][0/8] Loss_D: 0.7641 Loss_G: 2.0856 D(x): 0.5932 D(G(z)): 0.0584 / 0.0579\n", + "[264/1600][2/8] Loss_D: 0.7134 Loss_G: 2.0438 D(x): 0.6282 D(G(z)): 0.0599 / 0.0595\n", + "[264/1600][4/8] Loss_D: 0.7667 Loss_G: 2.1023 D(x): 0.6092 D(G(z)): 0.0576 / 0.0570\n", + "[264/1600][6/8] Loss_D: 0.7312 Loss_G: 2.1402 D(x): 0.6492 D(G(z)): 0.0540 / 0.0535\n", + "[265/1600][0/8] Loss_D: 0.7225 Loss_G: 2.0026 D(x): 0.5966 D(G(z)): 0.0622 / 0.0632\n", + "[265/1600][2/8] Loss_D: 0.7141 Loss_G: 2.1251 D(x): 0.6568 D(G(z)): 0.0549 / 0.0546\n", + "[265/1600][4/8] Loss_D: 0.7356 Loss_G: 2.0336 D(x): 0.6323 D(G(z)): 0.0629 / 0.0629\n", + "[265/1600][6/8] Loss_D: 0.7698 Loss_G: 2.1074 D(x): 0.6338 D(G(z)): 0.0564 / 0.0552\n", + "[266/1600][0/8] Loss_D: 0.7188 Loss_G: 2.0566 D(x): 0.6011 D(G(z)): 0.0604 / 0.0607\n", + "[266/1600][2/8] Loss_D: 0.7548 Loss_G: 2.0920 D(x): 0.5894 D(G(z)): 0.0575 / 0.0582\n", + "[266/1600][4/8] Loss_D: 0.7205 Loss_G: 2.0700 D(x): 0.6141 D(G(z)): 0.0582 / 0.0586\n", + "[266/1600][6/8] Loss_D: 0.7407 Loss_G: 2.0521 D(x): 0.6317 D(G(z)): 0.0610 / 0.0608\n", + "[267/1600][0/8] Loss_D: 0.6983 Loss_G: 2.0403 D(x): 0.5933 D(G(z)): 0.0605 / 0.0612\n", + "[267/1600][2/8] Loss_D: 0.7733 Loss_G: 2.0434 D(x): 0.6332 D(G(z)): 0.0619 / 0.0612\n", + "[267/1600][4/8] Loss_D: 0.6965 Loss_G: 2.1166 D(x): 0.6473 D(G(z)): 0.0560 / 0.0557\n", + "[267/1600][6/8] Loss_D: 0.6894 Loss_G: 2.0664 D(x): 0.6244 D(G(z)): 0.0583 / 0.0590\n", + "[268/1600][0/8] Loss_D: 0.7629 Loss_G: 2.0614 D(x): 0.6385 D(G(z)): 0.0586 / 0.0582\n", + "[268/1600][2/8] Loss_D: 0.7300 Loss_G: 2.0162 D(x): 0.6554 D(G(z)): 0.0639 / 0.0632\n", + "[268/1600][4/8] Loss_D: 0.7591 Loss_G: 2.0694 D(x): 0.6257 D(G(z)): 0.0607 / 0.0592\n", + "[268/1600][6/8] Loss_D: 0.7276 Loss_G: 2.1257 D(x): 0.6140 D(G(z)): 0.0544 / 0.0543\n", + "[269/1600][0/8] Loss_D: 0.7519 Loss_G: 2.0313 D(x): 0.6315 D(G(z)): 0.0634 / 0.0634\n", + "[269/1600][2/8] Loss_D: 0.7257 Loss_G: 1.9871 D(x): 0.6346 D(G(z)): 0.0677 / 0.0680\n", + "[269/1600][4/8] Loss_D: 0.7618 Loss_G: 2.0628 D(x): 0.6069 D(G(z)): 0.0609 / 0.0600\n", + "[269/1600][6/8] Loss_D: 0.7633 Loss_G: 2.0750 D(x): 0.6053 D(G(z)): 0.0590 / 0.0582\n", + "[270/1600][0/8] Loss_D: 0.6992 Loss_G: 2.0681 D(x): 0.6201 D(G(z)): 0.0590 / 0.0601\n", + "[270/1600][2/8] Loss_D: 0.6912 Loss_G: 2.0488 D(x): 0.6244 D(G(z)): 0.0592 / 0.0601\n", + "[270/1600][4/8] Loss_D: 0.7708 Loss_G: 2.0488 D(x): 0.5845 D(G(z)): 0.0618 / 0.0614\n", + "[270/1600][6/8] Loss_D: 0.6704 Loss_G: 2.0649 D(x): 0.6912 D(G(z)): 0.0591 / 0.0594\n", + "[271/1600][0/8] Loss_D: 0.7169 Loss_G: 2.0514 D(x): 0.6237 D(G(z)): 0.0613 / 0.0615\n", + "[271/1600][2/8] Loss_D: 0.6987 Loss_G: 1.9955 D(x): 0.6427 D(G(z)): 0.0657 / 0.0664\n", + "[271/1600][4/8] Loss_D: 0.7762 Loss_G: 2.0211 D(x): 0.6599 D(G(z)): 0.0651 / 0.0631\n", + "[271/1600][6/8] Loss_D: 0.7210 Loss_G: 2.0872 D(x): 0.6637 D(G(z)): 0.0574 / 0.0565\n", + "[272/1600][0/8] Loss_D: 0.7279 Loss_G: 2.0227 D(x): 0.6140 D(G(z)): 0.0633 / 0.0634\n", + "[272/1600][2/8] Loss_D: 0.7324 Loss_G: 2.0350 D(x): 0.6174 D(G(z)): 0.0626 / 0.0624\n", + "[272/1600][4/8] Loss_D: 0.7138 Loss_G: 1.9836 D(x): 0.6284 D(G(z)): 0.0659 / 0.0664\n", + "[272/1600][6/8] Loss_D: 0.6795 Loss_G: 1.9029 D(x): 0.6720 D(G(z)): 0.0730 / 0.0737\n", + "[273/1600][0/8] Loss_D: 0.7383 Loss_G: 2.0267 D(x): 0.6190 D(G(z)): 0.0623 / 0.0625\n", + "[273/1600][2/8] Loss_D: 0.7585 Loss_G: 1.9953 D(x): 0.6389 D(G(z)): 0.0655 / 0.0645\n", + "[273/1600][4/8] Loss_D: 0.7892 Loss_G: 2.0391 D(x): 0.6361 D(G(z)): 0.0626 / 0.0613\n", + "[273/1600][6/8] Loss_D: 0.7543 Loss_G: 2.0173 D(x): 0.6470 D(G(z)): 0.0637 / 0.0626\n", + "[274/1600][0/8] Loss_D: 0.7686 Loss_G: 2.0521 D(x): 0.6440 D(G(z)): 0.0621 / 0.0604\n", + "[274/1600][2/8] Loss_D: 0.7780 Loss_G: 2.1083 D(x): 0.5707 D(G(z)): 0.0562 / 0.0551\n", + "[274/1600][4/8] Loss_D: 0.7406 Loss_G: 2.0629 D(x): 0.6157 D(G(z)): 0.0603 / 0.0601\n", + "[274/1600][6/8] Loss_D: 0.7641 Loss_G: 2.0850 D(x): 0.6158 D(G(z)): 0.0579 / 0.0567\n", + "[275/1600][0/8] Loss_D: 0.6999 Loss_G: 2.1224 D(x): 0.5945 D(G(z)): 0.0536 / 0.0549\n", + "[275/1600][2/8] Loss_D: 0.7716 Loss_G: 2.0843 D(x): 0.6474 D(G(z)): 0.0583 / 0.0586\n", + "[275/1600][4/8] Loss_D: 0.7146 Loss_G: 2.0370 D(x): 0.6367 D(G(z)): 0.0637 / 0.0636\n", + "[275/1600][6/8] Loss_D: 0.7550 Loss_G: 2.0686 D(x): 0.6279 D(G(z)): 0.0590 / 0.0592\n", + "[276/1600][0/8] Loss_D: 0.7784 Loss_G: 2.0324 D(x): 0.5975 D(G(z)): 0.0628 / 0.0618\n", + "[276/1600][2/8] Loss_D: 0.6699 Loss_G: 2.0744 D(x): 0.6166 D(G(z)): 0.0587 / 0.0595\n", + "[276/1600][4/8] Loss_D: 0.7343 Loss_G: 2.0381 D(x): 0.6344 D(G(z)): 0.0631 / 0.0634\n", + "[276/1600][6/8] Loss_D: 0.7135 Loss_G: 2.0508 D(x): 0.6011 D(G(z)): 0.0590 / 0.0590\n", + "[277/1600][0/8] Loss_D: 0.7343 Loss_G: 2.0503 D(x): 0.6586 D(G(z)): 0.0612 / 0.0607\n", + "[277/1600][2/8] Loss_D: 0.7713 Loss_G: 2.0406 D(x): 0.6166 D(G(z)): 0.0608 / 0.0598\n", + "[277/1600][4/8] Loss_D: 0.7526 Loss_G: 1.9884 D(x): 0.6395 D(G(z)): 0.0667 / 0.0661\n", + "[277/1600][6/8] Loss_D: 0.7549 Loss_G: 2.1033 D(x): 0.6206 D(G(z)): 0.0570 / 0.0561\n", + "[278/1600][0/8] Loss_D: 0.7264 Loss_G: 2.0154 D(x): 0.6360 D(G(z)): 0.0625 / 0.0625\n", + "[278/1600][2/8] Loss_D: 0.7395 Loss_G: 2.1385 D(x): 0.6141 D(G(z)): 0.0532 / 0.0530\n", + "[278/1600][4/8] Loss_D: 0.6676 Loss_G: 2.0386 D(x): 0.6444 D(G(z)): 0.0603 / 0.0615\n", + "[278/1600][6/8] Loss_D: 0.7496 Loss_G: 2.0389 D(x): 0.6004 D(G(z)): 0.0620 / 0.0621\n", + "[279/1600][0/8] Loss_D: 0.7447 Loss_G: 2.0449 D(x): 0.6018 D(G(z)): 0.0611 / 0.0607\n", + "[279/1600][2/8] Loss_D: 0.6823 Loss_G: 2.1067 D(x): 0.6285 D(G(z)): 0.0544 / 0.0549\n", + "[279/1600][4/8] Loss_D: 0.7133 Loss_G: 2.0310 D(x): 0.6225 D(G(z)): 0.0610 / 0.0620\n", + "[279/1600][6/8] Loss_D: 0.7395 Loss_G: 2.0481 D(x): 0.6370 D(G(z)): 0.0616 / 0.0616\n", + "[280/1600][0/8] Loss_D: 0.7028 Loss_G: 1.9934 D(x): 0.6420 D(G(z)): 0.0642 / 0.0648\n", + "[280/1600][2/8] Loss_D: 0.6781 Loss_G: 2.0319 D(x): 0.6216 D(G(z)): 0.0625 / 0.0631\n", + "[280/1600][4/8] Loss_D: 0.7789 Loss_G: 1.8918 D(x): 0.5924 D(G(z)): 0.0765 / 0.0767\n", + "[280/1600][6/8] Loss_D: 0.7366 Loss_G: 2.0294 D(x): 0.6593 D(G(z)): 0.0627 / 0.0622\n", + "[281/1600][0/8] Loss_D: 0.7758 Loss_G: 2.0123 D(x): 0.6520 D(G(z)): 0.0654 / 0.0639\n", + "[281/1600][2/8] Loss_D: 0.7598 Loss_G: 2.0186 D(x): 0.6391 D(G(z)): 0.0648 / 0.0634\n", + "[281/1600][4/8] Loss_D: 0.6703 Loss_G: 2.1097 D(x): 0.6697 D(G(z)): 0.0566 / 0.0559\n", + "[281/1600][6/8] Loss_D: 0.7135 Loss_G: 2.0838 D(x): 0.6289 D(G(z)): 0.0583 / 0.0580\n", + "[282/1600][0/8] Loss_D: 0.7226 Loss_G: 2.0964 D(x): 0.6165 D(G(z)): 0.0553 / 0.0556\n", + "[282/1600][2/8] Loss_D: 0.7081 Loss_G: 2.0168 D(x): 0.6528 D(G(z)): 0.0649 / 0.0647\n", + "[282/1600][4/8] Loss_D: 0.7181 Loss_G: 2.0914 D(x): 0.6376 D(G(z)): 0.0570 / 0.0568\n", + "[282/1600][6/8] Loss_D: 0.6993 Loss_G: 2.0513 D(x): 0.6605 D(G(z)): 0.0608 / 0.0610\n", + "[283/1600][0/8] Loss_D: 0.7778 Loss_G: 2.0286 D(x): 0.6227 D(G(z)): 0.0631 / 0.0618\n", + "[283/1600][2/8] Loss_D: 0.7193 Loss_G: 1.9937 D(x): 0.6374 D(G(z)): 0.0663 / 0.0660\n", + "[283/1600][4/8] Loss_D: 0.6816 Loss_G: 2.0579 D(x): 0.6291 D(G(z)): 0.0593 / 0.0599\n", + "[283/1600][6/8] Loss_D: 0.6974 Loss_G: 1.9745 D(x): 0.6123 D(G(z)): 0.0651 / 0.0665\n", + "[284/1600][0/8] Loss_D: 0.7166 Loss_G: 1.9874 D(x): 0.6296 D(G(z)): 0.0648 / 0.0655\n", + "[284/1600][2/8] Loss_D: 0.7598 Loss_G: 1.9681 D(x): 0.6264 D(G(z)): 0.0702 / 0.0693\n", + "[284/1600][4/8] Loss_D: 0.7460 Loss_G: 1.9531 D(x): 0.6258 D(G(z)): 0.0703 / 0.0695\n", + "[284/1600][6/8] Loss_D: 0.7757 Loss_G: 2.0190 D(x): 0.6530 D(G(z)): 0.0650 / 0.0632\n", + "[285/1600][0/8] Loss_D: 0.7400 Loss_G: 2.1393 D(x): 0.5970 D(G(z)): 0.0538 / 0.0535\n", + "[285/1600][2/8] Loss_D: 0.7070 Loss_G: 2.0476 D(x): 0.6169 D(G(z)): 0.0602 / 0.0600\n", + "[285/1600][4/8] Loss_D: 0.7528 Loss_G: 2.1168 D(x): 0.6722 D(G(z)): 0.0547 / 0.0542\n", + "[285/1600][6/8] Loss_D: 0.6839 Loss_G: 2.1125 D(x): 0.6682 D(G(z)): 0.0549 / 0.0548\n", + "[286/1600][0/8] Loss_D: 0.6931 Loss_G: 2.0876 D(x): 0.6415 D(G(z)): 0.0575 / 0.0586\n", + "[286/1600][2/8] Loss_D: 0.7744 Loss_G: 1.9479 D(x): 0.5883 D(G(z)): 0.0702 / 0.0706\n", + "[286/1600][4/8] Loss_D: 0.7560 Loss_G: 2.0656 D(x): 0.6338 D(G(z)): 0.0604 / 0.0589\n", + "[286/1600][6/8] Loss_D: 0.7529 Loss_G: 2.0341 D(x): 0.6296 D(G(z)): 0.0625 / 0.0613\n", + "[287/1600][0/8] Loss_D: 0.7267 Loss_G: 2.0557 D(x): 0.6445 D(G(z)): 0.0594 / 0.0592\n", + "[287/1600][2/8] Loss_D: 0.7073 Loss_G: 1.9603 D(x): 0.6094 D(G(z)): 0.0666 / 0.0686\n", + "[287/1600][4/8] Loss_D: 0.6985 Loss_G: 1.9804 D(x): 0.6633 D(G(z)): 0.0671 / 0.0678\n", + "[287/1600][6/8] Loss_D: 0.6961 Loss_G: 2.0316 D(x): 0.6578 D(G(z)): 0.0632 / 0.0629\n", + "[288/1600][0/8] Loss_D: 0.7229 Loss_G: 2.0831 D(x): 0.6158 D(G(z)): 0.0577 / 0.0576\n", + "[288/1600][2/8] Loss_D: 0.7018 Loss_G: 1.9996 D(x): 0.6158 D(G(z)): 0.0636 / 0.0641\n", + "[288/1600][4/8] Loss_D: 0.7660 Loss_G: 1.9599 D(x): 0.6526 D(G(z)): 0.0686 / 0.0673\n", + "[288/1600][6/8] Loss_D: 0.7005 Loss_G: 2.0608 D(x): 0.6142 D(G(z)): 0.0577 / 0.0587\n", + "[289/1600][0/8] Loss_D: 0.6837 Loss_G: 1.8954 D(x): 0.6258 D(G(z)): 0.0741 / 0.0756\n", + "[289/1600][2/8] Loss_D: 0.7324 Loss_G: 1.9281 D(x): 0.6657 D(G(z)): 0.0746 / 0.0736\n", + "[289/1600][4/8] Loss_D: 0.7013 Loss_G: 1.9254 D(x): 0.6380 D(G(z)): 0.0727 / 0.0724\n", + "[289/1600][6/8] Loss_D: 0.6714 Loss_G: 1.9909 D(x): 0.6570 D(G(z)): 0.0655 / 0.0654\n", + "[290/1600][0/8] Loss_D: 0.6758 Loss_G: 1.9756 D(x): 0.6317 D(G(z)): 0.0670 / 0.0677\n", + "[290/1600][2/8] Loss_D: 0.7063 Loss_G: 1.9984 D(x): 0.6415 D(G(z)): 0.0645 / 0.0647\n", + "[290/1600][4/8] Loss_D: 0.6728 Loss_G: 1.9999 D(x): 0.6536 D(G(z)): 0.0644 / 0.0643\n", + "[290/1600][6/8] Loss_D: 0.7483 Loss_G: 1.9816 D(x): 0.6593 D(G(z)): 0.0683 / 0.0664\n", + "[291/1600][0/8] Loss_D: 0.6551 Loss_G: 1.9996 D(x): 0.6692 D(G(z)): 0.0657 / 0.0660\n", + "[291/1600][2/8] Loss_D: 0.6896 Loss_G: 2.0141 D(x): 0.6433 D(G(z)): 0.0626 / 0.0634\n", + "[291/1600][4/8] Loss_D: 0.6808 Loss_G: 1.9209 D(x): 0.6478 D(G(z)): 0.0721 / 0.0731\n", + "[291/1600][6/8] Loss_D: 0.7386 Loss_G: 1.9415 D(x): 0.6649 D(G(z)): 0.0723 / 0.0708\n", + "[292/1600][0/8] Loss_D: 0.6941 Loss_G: 1.9320 D(x): 0.6260 D(G(z)): 0.0727 / 0.0728\n", + "[292/1600][2/8] Loss_D: 0.7118 Loss_G: 2.0043 D(x): 0.6706 D(G(z)): 0.0640 / 0.0631\n", + "[292/1600][4/8] Loss_D: 0.6632 Loss_G: 2.0743 D(x): 0.6441 D(G(z)): 0.0570 / 0.0573\n", + "[292/1600][6/8] Loss_D: 0.6881 Loss_G: 2.0192 D(x): 0.6724 D(G(z)): 0.0619 / 0.0614\n", + "[293/1600][0/8] Loss_D: 0.7178 Loss_G: 1.9726 D(x): 0.6162 D(G(z)): 0.0671 / 0.0673\n", + "[293/1600][2/8] Loss_D: 0.7770 Loss_G: 2.0445 D(x): 0.6747 D(G(z)): 0.0627 / 0.0611\n", + "[293/1600][4/8] Loss_D: 0.7575 Loss_G: 2.0704 D(x): 0.6094 D(G(z)): 0.0591 / 0.0586\n", + "[293/1600][6/8] Loss_D: 0.6908 Loss_G: 1.9967 D(x): 0.6509 D(G(z)): 0.0655 / 0.0662\n", + "[294/1600][0/8] Loss_D: 0.7278 Loss_G: 2.0078 D(x): 0.6152 D(G(z)): 0.0659 / 0.0656\n", + "[294/1600][2/8] Loss_D: 0.7292 Loss_G: 2.0975 D(x): 0.6605 D(G(z)): 0.0574 / 0.0564\n", + "[294/1600][4/8] Loss_D: 0.7118 Loss_G: 2.0540 D(x): 0.6463 D(G(z)): 0.0595 / 0.0593\n", + "[294/1600][6/8] Loss_D: 0.7573 Loss_G: 2.0888 D(x): 0.6322 D(G(z)): 0.0571 / 0.0562\n", + "[295/1600][0/8] Loss_D: 0.7411 Loss_G: 2.0058 D(x): 0.6111 D(G(z)): 0.0647 / 0.0646\n", + "[295/1600][2/8] Loss_D: 0.6891 Loss_G: 2.0378 D(x): 0.6261 D(G(z)): 0.0601 / 0.0611\n", + "[295/1600][4/8] Loss_D: 0.7550 Loss_G: 2.0362 D(x): 0.6329 D(G(z)): 0.0625 / 0.0618\n", + "[295/1600][6/8] Loss_D: 0.7052 Loss_G: 2.0345 D(x): 0.6388 D(G(z)): 0.0619 / 0.0617\n", + "[296/1600][0/8] Loss_D: 0.7421 Loss_G: 2.0291 D(x): 0.6397 D(G(z)): 0.0625 / 0.0618\n", + "[296/1600][2/8] Loss_D: 0.7564 Loss_G: 2.1093 D(x): 0.6283 D(G(z)): 0.0557 / 0.0548\n", + "[296/1600][4/8] Loss_D: 0.6898 Loss_G: 2.0825 D(x): 0.6482 D(G(z)): 0.0576 / 0.0585\n", + "[296/1600][6/8] Loss_D: 0.7075 Loss_G: 2.0265 D(x): 0.6201 D(G(z)): 0.0622 / 0.0628\n", + "[297/1600][0/8] Loss_D: 0.7204 Loss_G: 1.9847 D(x): 0.6595 D(G(z)): 0.0677 / 0.0670\n", + "[297/1600][2/8] Loss_D: 0.6921 Loss_G: 2.0297 D(x): 0.6401 D(G(z)): 0.0622 / 0.0621\n", + "[297/1600][4/8] Loss_D: 0.7641 Loss_G: 2.0535 D(x): 0.6397 D(G(z)): 0.0627 / 0.0612\n", + "[297/1600][6/8] Loss_D: 0.7289 Loss_G: 2.1479 D(x): 0.6098 D(G(z)): 0.0526 / 0.0522\n", + "[298/1600][0/8] Loss_D: 0.6895 Loss_G: 2.1026 D(x): 0.6343 D(G(z)): 0.0552 / 0.0555\n", + "[298/1600][2/8] Loss_D: 0.7851 Loss_G: 2.0382 D(x): 0.6468 D(G(z)): 0.0618 / 0.0610\n", + "[298/1600][4/8] Loss_D: 0.7372 Loss_G: 2.0743 D(x): 0.5970 D(G(z)): 0.0581 / 0.0584\n", + "[298/1600][6/8] Loss_D: 0.7250 Loss_G: 2.0491 D(x): 0.6320 D(G(z)): 0.0594 / 0.0601\n", + "[299/1600][0/8] Loss_D: 0.7257 Loss_G: 1.9537 D(x): 0.6037 D(G(z)): 0.0674 / 0.0689\n", + "[299/1600][2/8] Loss_D: 0.7715 Loss_G: 1.9735 D(x): 0.5951 D(G(z)): 0.0686 / 0.0675\n", + "[299/1600][4/8] Loss_D: 0.7903 Loss_G: 2.0742 D(x): 0.6429 D(G(z)): 0.0617 / 0.0600\n", + "[299/1600][6/8] Loss_D: 0.7660 Loss_G: 2.0842 D(x): 0.6034 D(G(z)): 0.0587 / 0.0572\n", + "[300/1600][0/8] Loss_D: 0.7394 Loss_G: 2.0602 D(x): 0.6127 D(G(z)): 0.0605 / 0.0601\n", + "[300/1600][2/8] Loss_D: 0.7301 Loss_G: 2.0789 D(x): 0.6122 D(G(z)): 0.0569 / 0.0575\n", + "[300/1600][4/8] Loss_D: 0.7751 Loss_G: 2.0973 D(x): 0.6441 D(G(z)): 0.0580 / 0.0565\n", + "[300/1600][6/8] Loss_D: 0.7558 Loss_G: 2.1338 D(x): 0.6005 D(G(z)): 0.0542 / 0.0535\n", + "[301/1600][0/8] Loss_D: 0.7698 Loss_G: 2.0456 D(x): 0.5871 D(G(z)): 0.0611 / 0.0602\n", + "[301/1600][2/8] Loss_D: 0.6896 Loss_G: 2.0263 D(x): 0.6137 D(G(z)): 0.0597 / 0.0612\n", + "[301/1600][4/8] Loss_D: 0.7511 Loss_G: 2.1001 D(x): 0.6062 D(G(z)): 0.0557 / 0.0554\n", + "[301/1600][6/8] Loss_D: 0.7661 Loss_G: 2.1012 D(x): 0.6195 D(G(z)): 0.0577 / 0.0564\n", + "[302/1600][0/8] Loss_D: 0.7142 Loss_G: 2.0118 D(x): 0.5853 D(G(z)): 0.0628 / 0.0648\n", + "[302/1600][2/8] Loss_D: 0.7627 Loss_G: 2.0226 D(x): 0.6191 D(G(z)): 0.0622 / 0.0628\n", + "[302/1600][4/8] Loss_D: 0.7280 Loss_G: 2.0196 D(x): 0.6323 D(G(z)): 0.0620 / 0.0621\n", + "[302/1600][6/8] Loss_D: 0.7615 Loss_G: 1.9933 D(x): 0.6147 D(G(z)): 0.0659 / 0.0659\n", + "[303/1600][0/8] Loss_D: 0.7003 Loss_G: 1.9814 D(x): 0.6809 D(G(z)): 0.0667 / 0.0666\n", + "[303/1600][2/8] Loss_D: 0.7467 Loss_G: 1.9455 D(x): 0.6217 D(G(z)): 0.0709 / 0.0705\n", + "[303/1600][4/8] Loss_D: 0.6647 Loss_G: 2.0476 D(x): 0.6349 D(G(z)): 0.0603 / 0.0607\n", + "[303/1600][6/8] Loss_D: 0.7595 Loss_G: 2.0201 D(x): 0.6524 D(G(z)): 0.0645 / 0.0632\n", + "[304/1600][0/8] Loss_D: 0.6824 Loss_G: 2.0336 D(x): 0.6590 D(G(z)): 0.0610 / 0.0614\n", + "[304/1600][2/8] Loss_D: 0.7185 Loss_G: 1.8633 D(x): 0.6300 D(G(z)): 0.0791 / 0.0802\n", + "[304/1600][4/8] Loss_D: 0.7722 Loss_G: 1.9597 D(x): 0.6316 D(G(z)): 0.0709 / 0.0699\n", + "[304/1600][6/8] Loss_D: 0.7194 Loss_G: 1.9295 D(x): 0.6337 D(G(z)): 0.0723 / 0.0729\n", + "[305/1600][0/8] Loss_D: 0.7517 Loss_G: 1.9769 D(x): 0.6515 D(G(z)): 0.0701 / 0.0687\n", + "[305/1600][2/8] Loss_D: 0.7327 Loss_G: 1.8916 D(x): 0.6339 D(G(z)): 0.0766 / 0.0759\n", + "[305/1600][4/8] Loss_D: 0.7539 Loss_G: 2.0702 D(x): 0.6593 D(G(z)): 0.0607 / 0.0589\n", + "[305/1600][6/8] Loss_D: 0.7496 Loss_G: 2.0142 D(x): 0.5723 D(G(z)): 0.0637 / 0.0640\n", + "[306/1600][0/8] Loss_D: 0.7777 Loss_G: 2.0072 D(x): 0.6201 D(G(z)): 0.0648 / 0.0641\n", + "[306/1600][2/8] Loss_D: 0.7707 Loss_G: 2.0485 D(x): 0.5795 D(G(z)): 0.0606 / 0.0604\n", + "[306/1600][4/8] Loss_D: 0.7907 Loss_G: 2.0696 D(x): 0.6173 D(G(z)): 0.0603 / 0.0591\n", + "[306/1600][6/8] Loss_D: 0.7232 Loss_G: 2.0304 D(x): 0.6074 D(G(z)): 0.0619 / 0.0620\n", + "[307/1600][0/8] Loss_D: 0.7698 Loss_G: 2.0690 D(x): 0.6254 D(G(z)): 0.0609 / 0.0602\n", + "[307/1600][2/8] Loss_D: 0.7759 Loss_G: 2.0252 D(x): 0.5816 D(G(z)): 0.0641 / 0.0625\n", + "[307/1600][4/8] Loss_D: 0.7334 Loss_G: 2.0364 D(x): 0.5615 D(G(z)): 0.0614 / 0.0626\n", + "[307/1600][6/8] Loss_D: 0.7326 Loss_G: 1.9232 D(x): 0.5947 D(G(z)): 0.0705 / 0.0730\n", + "[308/1600][0/8] Loss_D: 0.7343 Loss_G: 1.9743 D(x): 0.6272 D(G(z)): 0.0657 / 0.0668\n", + "[308/1600][2/8] Loss_D: 0.7397 Loss_G: 1.9783 D(x): 0.6592 D(G(z)): 0.0691 / 0.0678\n", + "[308/1600][4/8] Loss_D: 0.6579 Loss_G: 2.0115 D(x): 0.6455 D(G(z)): 0.0635 / 0.0645\n", + "[308/1600][6/8] Loss_D: 0.7818 Loss_G: 2.0154 D(x): 0.6162 D(G(z)): 0.0654 / 0.0645\n", + "[309/1600][0/8] Loss_D: 0.7343 Loss_G: 1.9022 D(x): 0.5970 D(G(z)): 0.0743 / 0.0749\n", + "[309/1600][2/8] Loss_D: 0.6761 Loss_G: 2.0286 D(x): 0.6169 D(G(z)): 0.0606 / 0.0620\n", + "[309/1600][4/8] Loss_D: 0.6460 Loss_G: 1.9683 D(x): 0.6760 D(G(z)): 0.0667 / 0.0674\n", + "[309/1600][6/8] Loss_D: 0.7436 Loss_G: 1.9095 D(x): 0.6395 D(G(z)): 0.0738 / 0.0731\n", + "[310/1600][0/8] Loss_D: 0.6956 Loss_G: 1.9538 D(x): 0.6349 D(G(z)): 0.0695 / 0.0702\n", + "[310/1600][2/8] Loss_D: 0.6729 Loss_G: 1.8934 D(x): 0.6884 D(G(z)): 0.0773 / 0.0773\n", + "[310/1600][4/8] Loss_D: 0.7398 Loss_G: 1.9484 D(x): 0.6496 D(G(z)): 0.0693 / 0.0679\n", + "[310/1600][6/8] Loss_D: 0.7582 Loss_G: 1.9339 D(x): 0.6207 D(G(z)): 0.0726 / 0.0713\n", + "[311/1600][0/8] Loss_D: 0.7220 Loss_G: 1.9635 D(x): 0.6338 D(G(z)): 0.0700 / 0.0690\n", + "[311/1600][2/8] Loss_D: 0.7532 Loss_G: 1.9584 D(x): 0.6011 D(G(z)): 0.0688 / 0.0685\n", + "[311/1600][4/8] Loss_D: 0.6579 Loss_G: 1.9991 D(x): 0.6614 D(G(z)): 0.0633 / 0.0644\n", + "[311/1600][6/8] Loss_D: 0.7341 Loss_G: 1.9232 D(x): 0.6454 D(G(z)): 0.0715 / 0.0713\n", + "[312/1600][0/8] Loss_D: 0.7702 Loss_G: 1.9940 D(x): 0.6346 D(G(z)): 0.0661 / 0.0642\n", + "[312/1600][2/8] Loss_D: 0.6942 Loss_G: 1.9459 D(x): 0.6067 D(G(z)): 0.0693 / 0.0694\n", + "[312/1600][4/8] Loss_D: 0.7077 Loss_G: 1.9743 D(x): 0.6705 D(G(z)): 0.0672 / 0.0673\n", + "[312/1600][6/8] Loss_D: 0.6922 Loss_G: 1.9977 D(x): 0.6404 D(G(z)): 0.0635 / 0.0641\n", + "[313/1600][0/8] Loss_D: 0.7513 Loss_G: 1.9128 D(x): 0.6456 D(G(z)): 0.0723 / 0.0725\n", + "[313/1600][2/8] Loss_D: 0.7830 Loss_G: 1.9126 D(x): 0.6474 D(G(z)): 0.0757 / 0.0735\n", + "[313/1600][4/8] Loss_D: 0.7461 Loss_G: 1.9475 D(x): 0.6082 D(G(z)): 0.0685 / 0.0682\n", + "[313/1600][6/8] Loss_D: 0.6974 Loss_G: 1.9512 D(x): 0.6269 D(G(z)): 0.0675 / 0.0689\n", + "[314/1600][0/8] Loss_D: 0.7762 Loss_G: 1.8798 D(x): 0.6907 D(G(z)): 0.0790 / 0.0773\n", + "[314/1600][2/8] Loss_D: 0.7187 Loss_G: 1.9390 D(x): 0.6459 D(G(z)): 0.0718 / 0.0703\n", + "[314/1600][4/8] Loss_D: 0.7761 Loss_G: 1.9713 D(x): 0.6305 D(G(z)): 0.0704 / 0.0678\n", + "[314/1600][6/8] Loss_D: 0.7017 Loss_G: 2.0467 D(x): 0.6358 D(G(z)): 0.0621 / 0.0625\n", + "[315/1600][0/8] Loss_D: 0.6688 Loss_G: 1.9488 D(x): 0.6570 D(G(z)): 0.0683 / 0.0697\n", + "[315/1600][2/8] Loss_D: 0.6944 Loss_G: 1.9958 D(x): 0.6642 D(G(z)): 0.0643 / 0.0646\n", + "[315/1600][4/8] Loss_D: 0.7152 Loss_G: 1.9614 D(x): 0.6354 D(G(z)): 0.0671 / 0.0669\n", + "[315/1600][6/8] Loss_D: 0.7067 Loss_G: 1.9787 D(x): 0.6511 D(G(z)): 0.0660 / 0.0652\n", + "[316/1600][0/8] Loss_D: 0.7073 Loss_G: 1.9411 D(x): 0.6268 D(G(z)): 0.0709 / 0.0719\n", + "[316/1600][2/8] Loss_D: 0.7114 Loss_G: 1.9234 D(x): 0.6587 D(G(z)): 0.0740 / 0.0732\n", + "[316/1600][4/8] Loss_D: 0.7469 Loss_G: 2.0184 D(x): 0.6489 D(G(z)): 0.0636 / 0.0625\n", + "[316/1600][6/8] Loss_D: 0.7589 Loss_G: 2.0763 D(x): 0.6327 D(G(z)): 0.0597 / 0.0582\n", + "[317/1600][0/8] Loss_D: 0.7360 Loss_G: 2.0315 D(x): 0.5896 D(G(z)): 0.0604 / 0.0606\n", + "[317/1600][2/8] Loss_D: 0.6684 Loss_G: 2.0160 D(x): 0.6356 D(G(z)): 0.0633 / 0.0643\n", + "[317/1600][4/8] Loss_D: 0.7021 Loss_G: 2.0117 D(x): 0.6153 D(G(z)): 0.0607 / 0.0617\n", + "[317/1600][6/8] Loss_D: 0.6910 Loss_G: 1.9838 D(x): 0.6218 D(G(z)): 0.0647 / 0.0657\n", + "[318/1600][0/8] Loss_D: 0.7201 Loss_G: 1.9983 D(x): 0.6506 D(G(z)): 0.0655 / 0.0652\n", + "[318/1600][2/8] Loss_D: 0.7194 Loss_G: 1.9685 D(x): 0.6208 D(G(z)): 0.0673 / 0.0681\n", + "[318/1600][4/8] Loss_D: 0.7778 Loss_G: 1.9774 D(x): 0.6404 D(G(z)): 0.0690 / 0.0674\n", + "[318/1600][6/8] Loss_D: 0.7665 Loss_G: 1.9788 D(x): 0.5972 D(G(z)): 0.0681 / 0.0673\n", + "[319/1600][0/8] Loss_D: 0.7006 Loss_G: 1.9532 D(x): 0.6736 D(G(z)): 0.0695 / 0.0689\n", + "[319/1600][2/8] Loss_D: 0.6795 Loss_G: 2.0169 D(x): 0.6401 D(G(z)): 0.0625 / 0.0625\n", + "[319/1600][4/8] Loss_D: 0.6852 Loss_G: 1.9618 D(x): 0.6243 D(G(z)): 0.0678 / 0.0679\n", + "[319/1600][6/8] Loss_D: 0.7415 Loss_G: 2.0121 D(x): 0.6097 D(G(z)): 0.0637 / 0.0637\n", + "[320/1600][0/8] Loss_D: 0.7552 Loss_G: 1.9794 D(x): 0.6187 D(G(z)): 0.0676 / 0.0675\n", + "[320/1600][2/8] Loss_D: 0.7327 Loss_G: 1.9549 D(x): 0.5844 D(G(z)): 0.0684 / 0.0698\n", + "[320/1600][4/8] Loss_D: 0.7079 Loss_G: 1.9407 D(x): 0.6608 D(G(z)): 0.0697 / 0.0700\n", + "[320/1600][6/8] Loss_D: 0.7325 Loss_G: 1.9525 D(x): 0.6439 D(G(z)): 0.0707 / 0.0695\n", + "[321/1600][0/8] Loss_D: 0.7542 Loss_G: 1.9715 D(x): 0.6531 D(G(z)): 0.0687 / 0.0672\n", + "[321/1600][2/8] Loss_D: 0.6809 Loss_G: 2.0495 D(x): 0.6200 D(G(z)): 0.0608 / 0.0612\n", + "[321/1600][4/8] Loss_D: 0.7491 Loss_G: 1.9694 D(x): 0.6325 D(G(z)): 0.0689 / 0.0684\n", + "[321/1600][6/8] Loss_D: 0.7678 Loss_G: 2.0055 D(x): 0.6646 D(G(z)): 0.0661 / 0.0646\n", + "[322/1600][0/8] Loss_D: 0.7404 Loss_G: 1.9856 D(x): 0.6002 D(G(z)): 0.0639 / 0.0648\n", + "[322/1600][2/8] Loss_D: 0.7198 Loss_G: 1.9757 D(x): 0.6327 D(G(z)): 0.0669 / 0.0675\n", + "[322/1600][4/8] Loss_D: 0.7437 Loss_G: 1.9635 D(x): 0.6374 D(G(z)): 0.0696 / 0.0688\n", + "[322/1600][6/8] Loss_D: 0.7162 Loss_G: 1.9522 D(x): 0.6294 D(G(z)): 0.0715 / 0.0716\n", + "[323/1600][0/8] Loss_D: 0.6814 Loss_G: 1.9871 D(x): 0.6304 D(G(z)): 0.0651 / 0.0657\n", + "[323/1600][2/8] Loss_D: 0.7778 Loss_G: 1.9461 D(x): 0.6825 D(G(z)): 0.0727 / 0.0709\n", + "[323/1600][4/8] Loss_D: 0.7194 Loss_G: 1.9490 D(x): 0.6181 D(G(z)): 0.0688 / 0.0686\n", + "[323/1600][6/8] Loss_D: 0.7803 Loss_G: 1.9160 D(x): 0.6229 D(G(z)): 0.0748 / 0.0729\n", + "[324/1600][0/8] Loss_D: 0.7538 Loss_G: 2.0477 D(x): 0.6515 D(G(z)): 0.0626 / 0.0606\n", + "[324/1600][2/8] Loss_D: 0.7721 Loss_G: 2.0642 D(x): 0.5841 D(G(z)): 0.0599 / 0.0586\n", + "[324/1600][4/8] Loss_D: 0.7048 Loss_G: 1.9880 D(x): 0.6032 D(G(z)): 0.0651 / 0.0661\n", + "[324/1600][6/8] Loss_D: 0.7407 Loss_G: 1.9873 D(x): 0.6416 D(G(z)): 0.0681 / 0.0682\n", + "[325/1600][0/8] Loss_D: 0.7018 Loss_G: 2.0871 D(x): 0.5991 D(G(z)): 0.0556 / 0.0562\n", + "[325/1600][2/8] Loss_D: 0.6581 Loss_G: 1.9500 D(x): 0.6717 D(G(z)): 0.0682 / 0.0692\n", + "[325/1600][4/8] Loss_D: 0.6778 Loss_G: 1.9669 D(x): 0.6713 D(G(z)): 0.0684 / 0.0683\n", + "[325/1600][6/8] Loss_D: 0.6763 Loss_G: 2.0176 D(x): 0.6473 D(G(z)): 0.0622 / 0.0624\n", + "[326/1600][0/8] Loss_D: 0.7401 Loss_G: 2.0080 D(x): 0.6693 D(G(z)): 0.0652 / 0.0641\n", + "[326/1600][2/8] Loss_D: 0.7204 Loss_G: 1.9834 D(x): 0.5740 D(G(z)): 0.0647 / 0.0667\n", + "[326/1600][4/8] Loss_D: 0.7143 Loss_G: 1.9326 D(x): 0.6172 D(G(z)): 0.0705 / 0.0724\n", + "[326/1600][6/8] Loss_D: 0.7666 Loss_G: 1.8584 D(x): 0.6383 D(G(z)): 0.0798 / 0.0795\n", + "[327/1600][0/8] Loss_D: 0.7379 Loss_G: 1.9255 D(x): 0.6494 D(G(z)): 0.0738 / 0.0723\n", + "[327/1600][2/8] Loss_D: 0.7563 Loss_G: 1.9747 D(x): 0.6357 D(G(z)): 0.0693 / 0.0676\n", + "[327/1600][4/8] Loss_D: 0.7491 Loss_G: 1.9433 D(x): 0.6170 D(G(z)): 0.0713 / 0.0701\n", + "[327/1600][6/8] Loss_D: 0.7716 Loss_G: 1.9608 D(x): 0.6814 D(G(z)): 0.0709 / 0.0689\n", + "[328/1600][0/8] Loss_D: 0.7043 Loss_G: 2.0002 D(x): 0.6271 D(G(z)): 0.0634 / 0.0637\n", + "[328/1600][2/8] Loss_D: 0.7067 Loss_G: 1.9383 D(x): 0.6524 D(G(z)): 0.0709 / 0.0708\n", + "[328/1600][4/8] Loss_D: 0.6594 Loss_G: 2.0053 D(x): 0.6397 D(G(z)): 0.0637 / 0.0641\n", + "[328/1600][6/8] Loss_D: 0.7284 Loss_G: 1.9817 D(x): 0.5828 D(G(z)): 0.0659 / 0.0673\n", + "[329/1600][0/8] Loss_D: 0.7371 Loss_G: 1.9152 D(x): 0.6524 D(G(z)): 0.0727 / 0.0723\n", + "[329/1600][2/8] Loss_D: 0.7034 Loss_G: 1.9661 D(x): 0.6319 D(G(z)): 0.0681 / 0.0680\n", + "[329/1600][4/8] Loss_D: 0.7848 Loss_G: 1.9388 D(x): 0.6714 D(G(z)): 0.0716 / 0.0696\n", + "[329/1600][6/8] Loss_D: 0.7672 Loss_G: 1.9840 D(x): 0.5959 D(G(z)): 0.0679 / 0.0666\n", + "[330/1600][0/8] Loss_D: 0.6738 Loss_G: 1.9099 D(x): 0.6376 D(G(z)): 0.0726 / 0.0739\n", + "[330/1600][2/8] Loss_D: 0.7152 Loss_G: 1.9811 D(x): 0.5946 D(G(z)): 0.0651 / 0.0662\n", + "[330/1600][4/8] Loss_D: 0.7474 Loss_G: 2.0128 D(x): 0.6619 D(G(z)): 0.0649 / 0.0637\n", + "[330/1600][6/8] Loss_D: 0.7725 Loss_G: 2.0078 D(x): 0.6233 D(G(z)): 0.0656 / 0.0641\n", + "[331/1600][0/8] Loss_D: 0.6635 Loss_G: 1.9042 D(x): 0.6363 D(G(z)): 0.0732 / 0.0748\n", + "[331/1600][2/8] Loss_D: 0.7373 Loss_G: 1.9775 D(x): 0.6122 D(G(z)): 0.0675 / 0.0673\n", + "[331/1600][4/8] Loss_D: 0.7855 Loss_G: 1.9985 D(x): 0.5883 D(G(z)): 0.0648 / 0.0647\n", + "[331/1600][6/8] Loss_D: 0.7947 Loss_G: 1.9893 D(x): 0.6227 D(G(z)): 0.0691 / 0.0673\n", + "[332/1600][0/8] Loss_D: 0.7416 Loss_G: 1.9854 D(x): 0.6020 D(G(z)): 0.0659 / 0.0658\n", + "[332/1600][2/8] Loss_D: 0.7016 Loss_G: 1.9666 D(x): 0.6288 D(G(z)): 0.0669 / 0.0673\n", + "[332/1600][4/8] Loss_D: 0.7414 Loss_G: 1.9094 D(x): 0.6639 D(G(z)): 0.0760 / 0.0755\n", + "[332/1600][6/8] Loss_D: 0.6779 Loss_G: 1.9768 D(x): 0.6500 D(G(z)): 0.0657 / 0.0661\n", + "[333/1600][0/8] Loss_D: 0.6755 Loss_G: 1.9354 D(x): 0.6458 D(G(z)): 0.0699 / 0.0703\n", + "[333/1600][2/8] Loss_D: 0.7645 Loss_G: 1.9612 D(x): 0.6043 D(G(z)): 0.0698 / 0.0693\n", + "[333/1600][4/8] Loss_D: 0.7680 Loss_G: 1.9494 D(x): 0.6437 D(G(z)): 0.0715 / 0.0697\n", + "[333/1600][6/8] Loss_D: 0.7765 Loss_G: 2.0264 D(x): 0.6549 D(G(z)): 0.0643 / 0.0623\n", + "[334/1600][0/8] Loss_D: 0.7366 Loss_G: 2.0339 D(x): 0.5879 D(G(z)): 0.0620 / 0.0615\n", + "[334/1600][2/8] Loss_D: 0.7743 Loss_G: 2.0233 D(x): 0.5997 D(G(z)): 0.0629 / 0.0617\n", + "[334/1600][4/8] Loss_D: 0.7486 Loss_G: 1.9923 D(x): 0.6551 D(G(z)): 0.0658 / 0.0654\n", + "[334/1600][6/8] Loss_D: 0.7020 Loss_G: 1.9207 D(x): 0.6385 D(G(z)): 0.0714 / 0.0730\n", + "[335/1600][0/8] Loss_D: 0.7120 Loss_G: 1.9817 D(x): 0.5941 D(G(z)): 0.0645 / 0.0663\n", + "[335/1600][2/8] Loss_D: 0.7308 Loss_G: 1.9533 D(x): 0.6396 D(G(z)): 0.0700 / 0.0692\n", + "[335/1600][4/8] Loss_D: 0.7461 Loss_G: 2.0344 D(x): 0.6503 D(G(z)): 0.0621 / 0.0615\n", + "[335/1600][6/8] Loss_D: 0.7665 Loss_G: 1.9790 D(x): 0.6207 D(G(z)): 0.0670 / 0.0660\n", + "[336/1600][0/8] Loss_D: 0.7127 Loss_G: 1.9767 D(x): 0.6511 D(G(z)): 0.0667 / 0.0666\n", + "[336/1600][2/8] Loss_D: 0.7242 Loss_G: 1.9796 D(x): 0.6334 D(G(z)): 0.0677 / 0.0673\n", + "[336/1600][4/8] Loss_D: 0.7017 Loss_G: 1.9676 D(x): 0.6113 D(G(z)): 0.0654 / 0.0664\n", + "[336/1600][6/8] Loss_D: 0.7534 Loss_G: 1.9273 D(x): 0.6020 D(G(z)): 0.0725 / 0.0726\n", + "[337/1600][0/8] Loss_D: 0.7254 Loss_G: 1.8694 D(x): 0.6717 D(G(z)): 0.0790 / 0.0782\n", + "[337/1600][2/8] Loss_D: 0.7751 Loss_G: 1.8587 D(x): 0.6366 D(G(z)): 0.0827 / 0.0813\n", + "[337/1600][4/8] Loss_D: 0.7104 Loss_G: 1.9346 D(x): 0.6339 D(G(z)): 0.0711 / 0.0710\n", + "[337/1600][6/8] Loss_D: 0.7258 Loss_G: 1.9887 D(x): 0.6128 D(G(z)): 0.0666 / 0.0667\n", + "[338/1600][0/8] Loss_D: 0.6806 Loss_G: 1.9216 D(x): 0.6549 D(G(z)): 0.0711 / 0.0710\n", + "[338/1600][2/8] Loss_D: 0.7891 Loss_G: 1.8736 D(x): 0.6035 D(G(z)): 0.0787 / 0.0775\n", + "[338/1600][4/8] Loss_D: 0.7475 Loss_G: 2.0000 D(x): 0.6521 D(G(z)): 0.0662 / 0.0645\n", + "[338/1600][6/8] Loss_D: 0.7723 Loss_G: 1.9370 D(x): 0.5730 D(G(z)): 0.0704 / 0.0706\n", + "[339/1600][0/8] Loss_D: 0.7493 Loss_G: 1.9186 D(x): 0.6318 D(G(z)): 0.0714 / 0.0714\n", + "[339/1600][2/8] Loss_D: 0.7382 Loss_G: 1.8779 D(x): 0.5946 D(G(z)): 0.0771 / 0.0782\n", + "[339/1600][4/8] Loss_D: 0.7536 Loss_G: 1.8778 D(x): 0.6304 D(G(z)): 0.0795 / 0.0778\n", + "[339/1600][6/8] Loss_D: 0.7643 Loss_G: 1.9553 D(x): 0.6424 D(G(z)): 0.0716 / 0.0694\n", + "[340/1600][0/8] Loss_D: 0.7493 Loss_G: 1.9276 D(x): 0.6061 D(G(z)): 0.0707 / 0.0709\n", + "[340/1600][2/8] Loss_D: 0.7191 Loss_G: 1.9089 D(x): 0.6123 D(G(z)): 0.0723 / 0.0736\n", + "[340/1600][4/8] Loss_D: 0.7152 Loss_G: 1.8852 D(x): 0.6398 D(G(z)): 0.0755 / 0.0759\n", + "[340/1600][6/8] Loss_D: 0.7736 Loss_G: 1.9138 D(x): 0.6290 D(G(z)): 0.0737 / 0.0726\n", + "[341/1600][0/8] Loss_D: 0.7364 Loss_G: 1.9804 D(x): 0.6150 D(G(z)): 0.0670 / 0.0655\n", + "[341/1600][2/8] Loss_D: 0.7877 Loss_G: 1.9127 D(x): 0.6392 D(G(z)): 0.0773 / 0.0744\n", + "[341/1600][4/8] Loss_D: 0.7254 Loss_G: 2.0131 D(x): 0.6049 D(G(z)): 0.0632 / 0.0628\n", + "[341/1600][6/8] Loss_D: 0.7627 Loss_G: 2.0321 D(x): 0.6100 D(G(z)): 0.0624 / 0.0614\n", + "[342/1600][0/8] Loss_D: 0.7412 Loss_G: 1.9813 D(x): 0.6220 D(G(z)): 0.0666 / 0.0666\n", + "[342/1600][2/8] Loss_D: 0.6829 Loss_G: 1.9806 D(x): 0.6417 D(G(z)): 0.0635 / 0.0650\n", + "[342/1600][4/8] Loss_D: 0.7568 Loss_G: 2.0057 D(x): 0.6170 D(G(z)): 0.0663 / 0.0654\n", + "[342/1600][6/8] Loss_D: 0.7083 Loss_G: 1.9250 D(x): 0.6000 D(G(z)): 0.0694 / 0.0712\n", + "[343/1600][0/8] Loss_D: 0.7878 Loss_G: 1.8801 D(x): 0.5977 D(G(z)): 0.0765 / 0.0759\n", + "[343/1600][2/8] Loss_D: 0.7550 Loss_G: 1.9022 D(x): 0.6412 D(G(z)): 0.0771 / 0.0751\n", + "[343/1600][4/8] Loss_D: 0.7119 Loss_G: 1.9633 D(x): 0.6004 D(G(z)): 0.0673 / 0.0684\n", + "[343/1600][6/8] Loss_D: 0.6984 Loss_G: 1.9797 D(x): 0.6380 D(G(z)): 0.0668 / 0.0670\n", + "[344/1600][0/8] Loss_D: 0.7180 Loss_G: 1.9442 D(x): 0.6405 D(G(z)): 0.0704 / 0.0697\n", + "[344/1600][2/8] Loss_D: 0.7337 Loss_G: 1.9110 D(x): 0.6141 D(G(z)): 0.0738 / 0.0732\n", + "[344/1600][4/8] Loss_D: 0.7100 Loss_G: 2.0041 D(x): 0.6316 D(G(z)): 0.0642 / 0.0639\n", + "[344/1600][6/8] Loss_D: 0.7372 Loss_G: 1.9482 D(x): 0.6350 D(G(z)): 0.0694 / 0.0693\n", + "[345/1600][0/8] Loss_D: 0.7687 Loss_G: 1.9419 D(x): 0.6185 D(G(z)): 0.0707 / 0.0696\n", + "[345/1600][2/8] Loss_D: 0.7587 Loss_G: 1.9886 D(x): 0.6229 D(G(z)): 0.0679 / 0.0669\n", + "[345/1600][4/8] Loss_D: 0.7015 Loss_G: 1.9997 D(x): 0.6141 D(G(z)): 0.0637 / 0.0644\n", + "[345/1600][6/8] Loss_D: 0.7005 Loss_G: 1.9627 D(x): 0.6321 D(G(z)): 0.0691 / 0.0692\n", + "[346/1600][0/8] Loss_D: 0.6964 Loss_G: 1.9635 D(x): 0.6105 D(G(z)): 0.0679 / 0.0686\n", + "[346/1600][2/8] Loss_D: 0.7376 Loss_G: 1.8910 D(x): 0.6167 D(G(z)): 0.0759 / 0.0763\n", + "[346/1600][4/8] Loss_D: 0.7556 Loss_G: 1.9300 D(x): 0.6108 D(G(z)): 0.0727 / 0.0728\n", + "[346/1600][6/8] Loss_D: 0.7275 Loss_G: 1.8352 D(x): 0.6256 D(G(z)): 0.0824 / 0.0827\n", + "[347/1600][0/8] Loss_D: 0.7842 Loss_G: 1.9236 D(x): 0.6484 D(G(z)): 0.0759 / 0.0733\n", + "[347/1600][2/8] Loss_D: 0.7825 Loss_G: 1.9437 D(x): 0.5941 D(G(z)): 0.0731 / 0.0715\n", + "[347/1600][4/8] Loss_D: 0.6994 Loss_G: 1.9782 D(x): 0.6510 D(G(z)): 0.0667 / 0.0670\n", + "[347/1600][6/8] Loss_D: 0.7661 Loss_G: 1.9541 D(x): 0.6165 D(G(z)): 0.0717 / 0.0704\n", + "[348/1600][0/8] Loss_D: 0.6932 Loss_G: 1.9584 D(x): 0.6115 D(G(z)): 0.0683 / 0.0689\n", + "[348/1600][2/8] Loss_D: 0.7791 Loss_G: 1.9027 D(x): 0.6241 D(G(z)): 0.0755 / 0.0748\n", + "[348/1600][4/8] Loss_D: 0.7597 Loss_G: 1.9771 D(x): 0.6064 D(G(z)): 0.0691 / 0.0683\n", + "[348/1600][6/8] Loss_D: 0.6859 Loss_G: 1.9971 D(x): 0.6104 D(G(z)): 0.0660 / 0.0668\n", + "[349/1600][0/8] Loss_D: 0.6827 Loss_G: 1.9467 D(x): 0.6518 D(G(z)): 0.0681 / 0.0695\n", + "[349/1600][2/8] Loss_D: 0.7030 Loss_G: 1.8832 D(x): 0.6225 D(G(z)): 0.0754 / 0.0769\n", + "[349/1600][4/8] Loss_D: 0.7627 Loss_G: 1.7825 D(x): 0.5871 D(G(z)): 0.0872 / 0.0884\n", + "[349/1600][6/8] Loss_D: 0.7906 Loss_G: 1.9010 D(x): 0.6199 D(G(z)): 0.0763 / 0.0748\n", + "[350/1600][0/8] Loss_D: 0.7432 Loss_G: 1.8832 D(x): 0.6868 D(G(z)): 0.0791 / 0.0774\n", + "[350/1600][2/8] Loss_D: 0.6675 Loss_G: 1.9431 D(x): 0.6537 D(G(z)): 0.0698 / 0.0698\n", + "[350/1600][4/8] Loss_D: 0.7830 Loss_G: 1.9441 D(x): 0.6265 D(G(z)): 0.0727 / 0.0702\n", + "[350/1600][6/8] Loss_D: 0.7625 Loss_G: 1.9119 D(x): 0.6182 D(G(z)): 0.0751 / 0.0737\n", + "[351/1600][0/8] Loss_D: 0.7514 Loss_G: 1.9858 D(x): 0.6226 D(G(z)): 0.0659 / 0.0651\n", + "[351/1600][2/8] Loss_D: 0.7782 Loss_G: 2.0513 D(x): 0.6106 D(G(z)): 0.0613 / 0.0595\n", + "[351/1600][4/8] Loss_D: 0.7382 Loss_G: 2.0326 D(x): 0.6088 D(G(z)): 0.0605 / 0.0610\n", + "[351/1600][6/8] Loss_D: 0.6672 Loss_G: 2.0283 D(x): 0.6674 D(G(z)): 0.0617 / 0.0615\n", + "[352/1600][0/8] Loss_D: 0.7052 Loss_G: 1.9712 D(x): 0.6040 D(G(z)): 0.0690 / 0.0703\n", + "[352/1600][2/8] Loss_D: 0.7765 Loss_G: 2.0030 D(x): 0.6390 D(G(z)): 0.0657 / 0.0646\n", + "[352/1600][4/8] Loss_D: 0.7783 Loss_G: 2.0158 D(x): 0.6209 D(G(z)): 0.0646 / 0.0632\n", + "[352/1600][6/8] Loss_D: 0.7338 Loss_G: 1.9620 D(x): 0.6098 D(G(z)): 0.0673 / 0.0670\n", + "[353/1600][0/8] Loss_D: 0.7727 Loss_G: 1.9576 D(x): 0.6028 D(G(z)): 0.0684 / 0.0680\n", + "[353/1600][2/8] Loss_D: 0.7310 Loss_G: 2.0348 D(x): 0.5868 D(G(z)): 0.0608 / 0.0612\n", + "[353/1600][4/8] Loss_D: 0.7249 Loss_G: 1.9774 D(x): 0.6205 D(G(z)): 0.0682 / 0.0677\n", + "[353/1600][6/8] Loss_D: 0.7228 Loss_G: 1.9902 D(x): 0.6125 D(G(z)): 0.0665 / 0.0666\n", + "[354/1600][0/8] Loss_D: 0.7252 Loss_G: 2.0166 D(x): 0.6075 D(G(z)): 0.0643 / 0.0642\n", + "[354/1600][2/8] Loss_D: 0.7716 Loss_G: 1.9096 D(x): 0.6023 D(G(z)): 0.0737 / 0.0733\n", + "[354/1600][4/8] Loss_D: 0.7257 Loss_G: 1.9814 D(x): 0.5903 D(G(z)): 0.0665 / 0.0672\n", + "[354/1600][6/8] Loss_D: 0.7660 Loss_G: 1.9490 D(x): 0.6364 D(G(z)): 0.0704 / 0.0701\n", + "[355/1600][0/8] Loss_D: 0.7047 Loss_G: 1.9655 D(x): 0.6391 D(G(z)): 0.0680 / 0.0684\n", + "[355/1600][2/8] Loss_D: 0.7826 Loss_G: 1.9773 D(x): 0.5936 D(G(z)): 0.0662 / 0.0654\n", + "[355/1600][4/8] Loss_D: 0.6975 Loss_G: 2.0007 D(x): 0.6533 D(G(z)): 0.0630 / 0.0630\n", + "[355/1600][6/8] Loss_D: 0.7442 Loss_G: 1.8628 D(x): 0.6118 D(G(z)): 0.0782 / 0.0787\n", + "[356/1600][0/8] Loss_D: 0.7671 Loss_G: 1.9269 D(x): 0.6470 D(G(z)): 0.0738 / 0.0724\n", + "[356/1600][2/8] Loss_D: 0.7804 Loss_G: 1.8832 D(x): 0.5993 D(G(z)): 0.0788 / 0.0776\n", + "[356/1600][4/8] Loss_D: 0.7853 Loss_G: 1.9493 D(x): 0.6478 D(G(z)): 0.0736 / 0.0712\n", + "[356/1600][6/8] Loss_D: 0.7368 Loss_G: 1.9005 D(x): 0.5922 D(G(z)): 0.0754 / 0.0749\n", + "[357/1600][0/8] Loss_D: 0.7540 Loss_G: 1.9535 D(x): 0.5901 D(G(z)): 0.0682 / 0.0686\n", + "[357/1600][2/8] Loss_D: 0.7579 Loss_G: 1.8877 D(x): 0.6437 D(G(z)): 0.0773 / 0.0765\n", + "[357/1600][4/8] Loss_D: 0.6608 Loss_G: 1.9597 D(x): 0.6556 D(G(z)): 0.0673 / 0.0676\n", + "[357/1600][6/8] Loss_D: 0.7170 Loss_G: 1.8562 D(x): 0.6526 D(G(z)): 0.0788 / 0.0792\n", + "[358/1600][0/8] Loss_D: 0.7387 Loss_G: 1.9360 D(x): 0.6239 D(G(z)): 0.0725 / 0.0716\n", + "[358/1600][2/8] Loss_D: 0.7641 Loss_G: 2.0451 D(x): 0.6363 D(G(z)): 0.0616 / 0.0603\n", + "[358/1600][4/8] Loss_D: 0.7842 Loss_G: 1.9597 D(x): 0.6576 D(G(z)): 0.0715 / 0.0684\n", + "[358/1600][6/8] Loss_D: 0.7674 Loss_G: 1.9727 D(x): 0.6027 D(G(z)): 0.0695 / 0.0683\n", + "[359/1600][0/8] Loss_D: 0.7660 Loss_G: 1.9953 D(x): 0.6196 D(G(z)): 0.0683 / 0.0666\n", + "[359/1600][2/8] Loss_D: 0.7397 Loss_G: 1.9736 D(x): 0.5867 D(G(z)): 0.0658 / 0.0672\n", + "[359/1600][4/8] Loss_D: 0.7280 Loss_G: 2.0023 D(x): 0.5965 D(G(z)): 0.0619 / 0.0632\n", + "[359/1600][6/8] Loss_D: 0.6896 Loss_G: 1.9187 D(x): 0.6300 D(G(z)): 0.0720 / 0.0724\n", + "[360/1600][0/8] Loss_D: 0.7720 Loss_G: 1.9132 D(x): 0.6319 D(G(z)): 0.0749 / 0.0740\n", + "[360/1600][2/8] Loss_D: 0.6602 Loss_G: 1.9700 D(x): 0.6447 D(G(z)): 0.0661 / 0.0670\n", + "[360/1600][4/8] Loss_D: 0.7850 Loss_G: 1.8954 D(x): 0.6269 D(G(z)): 0.0770 / 0.0749\n", + "[360/1600][6/8] Loss_D: 0.7084 Loss_G: 1.9481 D(x): 0.6343 D(G(z)): 0.0689 / 0.0696\n", + "[361/1600][0/8] Loss_D: 0.6975 Loss_G: 1.9674 D(x): 0.6295 D(G(z)): 0.0652 / 0.0658\n", + "[361/1600][2/8] Loss_D: 0.6794 Loss_G: 1.8950 D(x): 0.6518 D(G(z)): 0.0744 / 0.0758\n", + "[361/1600][4/8] Loss_D: 0.7715 Loss_G: 1.8642 D(x): 0.6357 D(G(z)): 0.0800 / 0.0781\n", + "[361/1600][6/8] Loss_D: 0.7646 Loss_G: 1.9244 D(x): 0.6522 D(G(z)): 0.0740 / 0.0719\n", + "[362/1600][0/8] Loss_D: 0.7294 Loss_G: 1.9685 D(x): 0.6097 D(G(z)): 0.0693 / 0.0686\n", + "[362/1600][2/8] Loss_D: 0.6980 Loss_G: 1.9443 D(x): 0.6544 D(G(z)): 0.0694 / 0.0691\n", + "[362/1600][4/8] Loss_D: 0.7863 Loss_G: 1.9343 D(x): 0.6299 D(G(z)): 0.0718 / 0.0698\n", + "[362/1600][6/8] Loss_D: 0.6456 Loss_G: 2.0208 D(x): 0.6617 D(G(z)): 0.0623 / 0.0626\n", + "[363/1600][0/8] Loss_D: 0.6482 Loss_G: 2.0109 D(x): 0.6550 D(G(z)): 0.0620 / 0.0625\n", + "[363/1600][2/8] Loss_D: 0.7688 Loss_G: 2.0050 D(x): 0.6770 D(G(z)): 0.0650 / 0.0633\n", + "[363/1600][4/8] Loss_D: 0.7642 Loss_G: 1.8689 D(x): 0.6020 D(G(z)): 0.0781 / 0.0781\n", + "[363/1600][6/8] Loss_D: 0.7565 Loss_G: 1.9385 D(x): 0.6171 D(G(z)): 0.0724 / 0.0712\n", + "[364/1600][0/8] Loss_D: 0.7061 Loss_G: 1.9883 D(x): 0.6376 D(G(z)): 0.0657 / 0.0656\n", + "[364/1600][2/8] Loss_D: 0.7090 Loss_G: 1.9328 D(x): 0.6189 D(G(z)): 0.0711 / 0.0714\n", + "[364/1600][4/8] Loss_D: 0.7769 Loss_G: 1.9702 D(x): 0.5955 D(G(z)): 0.0689 / 0.0674\n", + "[364/1600][6/8] Loss_D: 0.6860 Loss_G: 1.9590 D(x): 0.6418 D(G(z)): 0.0673 / 0.0681\n", + "[365/1600][0/8] Loss_D: 0.7673 Loss_G: 1.9535 D(x): 0.6304 D(G(z)): 0.0690 / 0.0682\n", + "[365/1600][2/8] Loss_D: 0.7548 Loss_G: 1.9494 D(x): 0.6117 D(G(z)): 0.0700 / 0.0693\n", + "[365/1600][4/8] Loss_D: 0.7081 Loss_G: 1.9636 D(x): 0.6230 D(G(z)): 0.0669 / 0.0684\n", + "[365/1600][6/8] Loss_D: 0.6875 Loss_G: 1.9451 D(x): 0.6236 D(G(z)): 0.0685 / 0.0703\n", + "[366/1600][0/8] Loss_D: 0.7409 Loss_G: 1.9092 D(x): 0.6481 D(G(z)): 0.0732 / 0.0730\n", + "[366/1600][2/8] Loss_D: 0.6838 Loss_G: 1.8824 D(x): 0.6494 D(G(z)): 0.0771 / 0.0762\n", + "[366/1600][4/8] Loss_D: 0.7682 Loss_G: 1.8887 D(x): 0.6250 D(G(z)): 0.0791 / 0.0772\n", + "[366/1600][6/8] Loss_D: 0.6894 Loss_G: 1.8849 D(x): 0.6366 D(G(z)): 0.0762 / 0.0759\n", + "[367/1600][0/8] Loss_D: 0.7738 Loss_G: 1.9788 D(x): 0.6415 D(G(z)): 0.0681 / 0.0660\n", + "[367/1600][2/8] Loss_D: 0.7396 Loss_G: 2.0380 D(x): 0.6287 D(G(z)): 0.0634 / 0.0617\n", + "[367/1600][4/8] Loss_D: 0.7107 Loss_G: 2.0667 D(x): 0.6053 D(G(z)): 0.0581 / 0.0582\n", + "[367/1600][6/8] Loss_D: 0.6911 Loss_G: 2.0026 D(x): 0.6341 D(G(z)): 0.0633 / 0.0646\n", + "[368/1600][0/8] Loss_D: 0.7062 Loss_G: 1.9918 D(x): 0.6516 D(G(z)): 0.0657 / 0.0651\n", + "[368/1600][2/8] Loss_D: 0.6654 Loss_G: 1.9660 D(x): 0.6624 D(G(z)): 0.0686 / 0.0689\n", + "[368/1600][4/8] Loss_D: 0.7667 Loss_G: 2.0053 D(x): 0.6085 D(G(z)): 0.0658 / 0.0654\n", + "[368/1600][6/8] Loss_D: 0.7284 Loss_G: 2.0127 D(x): 0.6095 D(G(z)): 0.0637 / 0.0635\n", + "[369/1600][0/8] Loss_D: 0.6769 Loss_G: 2.0032 D(x): 0.6573 D(G(z)): 0.0631 / 0.0638\n", + "[369/1600][2/8] Loss_D: 0.7272 Loss_G: 2.0091 D(x): 0.6177 D(G(z)): 0.0624 / 0.0628\n", + "[369/1600][4/8] Loss_D: 0.6820 Loss_G: 2.0076 D(x): 0.6187 D(G(z)): 0.0623 / 0.0636\n", + "[369/1600][6/8] Loss_D: 0.7149 Loss_G: 1.9325 D(x): 0.6412 D(G(z)): 0.0704 / 0.0712\n", + "[370/1600][0/8] Loss_D: 0.7823 Loss_G: 1.9479 D(x): 0.6463 D(G(z)): 0.0723 / 0.0694\n", + "[370/1600][2/8] Loss_D: 0.7602 Loss_G: 1.9941 D(x): 0.6641 D(G(z)): 0.0674 / 0.0655\n", + "[370/1600][4/8] Loss_D: 0.6905 Loss_G: 1.9486 D(x): 0.6634 D(G(z)): 0.0716 / 0.0710\n", + "[370/1600][6/8] Loss_D: 0.6426 Loss_G: 1.9502 D(x): 0.6667 D(G(z)): 0.0697 / 0.0703\n", + "[371/1600][0/8] Loss_D: 0.7821 Loss_G: 1.9398 D(x): 0.6525 D(G(z)): 0.0722 / 0.0702\n", + "[371/1600][2/8] Loss_D: 0.7675 Loss_G: 1.9652 D(x): 0.6314 D(G(z)): 0.0696 / 0.0675\n", + "[371/1600][4/8] Loss_D: 0.7132 Loss_G: 1.9896 D(x): 0.6292 D(G(z)): 0.0643 / 0.0645\n", + "[371/1600][6/8] Loss_D: 0.7451 Loss_G: 2.0577 D(x): 0.6297 D(G(z)): 0.0601 / 0.0590\n", + "[372/1600][0/8] Loss_D: 0.7234 Loss_G: 1.9675 D(x): 0.6099 D(G(z)): 0.0660 / 0.0664\n", + "[372/1600][2/8] Loss_D: 0.7373 Loss_G: 2.0450 D(x): 0.6138 D(G(z)): 0.0610 / 0.0608\n", + "[372/1600][4/8] Loss_D: 0.6669 Loss_G: 2.1103 D(x): 0.6277 D(G(z)): 0.0528 / 0.0538\n", + "[372/1600][6/8] Loss_D: 0.7147 Loss_G: 2.0323 D(x): 0.6268 D(G(z)): 0.0603 / 0.0609\n", + "[373/1600][0/8] Loss_D: 0.7212 Loss_G: 1.9992 D(x): 0.5835 D(G(z)): 0.0637 / 0.0646\n", + "[373/1600][2/8] Loss_D: 0.7537 Loss_G: 1.9579 D(x): 0.6390 D(G(z)): 0.0703 / 0.0685\n", + "[373/1600][4/8] Loss_D: 0.7478 Loss_G: 1.9395 D(x): 0.6157 D(G(z)): 0.0693 / 0.0690\n", + "[373/1600][6/8] Loss_D: 0.7323 Loss_G: 2.0004 D(x): 0.6168 D(G(z)): 0.0650 / 0.0647\n", + "[374/1600][0/8] Loss_D: 0.7084 Loss_G: 1.9499 D(x): 0.5968 D(G(z)): 0.0669 / 0.0694\n", + "[374/1600][2/8] Loss_D: 0.6853 Loss_G: 1.9191 D(x): 0.6615 D(G(z)): 0.0706 / 0.0716\n", + "[374/1600][4/8] Loss_D: 0.7914 Loss_G: 1.9074 D(x): 0.6179 D(G(z)): 0.0762 / 0.0737\n", + "[374/1600][6/8] Loss_D: 0.6992 Loss_G: 2.0241 D(x): 0.6374 D(G(z)): 0.0609 / 0.0608\n", + "[375/1600][0/8] Loss_D: 0.7838 Loss_G: 1.9410 D(x): 0.6780 D(G(z)): 0.0728 / 0.0713\n", + "[375/1600][2/8] Loss_D: 0.7087 Loss_G: 1.9924 D(x): 0.6279 D(G(z)): 0.0665 / 0.0662\n", + "[375/1600][4/8] Loss_D: 0.7529 Loss_G: 1.9361 D(x): 0.6096 D(G(z)): 0.0729 / 0.0715\n", + "[375/1600][6/8] Loss_D: 0.7240 Loss_G: 2.0427 D(x): 0.6104 D(G(z)): 0.0610 / 0.0605\n", + "[376/1600][0/8] Loss_D: 0.7146 Loss_G: 2.0745 D(x): 0.6043 D(G(z)): 0.0568 / 0.0576\n", + "[376/1600][2/8] Loss_D: 0.7720 Loss_G: 1.9895 D(x): 0.6256 D(G(z)): 0.0666 / 0.0661\n", + "[376/1600][4/8] Loss_D: 0.7276 Loss_G: 1.9898 D(x): 0.6046 D(G(z)): 0.0649 / 0.0652\n", + "[376/1600][6/8] Loss_D: 0.6856 Loss_G: 1.9770 D(x): 0.6179 D(G(z)): 0.0641 / 0.0661\n", + "[377/1600][0/8] Loss_D: 0.7780 Loss_G: 1.9375 D(x): 0.6826 D(G(z)): 0.0718 / 0.0694\n", + "[377/1600][2/8] Loss_D: 0.7389 Loss_G: 1.9338 D(x): 0.6419 D(G(z)): 0.0721 / 0.0704\n", + "[377/1600][4/8] Loss_D: 0.7741 Loss_G: 2.0565 D(x): 0.5728 D(G(z)): 0.0595 / 0.0591\n", + "[377/1600][6/8] Loss_D: 0.6845 Loss_G: 1.9861 D(x): 0.6670 D(G(z)): 0.0646 / 0.0650\n", + "[378/1600][0/8] Loss_D: 0.6898 Loss_G: 1.9757 D(x): 0.6390 D(G(z)): 0.0649 / 0.0659\n", + "[378/1600][2/8] Loss_D: 0.7096 Loss_G: 1.9282 D(x): 0.6346 D(G(z)): 0.0706 / 0.0714\n", + "[378/1600][4/8] Loss_D: 0.7029 Loss_G: 1.9578 D(x): 0.6568 D(G(z)): 0.0690 / 0.0679\n", + "[378/1600][6/8] Loss_D: 0.7269 Loss_G: 1.9384 D(x): 0.6150 D(G(z)): 0.0706 / 0.0703\n", + "[379/1600][0/8] Loss_D: 0.6835 Loss_G: 1.9692 D(x): 0.6331 D(G(z)): 0.0670 / 0.0668\n", + "[379/1600][2/8] Loss_D: 0.7354 Loss_G: 1.9541 D(x): 0.6191 D(G(z)): 0.0684 / 0.0681\n", + "[379/1600][4/8] Loss_D: 0.7407 Loss_G: 1.9316 D(x): 0.6365 D(G(z)): 0.0722 / 0.0720\n", + "[379/1600][6/8] Loss_D: 0.7408 Loss_G: 1.9295 D(x): 0.6773 D(G(z)): 0.0708 / 0.0698\n", + "[380/1600][0/8] Loss_D: 0.7544 Loss_G: 1.9923 D(x): 0.6562 D(G(z)): 0.0674 / 0.0658\n", + "[380/1600][2/8] Loss_D: 0.7731 Loss_G: 1.9959 D(x): 0.6609 D(G(z)): 0.0674 / 0.0649\n", + "[380/1600][4/8] Loss_D: 0.7644 Loss_G: 2.0283 D(x): 0.6052 D(G(z)): 0.0615 / 0.0610\n", + "[380/1600][6/8] Loss_D: 0.7394 Loss_G: 2.0468 D(x): 0.6277 D(G(z)): 0.0598 / 0.0586\n", + "[381/1600][0/8] Loss_D: 0.7363 Loss_G: 1.9574 D(x): 0.6355 D(G(z)): 0.0686 / 0.0682\n", + "[381/1600][2/8] Loss_D: 0.7590 Loss_G: 2.0504 D(x): 0.6511 D(G(z)): 0.0617 / 0.0605\n", + "[381/1600][4/8] Loss_D: 0.6908 Loss_G: 2.0125 D(x): 0.6308 D(G(z)): 0.0617 / 0.0623\n", + "[381/1600][6/8] Loss_D: 0.7714 Loss_G: 1.9544 D(x): 0.5839 D(G(z)): 0.0690 / 0.0689\n", + "[382/1600][0/8] Loss_D: 0.7790 Loss_G: 2.0010 D(x): 0.5983 D(G(z)): 0.0659 / 0.0646\n", + "[382/1600][2/8] Loss_D: 0.7346 Loss_G: 2.0388 D(x): 0.6329 D(G(z)): 0.0613 / 0.0604\n", + "[382/1600][4/8] Loss_D: 0.7649 Loss_G: 2.0650 D(x): 0.6237 D(G(z)): 0.0602 / 0.0584\n", + "[382/1600][6/8] Loss_D: 0.7547 Loss_G: 2.0437 D(x): 0.6325 D(G(z)): 0.0619 / 0.0606\n", + "[383/1600][0/8] Loss_D: 0.7231 Loss_G: 2.0308 D(x): 0.5645 D(G(z)): 0.0585 / 0.0612\n", + "[383/1600][2/8] Loss_D: 0.7067 Loss_G: 1.9791 D(x): 0.6284 D(G(z)): 0.0662 / 0.0668\n", + "[383/1600][4/8] Loss_D: 0.7855 Loss_G: 1.9782 D(x): 0.6386 D(G(z)): 0.0687 / 0.0664\n", + "[383/1600][6/8] Loss_D: 0.7494 Loss_G: 2.0452 D(x): 0.5571 D(G(z)): 0.0591 / 0.0599\n", + "[384/1600][0/8] Loss_D: 0.7631 Loss_G: 2.0354 D(x): 0.6553 D(G(z)): 0.0622 / 0.0609\n", + "[384/1600][2/8] Loss_D: 0.7399 Loss_G: 1.9716 D(x): 0.5990 D(G(z)): 0.0662 / 0.0667\n", + "[384/1600][4/8] Loss_D: 0.7365 Loss_G: 2.0294 D(x): 0.6161 D(G(z)): 0.0617 / 0.0611\n", + "[384/1600][6/8] Loss_D: 0.7218 Loss_G: 2.0489 D(x): 0.6129 D(G(z)): 0.0590 / 0.0595\n", + "[385/1600][0/8] Loss_D: 0.6936 Loss_G: 1.9914 D(x): 0.6307 D(G(z)): 0.0646 / 0.0648\n", + "[385/1600][2/8] Loss_D: 0.6924 Loss_G: 2.1027 D(x): 0.6400 D(G(z)): 0.0554 / 0.0559\n", + "[385/1600][4/8] Loss_D: 0.7749 Loss_G: 2.0123 D(x): 0.5740 D(G(z)): 0.0637 / 0.0634\n", + "[385/1600][6/8] Loss_D: 0.6987 Loss_G: 2.0131 D(x): 0.6246 D(G(z)): 0.0625 / 0.0626\n", + "[386/1600][0/8] Loss_D: 0.7695 Loss_G: 1.9490 D(x): 0.5968 D(G(z)): 0.0703 / 0.0689\n", + "[386/1600][2/8] Loss_D: 0.7531 Loss_G: 2.0166 D(x): 0.6236 D(G(z)): 0.0637 / 0.0622\n", + "[386/1600][4/8] Loss_D: 0.7651 Loss_G: 2.1463 D(x): 0.6300 D(G(z)): 0.0531 / 0.0517\n", + "[386/1600][6/8] Loss_D: 0.7648 Loss_G: 2.1030 D(x): 0.6287 D(G(z)): 0.0568 / 0.0550\n", + "[387/1600][0/8] Loss_D: 0.7414 Loss_G: 2.0632 D(x): 0.5740 D(G(z)): 0.0578 / 0.0587\n", + "[387/1600][2/8] Loss_D: 0.7117 Loss_G: 2.0884 D(x): 0.5977 D(G(z)): 0.0560 / 0.0569\n", + "[387/1600][4/8] Loss_D: 0.7729 Loss_G: 2.1475 D(x): 0.5933 D(G(z)): 0.0536 / 0.0526\n", + "[387/1600][6/8] Loss_D: 0.7605 Loss_G: 2.0755 D(x): 0.6100 D(G(z)): 0.0574 / 0.0570\n", + "[388/1600][0/8] Loss_D: 0.7165 Loss_G: 1.9892 D(x): 0.6048 D(G(z)): 0.0635 / 0.0657\n", + "[388/1600][2/8] Loss_D: 0.7470 Loss_G: 2.0052 D(x): 0.6238 D(G(z)): 0.0635 / 0.0636\n", + "[388/1600][4/8] Loss_D: 0.7204 Loss_G: 1.9746 D(x): 0.6070 D(G(z)): 0.0669 / 0.0676\n", + "[388/1600][6/8] Loss_D: 0.7736 Loss_G: 1.9649 D(x): 0.6358 D(G(z)): 0.0669 / 0.0664\n", + "[389/1600][0/8] Loss_D: 0.7478 Loss_G: 1.9527 D(x): 0.6287 D(G(z)): 0.0702 / 0.0690\n", + "[389/1600][2/8] Loss_D: 0.7309 Loss_G: 2.0407 D(x): 0.6291 D(G(z)): 0.0609 / 0.0602\n", + "[389/1600][4/8] Loss_D: 0.7604 Loss_G: 2.0254 D(x): 0.6229 D(G(z)): 0.0623 / 0.0613\n", + "[389/1600][6/8] Loss_D: 0.7061 Loss_G: 2.0149 D(x): 0.6107 D(G(z)): 0.0619 / 0.0627\n", + "[390/1600][0/8] Loss_D: 0.7653 Loss_G: 2.0439 D(x): 0.6133 D(G(z)): 0.0625 / 0.0610\n", + "[390/1600][2/8] Loss_D: 0.6747 Loss_G: 2.1224 D(x): 0.6317 D(G(z)): 0.0534 / 0.0534\n", + "[390/1600][4/8] Loss_D: 0.7243 Loss_G: 2.0177 D(x): 0.6181 D(G(z)): 0.0615 / 0.0618\n", + "[390/1600][6/8] Loss_D: 0.7189 Loss_G: 1.9944 D(x): 0.6155 D(G(z)): 0.0629 / 0.0635\n", + "[391/1600][0/8] Loss_D: 0.6813 Loss_G: 1.9200 D(x): 0.6315 D(G(z)): 0.0699 / 0.0719\n", + "[391/1600][2/8] Loss_D: 0.6827 Loss_G: 1.9028 D(x): 0.6717 D(G(z)): 0.0747 / 0.0748\n", + "[391/1600][4/8] Loss_D: 0.6806 Loss_G: 1.9832 D(x): 0.6780 D(G(z)): 0.0686 / 0.0677\n", + "[391/1600][6/8] Loss_D: 0.7551 Loss_G: 1.9887 D(x): 0.6600 D(G(z)): 0.0676 / 0.0657\n", + "[392/1600][0/8] Loss_D: 0.7080 Loss_G: 2.0420 D(x): 0.6304 D(G(z)): 0.0606 / 0.0602\n", + "[392/1600][2/8] Loss_D: 0.7789 Loss_G: 1.9760 D(x): 0.6575 D(G(z)): 0.0685 / 0.0674\n", + "[392/1600][4/8] Loss_D: 0.6928 Loss_G: 2.0026 D(x): 0.6114 D(G(z)): 0.0625 / 0.0633\n", + "[392/1600][6/8] Loss_D: 0.7451 Loss_G: 2.0263 D(x): 0.6286 D(G(z)): 0.0607 / 0.0613\n", + "[393/1600][0/8] Loss_D: 0.7703 Loss_G: 2.0267 D(x): 0.6359 D(G(z)): 0.0649 / 0.0628\n", + "[393/1600][2/8] Loss_D: 0.7415 Loss_G: 2.0901 D(x): 0.6358 D(G(z)): 0.0571 / 0.0558\n", + "[393/1600][4/8] Loss_D: 0.6862 Loss_G: 2.0623 D(x): 0.6344 D(G(z)): 0.0592 / 0.0604\n", + "[393/1600][6/8] Loss_D: 0.7632 Loss_G: 1.9666 D(x): 0.6600 D(G(z)): 0.0681 / 0.0672\n", + "[394/1600][0/8] Loss_D: 0.6876 Loss_G: 1.9267 D(x): 0.6319 D(G(z)): 0.0704 / 0.0718\n", + "[394/1600][2/8] Loss_D: 0.6495 Loss_G: 1.9492 D(x): 0.6763 D(G(z)): 0.0677 / 0.0678\n", + "[394/1600][4/8] Loss_D: 0.7576 Loss_G: 1.8309 D(x): 0.6118 D(G(z)): 0.0808 / 0.0805\n", + "[394/1600][6/8] Loss_D: 0.6715 Loss_G: 1.9414 D(x): 0.6691 D(G(z)): 0.0709 / 0.0705\n", + "[395/1600][0/8] Loss_D: 0.7417 Loss_G: 1.9105 D(x): 0.6257 D(G(z)): 0.0732 / 0.0720\n", + "[395/1600][2/8] Loss_D: 0.7218 Loss_G: 1.9363 D(x): 0.6614 D(G(z)): 0.0721 / 0.0704\n", + "[395/1600][4/8] Loss_D: 0.6887 Loss_G: 1.9854 D(x): 0.6576 D(G(z)): 0.0656 / 0.0647\n", + "[395/1600][6/8] Loss_D: 0.6946 Loss_G: 1.9988 D(x): 0.6351 D(G(z)): 0.0636 / 0.0633\n", + "[396/1600][0/8] Loss_D: 0.7178 Loss_G: 2.0043 D(x): 0.6079 D(G(z)): 0.0627 / 0.0638\n", + "[396/1600][2/8] Loss_D: 0.7749 Loss_G: 2.0267 D(x): 0.6036 D(G(z)): 0.0634 / 0.0623\n", + "[396/1600][4/8] Loss_D: 0.7463 Loss_G: 1.9808 D(x): 0.5965 D(G(z)): 0.0656 / 0.0663\n", + "[396/1600][6/8] Loss_D: 0.7161 Loss_G: 2.0575 D(x): 0.6369 D(G(z)): 0.0592 / 0.0585\n", + "[397/1600][0/8] Loss_D: 0.7705 Loss_G: 1.9449 D(x): 0.6404 D(G(z)): 0.0716 / 0.0694\n", + "[397/1600][2/8] Loss_D: 0.7498 Loss_G: 2.0401 D(x): 0.6329 D(G(z)): 0.0619 / 0.0605\n", + "[397/1600][4/8] Loss_D: 0.7335 Loss_G: 2.0100 D(x): 0.6352 D(G(z)): 0.0643 / 0.0636\n", + "[397/1600][6/8] Loss_D: 0.7096 Loss_G: 2.0391 D(x): 0.6228 D(G(z)): 0.0621 / 0.0621\n", + "[398/1600][0/8] Loss_D: 0.7761 Loss_G: 2.0451 D(x): 0.6476 D(G(z)): 0.0625 / 0.0602\n", + "[398/1600][2/8] Loss_D: 0.7426 Loss_G: 2.0009 D(x): 0.5886 D(G(z)): 0.0637 / 0.0647\n", + "[398/1600][4/8] Loss_D: 0.7741 Loss_G: 2.0097 D(x): 0.6084 D(G(z)): 0.0635 / 0.0639\n", + "[398/1600][6/8] Loss_D: 0.7175 Loss_G: 2.0375 D(x): 0.6548 D(G(z)): 0.0640 / 0.0630\n", + "[399/1600][0/8] Loss_D: 0.7123 Loss_G: 2.0115 D(x): 0.6439 D(G(z)): 0.0642 / 0.0628\n", + "[399/1600][2/8] Loss_D: 0.6739 Loss_G: 2.0445 D(x): 0.6218 D(G(z)): 0.0584 / 0.0596\n", + "[399/1600][4/8] Loss_D: 0.7226 Loss_G: 2.0337 D(x): 0.5834 D(G(z)): 0.0595 / 0.0612\n", + "[399/1600][6/8] Loss_D: 0.7134 Loss_G: 2.0392 D(x): 0.6067 D(G(z)): 0.0588 / 0.0603\n", + "[400/1600][0/8] Loss_D: 0.7329 Loss_G: 1.9425 D(x): 0.6143 D(G(z)): 0.0696 / 0.0689\n", + "[400/1600][2/8] Loss_D: 0.7541 Loss_G: 1.9818 D(x): 0.6436 D(G(z)): 0.0685 / 0.0668\n", + "[400/1600][4/8] Loss_D: 0.6807 Loss_G: 2.0367 D(x): 0.6468 D(G(z)): 0.0611 / 0.0603\n", + "[400/1600][6/8] Loss_D: 0.7276 Loss_G: 2.0409 D(x): 0.6144 D(G(z)): 0.0610 / 0.0619\n", + "[401/1600][0/8] Loss_D: 0.6945 Loss_G: 2.0561 D(x): 0.6271 D(G(z)): 0.0581 / 0.0588\n", + "[401/1600][2/8] Loss_D: 0.7805 Loss_G: 2.0025 D(x): 0.6558 D(G(z)): 0.0662 / 0.0634\n", + "[401/1600][4/8] Loss_D: 0.7282 Loss_G: 2.0361 D(x): 0.6090 D(G(z)): 0.0616 / 0.0611\n", + "[401/1600][6/8] Loss_D: 0.6578 Loss_G: 2.0554 D(x): 0.6621 D(G(z)): 0.0599 / 0.0597\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[402/1600][0/8] Loss_D: 0.7228 Loss_G: 2.0610 D(x): 0.6388 D(G(z)): 0.0595 / 0.0582\n", + "[402/1600][2/8] Loss_D: 0.6430 Loss_G: 2.0225 D(x): 0.6558 D(G(z)): 0.0612 / 0.0624\n", + "[402/1600][4/8] Loss_D: 0.7276 Loss_G: 2.0527 D(x): 0.6216 D(G(z)): 0.0599 / 0.0596\n", + "[402/1600][6/8] Loss_D: 0.7496 Loss_G: 2.0069 D(x): 0.6197 D(G(z)): 0.0627 / 0.0627\n", + "[403/1600][0/8] Loss_D: 0.6827 Loss_G: 1.9827 D(x): 0.6292 D(G(z)): 0.0639 / 0.0656\n", + "[403/1600][2/8] Loss_D: 0.7280 Loss_G: 1.9405 D(x): 0.6229 D(G(z)): 0.0697 / 0.0704\n", + "[403/1600][4/8] Loss_D: 0.7520 Loss_G: 1.9793 D(x): 0.6520 D(G(z)): 0.0673 / 0.0653\n", + "[403/1600][6/8] Loss_D: 0.7220 Loss_G: 2.0078 D(x): 0.6119 D(G(z)): 0.0621 / 0.0621\n", + "[404/1600][0/8] Loss_D: 0.7700 Loss_G: 2.0361 D(x): 0.6554 D(G(z)): 0.0612 / 0.0598\n", + "[404/1600][2/8] Loss_D: 0.7447 Loss_G: 1.9136 D(x): 0.6204 D(G(z)): 0.0724 / 0.0723\n", + "[404/1600][4/8] Loss_D: 0.7235 Loss_G: 1.9369 D(x): 0.6053 D(G(z)): 0.0691 / 0.0702\n", + "[404/1600][6/8] Loss_D: 0.7572 Loss_G: 1.9415 D(x): 0.6615 D(G(z)): 0.0721 / 0.0696\n", + "[405/1600][0/8] Loss_D: 0.6839 Loss_G: 1.9708 D(x): 0.6485 D(G(z)): 0.0662 / 0.0666\n", + "[405/1600][2/8] Loss_D: 0.6653 Loss_G: 1.9708 D(x): 0.6442 D(G(z)): 0.0649 / 0.0661\n", + "[405/1600][4/8] Loss_D: 0.6990 Loss_G: 1.9173 D(x): 0.6754 D(G(z)): 0.0715 / 0.0713\n", + "[405/1600][6/8] Loss_D: 0.7254 Loss_G: 1.9726 D(x): 0.6224 D(G(z)): 0.0656 / 0.0656\n", + "[406/1600][0/8] Loss_D: 0.7197 Loss_G: 1.9629 D(x): 0.6398 D(G(z)): 0.0672 / 0.0668\n", + "[406/1600][2/8] Loss_D: 0.7625 Loss_G: 1.9375 D(x): 0.6506 D(G(z)): 0.0719 / 0.0697\n", + "[406/1600][4/8] Loss_D: 0.7406 Loss_G: 1.9453 D(x): 0.6369 D(G(z)): 0.0708 / 0.0704\n", + "[406/1600][6/8] Loss_D: 0.7266 Loss_G: 1.9735 D(x): 0.6595 D(G(z)): 0.0685 / 0.0675\n", + "[407/1600][0/8] Loss_D: 0.7328 Loss_G: 1.9564 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D(x): 0.6744 D(G(z)): 0.0656 / 0.0647\n", + "[434/1600][6/8] Loss_D: 0.6849 Loss_G: 1.9787 D(x): 0.6363 D(G(z)): 0.0670 / 0.0677\n", + "[435/1600][0/8] Loss_D: 0.7376 Loss_G: 2.0130 D(x): 0.5889 D(G(z)): 0.0625 / 0.0634\n", + "[435/1600][2/8] Loss_D: 0.6843 Loss_G: 2.0084 D(x): 0.6155 D(G(z)): 0.0621 / 0.0630\n", + "[435/1600][4/8] Loss_D: 0.7250 Loss_G: 2.0233 D(x): 0.6546 D(G(z)): 0.0646 / 0.0640\n", + "[435/1600][6/8] Loss_D: 0.7318 Loss_G: 1.9913 D(x): 0.6004 D(G(z)): 0.0648 / 0.0653\n", + "[436/1600][0/8] Loss_D: 0.7017 Loss_G: 1.9990 D(x): 0.6698 D(G(z)): 0.0644 / 0.0640\n", + "[436/1600][2/8] Loss_D: 0.7386 Loss_G: 2.0546 D(x): 0.6608 D(G(z)): 0.0601 / 0.0595\n", + "[436/1600][4/8] Loss_D: 0.6875 Loss_G: 2.0130 D(x): 0.6509 D(G(z)): 0.0625 / 0.0624\n", + "[436/1600][6/8] Loss_D: 0.7804 Loss_G: 2.0602 D(x): 0.6381 D(G(z)): 0.0620 / 0.0595\n", + "[437/1600][0/8] Loss_D: 0.6864 Loss_G: 2.0504 D(x): 0.6504 D(G(z)): 0.0609 / 0.0608\n", + "[437/1600][2/8] Loss_D: 0.7492 Loss_G: 2.0510 D(x): 0.6490 D(G(z)): 0.0606 / 0.0599\n", + "[437/1600][4/8] Loss_D: 0.6827 Loss_G: 1.9489 D(x): 0.6475 D(G(z)): 0.0676 / 0.0690\n", + "[437/1600][6/8] Loss_D: 0.7720 Loss_G: 2.0173 D(x): 0.6742 D(G(z)): 0.0665 / 0.0640\n", + "[438/1600][0/8] Loss_D: 0.7544 Loss_G: 2.0757 D(x): 0.6661 D(G(z)): 0.0589 / 0.0575\n", + "[438/1600][2/8] Loss_D: 0.6955 Loss_G: 2.0367 D(x): 0.6375 D(G(z)): 0.0602 / 0.0609\n", + "[438/1600][4/8] Loss_D: 0.7820 Loss_G: 2.0163 D(x): 0.6424 D(G(z)): 0.0674 / 0.0645\n", + "[438/1600][6/8] Loss_D: 0.7447 Loss_G: 2.0545 D(x): 0.6181 D(G(z)): 0.0609 / 0.0603\n", + "[439/1600][0/8] Loss_D: 0.7554 Loss_G: 2.1041 D(x): 0.5917 D(G(z)): 0.0550 / 0.0547\n", + "[439/1600][2/8] Loss_D: 0.7640 Loss_G: 2.0158 D(x): 0.6439 D(G(z)): 0.0649 / 0.0633\n", + "[439/1600][4/8] Loss_D: 0.7518 Loss_G: 2.0634 D(x): 0.6478 D(G(z)): 0.0612 / 0.0593\n", + "[439/1600][6/8] Loss_D: 0.7180 Loss_G: 2.1033 D(x): 0.6051 D(G(z)): 0.0542 / 0.0546\n", + "[440/1600][0/8] Loss_D: 0.7053 Loss_G: 1.9790 D(x): 0.6414 D(G(z)): 0.0643 / 0.0657\n", + "[440/1600][2/8] Loss_D: 0.7263 Loss_G: 2.0550 D(x): 0.6462 D(G(z)): 0.0601 / 0.0587\n", + "[440/1600][4/8] Loss_D: 0.7268 Loss_G: 2.0320 D(x): 0.5845 D(G(z)): 0.0610 / 0.0623\n", + "[440/1600][6/8] Loss_D: 0.7396 Loss_G: 2.0496 D(x): 0.6480 D(G(z)): 0.0616 / 0.0607\n", + "[441/1600][0/8] Loss_D: 0.7062 Loss_G: 1.9531 D(x): 0.6130 D(G(z)): 0.0662 / 0.0674\n", + "[441/1600][2/8] Loss_D: 0.6664 Loss_G: 2.0032 D(x): 0.6381 D(G(z)): 0.0620 / 0.0637\n", + "[441/1600][4/8] Loss_D: 0.7654 Loss_G: 1.9867 D(x): 0.6486 D(G(z)): 0.0679 / 0.0664\n", + "[441/1600][6/8] Loss_D: 0.6362 Loss_G: 1.9999 D(x): 0.6876 D(G(z)): 0.0633 / 0.0643\n", + "[442/1600][0/8] Loss_D: 0.6584 Loss_G: 1.9453 D(x): 0.6891 D(G(z)): 0.0700 / 0.0690\n", + "[442/1600][2/8] Loss_D: 0.6598 Loss_G: 1.9074 D(x): 0.6591 D(G(z)): 0.0728 / 0.0736\n", + "[442/1600][4/8] Loss_D: 0.7888 Loss_G: 1.9315 D(x): 0.6790 D(G(z)): 0.0757 / 0.0726\n", + "[442/1600][6/8] Loss_D: 0.7600 Loss_G: 2.0607 D(x): 0.6601 D(G(z)): 0.0628 / 0.0594\n", + "[443/1600][0/8] Loss_D: 0.7117 Loss_G: 2.0525 D(x): 0.6244 D(G(z)): 0.0602 / 0.0598\n", + "[443/1600][2/8] Loss_D: 0.7058 Loss_G: 2.0638 D(x): 0.6591 D(G(z)): 0.0616 / 0.0610\n", + "[443/1600][4/8] Loss_D: 0.7536 Loss_G: 1.9954 D(x): 0.5926 D(G(z)): 0.0655 / 0.0655\n", + "[443/1600][6/8] Loss_D: 0.7194 Loss_G: 2.0630 D(x): 0.5878 D(G(z)): 0.0574 / 0.0585\n", + "[444/1600][0/8] Loss_D: 0.7364 Loss_G: 2.0870 D(x): 0.6780 D(G(z)): 0.0580 / 0.0573\n", + "[444/1600][2/8] Loss_D: 0.6987 Loss_G: 1.9881 D(x): 0.6575 D(G(z)): 0.0669 / 0.0662\n", + "[444/1600][4/8] Loss_D: 0.6900 Loss_G: 2.0436 D(x): 0.6235 D(G(z)): 0.0601 / 0.0609\n", + "[444/1600][6/8] Loss_D: 0.7685 Loss_G: 2.0998 D(x): 0.6213 D(G(z)): 0.0573 / 0.0558\n", + "[445/1600][0/8] Loss_D: 0.7554 Loss_G: 2.0511 D(x): 0.6031 D(G(z)): 0.0601 / 0.0600\n", + "[445/1600][2/8] Loss_D: 0.6761 Loss_G: 2.0190 D(x): 0.6341 D(G(z)): 0.0637 / 0.0638\n", + "[445/1600][4/8] Loss_D: 0.7250 Loss_G: 2.0452 D(x): 0.6460 D(G(z)): 0.0604 / 0.0600\n", + "[445/1600][6/8] Loss_D: 0.7233 Loss_G: 2.0082 D(x): 0.5834 D(G(z)): 0.0627 / 0.0645\n", + "[446/1600][0/8] Loss_D: 0.6704 Loss_G: 2.0180 D(x): 0.6383 D(G(z)): 0.0620 / 0.0637\n", + "[446/1600][2/8] Loss_D: 0.7375 Loss_G: 2.0563 D(x): 0.6264 D(G(z)): 0.0596 / 0.0593\n", + "[446/1600][4/8] Loss_D: 0.7415 Loss_G: 2.0149 D(x): 0.6660 D(G(z)): 0.0649 / 0.0624\n", + "[446/1600][6/8] Loss_D: 0.7072 Loss_G: 2.0111 D(x): 0.6296 D(G(z)): 0.0654 / 0.0647\n", + "[447/1600][0/8] Loss_D: 0.6966 Loss_G: 2.0489 D(x): 0.6135 D(G(z)): 0.0598 / 0.0612\n", + "[447/1600][2/8] Loss_D: 0.6677 Loss_G: 2.0474 D(x): 0.6329 D(G(z)): 0.0595 / 0.0614\n", + "[447/1600][4/8] Loss_D: 0.6703 Loss_G: 1.9370 D(x): 0.6428 D(G(z)): 0.0704 / 0.0725\n", + "[447/1600][6/8] Loss_D: 0.7287 Loss_G: 1.9478 D(x): 0.6854 D(G(z)): 0.0717 / 0.0707\n", + "[448/1600][0/8] Loss_D: 0.6853 Loss_G: 1.8942 D(x): 0.6247 D(G(z)): 0.0767 / 0.0778\n", + "[448/1600][2/8] Loss_D: 0.7068 Loss_G: 1.9917 D(x): 0.7106 D(G(z)): 0.0677 / 0.0656\n", + "[448/1600][4/8] Loss_D: 0.6802 Loss_G: 1.9452 D(x): 0.6927 D(G(z)): 0.0727 / 0.0705\n", + "[448/1600][6/8] Loss_D: 0.7369 Loss_G: 1.9978 D(x): 0.6001 D(G(z)): 0.0667 / 0.0676\n", + "[449/1600][0/8] Loss_D: 0.7521 Loss_G: 1.9982 D(x): 0.6557 D(G(z)): 0.0649 / 0.0640\n", + "[449/1600][2/8] Loss_D: 0.7454 Loss_G: 1.9359 D(x): 0.6214 D(G(z)): 0.0718 / 0.0705\n", + "[449/1600][4/8] Loss_D: 0.6596 Loss_G: 2.0635 D(x): 0.6327 D(G(z)): 0.0589 / 0.0592\n", + "[449/1600][6/8] Loss_D: 0.7537 Loss_G: 2.0172 D(x): 0.6564 D(G(z)): 0.0680 / 0.0651\n", + "[450/1600][0/8] Loss_D: 0.7513 Loss_G: 2.0966 D(x): 0.6171 D(G(z)): 0.0565 / 0.0560\n", + "[450/1600][2/8] Loss_D: 0.7097 Loss_G: 2.0674 D(x): 0.6541 D(G(z)): 0.0597 / 0.0591\n", + "[450/1600][4/8] Loss_D: 0.7077 Loss_G: 2.0457 D(x): 0.6404 D(G(z)): 0.0600 / 0.0599\n", + "[450/1600][6/8] Loss_D: 0.7626 Loss_G: 2.0703 D(x): 0.6396 D(G(z)): 0.0597 / 0.0589\n", + "[451/1600][0/8] Loss_D: 0.7367 Loss_G: 1.9693 D(x): 0.6316 D(G(z)): 0.0684 / 0.0684\n", + "[451/1600][2/8] Loss_D: 0.6679 Loss_G: 2.0362 D(x): 0.6315 D(G(z)): 0.0628 / 0.0638\n", + "[451/1600][4/8] Loss_D: 0.7288 Loss_G: 2.1009 D(x): 0.6421 D(G(z)): 0.0571 / 0.0560\n", + "[451/1600][6/8] Loss_D: 0.7589 Loss_G: 2.0160 D(x): 0.6748 D(G(z)): 0.0656 / 0.0635\n", + "[452/1600][0/8] Loss_D: 0.7122 Loss_G: 2.0946 D(x): 0.6386 D(G(z)): 0.0573 / 0.0564\n", + "[452/1600][2/8] Loss_D: 0.6817 Loss_G: 2.0737 D(x): 0.6131 D(G(z)): 0.0553 / 0.0578\n", + "[452/1600][4/8] Loss_D: 0.6728 Loss_G: 2.0430 D(x): 0.6270 D(G(z)): 0.0586 / 0.0601\n", + "[452/1600][6/8] Loss_D: 0.6351 Loss_G: 1.9773 D(x): 0.6810 D(G(z)): 0.0646 / 0.0662\n", + "[453/1600][0/8] Loss_D: 0.7793 Loss_G: 2.0398 D(x): 0.6559 D(G(z)): 0.0645 / 0.0616\n", + "[453/1600][2/8] Loss_D: 0.6989 Loss_G: 2.0216 D(x): 0.6602 D(G(z)): 0.0639 / 0.0624\n", + "[453/1600][4/8] Loss_D: 0.6851 Loss_G: 1.9966 D(x): 0.6353 D(G(z)): 0.0650 / 0.0661\n", + "[453/1600][6/8] Loss_D: 0.7468 Loss_G: 2.0329 D(x): 0.6740 D(G(z)): 0.0643 / 0.0621\n", + "[454/1600][0/8] Loss_D: 0.7553 Loss_G: 2.0628 D(x): 0.5976 D(G(z)): 0.0591 / 0.0583\n", + "[454/1600][2/8] Loss_D: 0.7557 Loss_G: 2.0255 D(x): 0.6267 D(G(z)): 0.0630 / 0.0627\n", + "[454/1600][4/8] Loss_D: 0.7739 Loss_G: 2.0962 D(x): 0.6552 D(G(z)): 0.0599 / 0.0573\n", + "[454/1600][6/8] Loss_D: 0.7524 Loss_G: 2.1283 D(x): 0.6133 D(G(z)): 0.0550 / 0.0544\n", + "[455/1600][0/8] Loss_D: 0.6953 Loss_G: 2.0757 D(x): 0.6077 D(G(z)): 0.0564 / 0.0582\n", + "[455/1600][2/8] Loss_D: 0.7049 Loss_G: 2.0947 D(x): 0.6858 D(G(z)): 0.0578 / 0.0572\n", + "[455/1600][4/8] Loss_D: 0.7220 Loss_G: 2.0308 D(x): 0.6027 D(G(z)): 0.0626 / 0.0625\n", + "[455/1600][6/8] Loss_D: 0.7338 Loss_G: 2.0603 D(x): 0.6439 D(G(z)): 0.0611 / 0.0599\n", + "[456/1600][0/8] Loss_D: 0.7107 Loss_G: 2.0956 D(x): 0.6385 D(G(z)): 0.0568 / 0.0572\n", + "[456/1600][2/8] Loss_D: 0.7463 Loss_G: 2.0886 D(x): 0.6310 D(G(z)): 0.0589 / 0.0576\n", + "[456/1600][4/8] Loss_D: 0.6625 Loss_G: 2.0240 D(x): 0.6441 D(G(z)): 0.0632 / 0.0646\n", + "[456/1600][6/8] Loss_D: 0.7774 Loss_G: 2.0324 D(x): 0.5818 D(G(z)): 0.0622 / 0.0625\n", + "[457/1600][0/8] Loss_D: 0.7590 Loss_G: 2.0072 D(x): 0.6044 D(G(z)): 0.0659 / 0.0644\n", + "[457/1600][2/8] Loss_D: 0.7591 Loss_G: 2.1035 D(x): 0.6370 D(G(z)): 0.0594 / 0.0576\n", + "[457/1600][4/8] Loss_D: 0.7599 Loss_G: 2.1459 D(x): 0.6411 D(G(z)): 0.0559 / 0.0535\n", + "[457/1600][6/8] Loss_D: 0.7135 Loss_G: 2.1478 D(x): 0.5817 D(G(z)): 0.0506 / 0.0524\n", + "[458/1600][0/8] Loss_D: 0.7447 Loss_G: 2.0231 D(x): 0.6636 D(G(z)): 0.0618 / 0.0621\n", + "[458/1600][2/8] Loss_D: 0.7638 Loss_G: 2.1479 D(x): 0.6342 D(G(z)): 0.0560 / 0.0536\n", + "[458/1600][4/8] Loss_D: 0.7397 Loss_G: 2.1678 D(x): 0.6005 D(G(z)): 0.0511 / 0.0507\n", + "[458/1600][6/8] Loss_D: 0.7568 Loss_G: 2.2089 D(x): 0.5960 D(G(z)): 0.0488 / 0.0481\n", + "[459/1600][0/8] Loss_D: 0.7521 Loss_G: 2.1484 D(x): 0.6159 D(G(z)): 0.0540 / 0.0529\n", + "[459/1600][2/8] Loss_D: 0.6755 Loss_G: 2.1436 D(x): 0.6039 D(G(z)): 0.0503 / 0.0517\n", + "[459/1600][4/8] Loss_D: 0.7633 Loss_G: 2.1942 D(x): 0.6075 D(G(z)): 0.0502 / 0.0497\n", + "[459/1600][6/8] Loss_D: 0.6594 Loss_G: 2.0816 D(x): 0.6319 D(G(z)): 0.0574 / 0.0589\n", + "[460/1600][0/8] Loss_D: 0.7186 Loss_G: 2.0916 D(x): 0.6275 D(G(z)): 0.0548 / 0.0559\n", + "[460/1600][2/8] Loss_D: 0.6666 Loss_G: 2.0633 D(x): 0.6846 D(G(z)): 0.0593 / 0.0595\n", + "[460/1600][4/8] Loss_D: 0.7148 Loss_G: 2.0250 D(x): 0.6475 D(G(z)): 0.0633 / 0.0625\n", + "[460/1600][6/8] Loss_D: 0.6384 Loss_G: 2.1133 D(x): 0.6560 D(G(z)): 0.0565 / 0.0567\n", + "[461/1600][0/8] Loss_D: 0.6790 Loss_G: 2.0706 D(x): 0.6544 D(G(z)): 0.0570 / 0.0572\n", + "[461/1600][2/8] Loss_D: 0.7259 Loss_G: 2.0201 D(x): 0.6352 D(G(z)): 0.0635 / 0.0632\n", + "[461/1600][4/8] Loss_D: 0.7475 Loss_G: 2.1387 D(x): 0.5961 D(G(z)): 0.0544 / 0.0539\n", + "[461/1600][6/8] Loss_D: 0.7727 Loss_G: 1.9964 D(x): 0.6271 D(G(z)): 0.0669 / 0.0656\n", + "[462/1600][0/8] Loss_D: 0.7053 Loss_G: 2.0627 D(x): 0.6607 D(G(z)): 0.0603 / 0.0590\n", + "[462/1600][2/8] Loss_D: 0.7243 Loss_G: 2.0812 D(x): 0.6172 D(G(z)): 0.0578 / 0.0580\n", + "[462/1600][4/8] Loss_D: 0.7008 Loss_G: 2.0750 D(x): 0.6136 D(G(z)): 0.0577 / 0.0592\n", + "[462/1600][6/8] Loss_D: 0.7481 Loss_G: 1.9985 D(x): 0.6304 D(G(z)): 0.0663 / 0.0648\n", + "[463/1600][0/8] Loss_D: 0.6704 Loss_G: 2.1141 D(x): 0.6396 D(G(z)): 0.0551 / 0.0558\n", + "[463/1600][2/8] Loss_D: 0.7669 Loss_G: 2.0626 D(x): 0.6331 D(G(z)): 0.0604 / 0.0590\n", + "[463/1600][4/8] Loss_D: 0.7685 Loss_G: 2.0583 D(x): 0.6134 D(G(z)): 0.0607 / 0.0591\n", + "[463/1600][6/8] Loss_D: 0.7299 Loss_G: 2.1273 D(x): 0.6226 D(G(z)): 0.0542 / 0.0538\n", + "[464/1600][0/8] Loss_D: 0.7538 Loss_G: 2.1176 D(x): 0.6686 D(G(z)): 0.0560 / 0.0545\n", + "[464/1600][2/8] Loss_D: 0.7426 Loss_G: 2.1161 D(x): 0.6196 D(G(z)): 0.0555 / 0.0547\n", + "[464/1600][4/8] Loss_D: 0.7310 Loss_G: 2.1207 D(x): 0.6375 D(G(z)): 0.0564 / 0.0556\n", + "[464/1600][6/8] Loss_D: 0.7568 Loss_G: 2.0996 D(x): 0.5968 D(G(z)): 0.0593 / 0.0581\n", + "[465/1600][0/8] Loss_D: 0.7219 Loss_G: 2.1415 D(x): 0.5975 D(G(z)): 0.0528 / 0.0534\n", + "[465/1600][2/8] Loss_D: 0.7039 Loss_G: 2.1047 D(x): 0.6261 D(G(z)): 0.0568 / 0.0574\n", + "[465/1600][4/8] Loss_D: 0.7471 Loss_G: 2.1459 D(x): 0.6294 D(G(z)): 0.0521 / 0.0514\n", + "[465/1600][6/8] Loss_D: 0.7469 Loss_G: 2.1683 D(x): 0.5858 D(G(z)): 0.0510 / 0.0502\n", + "[466/1600][0/8] Loss_D: 0.7134 Loss_G: 2.0725 D(x): 0.6086 D(G(z)): 0.0575 / 0.0582\n", + "[466/1600][2/8] Loss_D: 0.7233 Loss_G: 2.1741 D(x): 0.6066 D(G(z)): 0.0516 / 0.0515\n", + "[466/1600][4/8] Loss_D: 0.7052 Loss_G: 2.0687 D(x): 0.6114 D(G(z)): 0.0593 / 0.0605\n", + "[466/1600][6/8] Loss_D: 0.7273 Loss_G: 2.1460 D(x): 0.6420 D(G(z)): 0.0537 / 0.0534\n", + "[467/1600][0/8] Loss_D: 0.7211 Loss_G: 2.0402 D(x): 0.6000 D(G(z)): 0.0590 / 0.0611\n", + "[467/1600][2/8] Loss_D: 0.7214 Loss_G: 2.0542 D(x): 0.6237 D(G(z)): 0.0598 / 0.0597\n", + "[467/1600][4/8] Loss_D: 0.7410 Loss_G: 2.0211 D(x): 0.6501 D(G(z)): 0.0637 / 0.0630\n", + "[467/1600][6/8] Loss_D: 0.7003 Loss_G: 2.0232 D(x): 0.6513 D(G(z)): 0.0635 / 0.0632\n", + "[468/1600][0/8] Loss_D: 0.6828 Loss_G: 2.1085 D(x): 0.6147 D(G(z)): 0.0542 / 0.0559\n", + "[468/1600][2/8] Loss_D: 0.6937 Loss_G: 2.0049 D(x): 0.6554 D(G(z)): 0.0657 / 0.0656\n", + "[468/1600][4/8] Loss_D: 0.7339 Loss_G: 2.0225 D(x): 0.6222 D(G(z)): 0.0634 / 0.0632\n", + "[468/1600][6/8] Loss_D: 0.7384 Loss_G: 2.0499 D(x): 0.6402 D(G(z)): 0.0618 / 0.0610\n", + "[469/1600][0/8] Loss_D: 0.6986 Loss_G: 2.0901 D(x): 0.6476 D(G(z)): 0.0573 / 0.0566\n", + "[469/1600][2/8] Loss_D: 0.7307 Loss_G: 2.1666 D(x): 0.6430 D(G(z)): 0.0533 / 0.0517\n", + "[469/1600][4/8] Loss_D: 0.7679 Loss_G: 2.1281 D(x): 0.6647 D(G(z)): 0.0560 / 0.0541\n", + "[469/1600][6/8] Loss_D: 0.7062 Loss_G: 2.1601 D(x): 0.6471 D(G(z)): 0.0523 / 0.0515\n", + "[470/1600][0/8] Loss_D: 0.6980 Loss_G: 2.1484 D(x): 0.6333 D(G(z)): 0.0523 / 0.0520\n", + "[470/1600][2/8] Loss_D: 0.7050 Loss_G: 2.1474 D(x): 0.6179 D(G(z)): 0.0522 / 0.0529\n", + "[470/1600][4/8] Loss_D: 0.7700 Loss_G: 2.0909 D(x): 0.6269 D(G(z)): 0.0579 / 0.0569\n", + "[470/1600][6/8] Loss_D: 0.7436 Loss_G: 2.1292 D(x): 0.6127 D(G(z)): 0.0551 / 0.0547\n", + "[471/1600][0/8] Loss_D: 0.7209 Loss_G: 2.1847 D(x): 0.6598 D(G(z)): 0.0509 / 0.0509\n", + "[471/1600][2/8] Loss_D: 0.7312 Loss_G: 2.1221 D(x): 0.6389 D(G(z)): 0.0565 / 0.0562\n", + "[471/1600][4/8] Loss_D: 0.6820 Loss_G: 2.0500 D(x): 0.6774 D(G(z)): 0.0611 / 0.0607\n", + "[471/1600][6/8] Loss_D: 0.7390 Loss_G: 2.1669 D(x): 0.6720 D(G(z)): 0.0545 / 0.0525\n", + "[472/1600][0/8] Loss_D: 0.6886 Loss_G: 2.1181 D(x): 0.6214 D(G(z)): 0.0528 / 0.0544\n", + "[472/1600][2/8] Loss_D: 0.7436 Loss_G: 2.0757 D(x): 0.6373 D(G(z)): 0.0584 / 0.0575\n", + "[472/1600][4/8] Loss_D: 0.7515 Loss_G: 2.1566 D(x): 0.6491 D(G(z)): 0.0537 / 0.0524\n", + "[472/1600][6/8] Loss_D: 0.6956 Loss_G: 2.0124 D(x): 0.6105 D(G(z)): 0.0640 / 0.0658\n", + "[473/1600][0/8] Loss_D: 0.6772 Loss_G: 2.1060 D(x): 0.6613 D(G(z)): 0.0551 / 0.0562\n", + "[473/1600][2/8] Loss_D: 0.7530 Loss_G: 2.0600 D(x): 0.6492 D(G(z)): 0.0609 / 0.0598\n", + "[473/1600][4/8] Loss_D: 0.7031 Loss_G: 1.9820 D(x): 0.6210 D(G(z)): 0.0663 / 0.0671\n", + "[473/1600][6/8] Loss_D: 0.7076 Loss_G: 2.0470 D(x): 0.6525 D(G(z)): 0.0625 / 0.0609\n", + "[474/1600][0/8] Loss_D: 0.6742 Loss_G: 2.0152 D(x): 0.6453 D(G(z)): 0.0626 / 0.0636\n", + "[474/1600][2/8] Loss_D: 0.7641 Loss_G: 2.0839 D(x): 0.6721 D(G(z)): 0.0616 / 0.0593\n", + "[474/1600][4/8] Loss_D: 0.6984 Loss_G: 2.0746 D(x): 0.6501 D(G(z)): 0.0604 / 0.0589\n", + "[474/1600][6/8] Loss_D: 0.6713 Loss_G: 2.1513 D(x): 0.6724 D(G(z)): 0.0523 / 0.0527\n", + "[475/1600][0/8] Loss_D: 0.7175 Loss_G: 2.0864 D(x): 0.6218 D(G(z)): 0.0575 / 0.0572\n", + "[475/1600][2/8] Loss_D: 0.7004 Loss_G: 2.0933 D(x): 0.6516 D(G(z)): 0.0574 / 0.0572\n", + "[475/1600][4/8] Loss_D: 0.6723 Loss_G: 2.0986 D(x): 0.6541 D(G(z)): 0.0567 / 0.0569\n", + "[475/1600][6/8] Loss_D: 0.7533 Loss_G: 2.0715 D(x): 0.6740 D(G(z)): 0.0619 / 0.0592\n", + "[476/1600][0/8] Loss_D: 0.6980 Loss_G: 2.1527 D(x): 0.6172 D(G(z)): 0.0514 / 0.0526\n", + "[476/1600][2/8] Loss_D: 0.7424 Loss_G: 2.1157 D(x): 0.6043 D(G(z)): 0.0548 / 0.0552\n", + "[476/1600][4/8] Loss_D: 0.7435 Loss_G: 2.0174 D(x): 0.6474 D(G(z)): 0.0643 / 0.0625\n", + "[476/1600][6/8] Loss_D: 0.6660 Loss_G: 2.1334 D(x): 0.6178 D(G(z)): 0.0520 / 0.0529\n", + "[477/1600][0/8] Loss_D: 0.7352 Loss_G: 2.1868 D(x): 0.6254 D(G(z)): 0.0511 / 0.0509\n", + "[477/1600][2/8] Loss_D: 0.6687 Loss_G: 2.0431 D(x): 0.6608 D(G(z)): 0.0612 / 0.0625\n", + "[477/1600][4/8] Loss_D: 0.6955 Loss_G: 2.1202 D(x): 0.6561 D(G(z)): 0.0566 / 0.0555\n", + "[477/1600][6/8] Loss_D: 0.6728 Loss_G: 2.0991 D(x): 0.6177 D(G(z)): 0.0553 / 0.0574\n", + "[478/1600][0/8] Loss_D: 0.7354 Loss_G: 2.0800 D(x): 0.6415 D(G(z)): 0.0586 / 0.0584\n", + "[478/1600][2/8] Loss_D: 0.7353 Loss_G: 2.0440 D(x): 0.6337 D(G(z)): 0.0628 / 0.0621\n", + "[478/1600][4/8] Loss_D: 0.7225 Loss_G: 2.0918 D(x): 0.6337 D(G(z)): 0.0589 / 0.0575\n", + "[478/1600][6/8] Loss_D: 0.7501 Loss_G: 2.0821 D(x): 0.6684 D(G(z)): 0.0592 / 0.0573\n", + "[479/1600][0/8] Loss_D: 0.7382 Loss_G: 2.1377 D(x): 0.6117 D(G(z)): 0.0542 / 0.0539\n", + "[479/1600][2/8] Loss_D: 0.7709 Loss_G: 2.0960 D(x): 0.5914 D(G(z)): 0.0585 / 0.0568\n", + "[479/1600][4/8] Loss_D: 0.6713 Loss_G: 2.1225 D(x): 0.6686 D(G(z)): 0.0546 / 0.0550\n", + "[479/1600][6/8] Loss_D: 0.7698 Loss_G: 2.1495 D(x): 0.6595 D(G(z)): 0.0546 / 0.0527\n", + "[480/1600][0/8] Loss_D: 0.7258 Loss_G: 2.1880 D(x): 0.6296 D(G(z)): 0.0484 / 0.0484\n", + "[480/1600][2/8] Loss_D: 0.6679 Loss_G: 2.0948 D(x): 0.6309 D(G(z)): 0.0544 / 0.0561\n", + "[480/1600][4/8] Loss_D: 0.6623 Loss_G: 2.0584 D(x): 0.6345 D(G(z)): 0.0570 / 0.0594\n", + "[480/1600][6/8] Loss_D: 0.6897 Loss_G: 2.0051 D(x): 0.6350 D(G(z)): 0.0640 / 0.0654\n", + "[481/1600][0/8] Loss_D: 0.6453 Loss_G: 2.0920 D(x): 0.6527 D(G(z)): 0.0571 / 0.0577\n", + "[481/1600][2/8] Loss_D: 0.7660 Loss_G: 2.0320 D(x): 0.6376 D(G(z)): 0.0653 / 0.0632\n", + "[481/1600][4/8] Loss_D: 0.7445 Loss_G: 2.0498 D(x): 0.6374 D(G(z)): 0.0615 / 0.0603\n", + "[481/1600][6/8] Loss_D: 0.7396 Loss_G: 2.1313 D(x): 0.6279 D(G(z)): 0.0574 / 0.0555\n", + "[482/1600][0/8] Loss_D: 0.7336 Loss_G: 2.1057 D(x): 0.6060 D(G(z)): 0.0560 / 0.0568\n", + "[482/1600][2/8] Loss_D: 0.6447 Loss_G: 2.0651 D(x): 0.6525 D(G(z)): 0.0600 / 0.0614\n", + "[482/1600][4/8] Loss_D: 0.7735 Loss_G: 2.0701 D(x): 0.6733 D(G(z)): 0.0611 / 0.0591\n", + "[482/1600][6/8] Loss_D: 0.7043 Loss_G: 2.1167 D(x): 0.6327 D(G(z)): 0.0541 / 0.0542\n", + "[483/1600][0/8] Loss_D: 0.7441 Loss_G: 2.1492 D(x): 0.6628 D(G(z)): 0.0556 / 0.0535\n", + "[483/1600][2/8] Loss_D: 0.7370 Loss_G: 2.1823 D(x): 0.6144 D(G(z)): 0.0519 / 0.0506\n", + "[483/1600][4/8] Loss_D: 0.7200 Loss_G: 2.1596 D(x): 0.5862 D(G(z)): 0.0513 / 0.0519\n", + "[483/1600][6/8] Loss_D: 0.6580 Loss_G: 2.1158 D(x): 0.6315 D(G(z)): 0.0558 / 0.0567\n", + "[484/1600][0/8] Loss_D: 0.6612 Loss_G: 2.0768 D(x): 0.6352 D(G(z)): 0.0577 / 0.0594\n", + "[484/1600][2/8] Loss_D: 0.7722 Loss_G: 2.1072 D(x): 0.6370 D(G(z)): 0.0577 / 0.0567\n", + "[484/1600][4/8] Loss_D: 0.6816 Loss_G: 2.1184 D(x): 0.6057 D(G(z)): 0.0537 / 0.0545\n", + "[484/1600][6/8] Loss_D: 0.6901 Loss_G: 2.0628 D(x): 0.6562 D(G(z)): 0.0591 / 0.0599\n", + "[485/1600][0/8] Loss_D: 0.7080 Loss_G: 2.1178 D(x): 0.6531 D(G(z)): 0.0555 / 0.0555\n", + "[485/1600][2/8] Loss_D: 0.7330 Loss_G: 2.1063 D(x): 0.6382 D(G(z)): 0.0572 / 0.0561\n", + "[485/1600][4/8] Loss_D: 0.7571 Loss_G: 2.0949 D(x): 0.6182 D(G(z)): 0.0594 / 0.0569\n", + "[485/1600][6/8] Loss_D: 0.7347 Loss_G: 2.1868 D(x): 0.5873 D(G(z)): 0.0514 / 0.0503\n", + "[486/1600][0/8] Loss_D: 0.7097 Loss_G: 2.1961 D(x): 0.5988 D(G(z)): 0.0479 / 0.0498\n", + "[486/1600][2/8] Loss_D: 0.6985 Loss_G: 2.1229 D(x): 0.6092 D(G(z)): 0.0540 / 0.0552\n", + "[486/1600][4/8] Loss_D: 0.7529 Loss_G: 2.1154 D(x): 0.6095 D(G(z)): 0.0560 / 0.0550\n", + "[486/1600][6/8] Loss_D: 0.7453 Loss_G: 2.1114 D(x): 0.6298 D(G(z)): 0.0575 / 0.0563\n", + "[487/1600][0/8] Loss_D: 0.7236 Loss_G: 2.1817 D(x): 0.6344 D(G(z)): 0.0502 / 0.0497\n", + "[487/1600][2/8] Loss_D: 0.7157 Loss_G: 2.0577 D(x): 0.6454 D(G(z)): 0.0595 / 0.0599\n", + "[487/1600][4/8] Loss_D: 0.6367 Loss_G: 2.0629 D(x): 0.6816 D(G(z)): 0.0571 / 0.0585\n", + "[487/1600][6/8] Loss_D: 0.7815 Loss_G: 2.0629 D(x): 0.6714 D(G(z)): 0.0632 / 0.0611\n", + "[488/1600][0/8] Loss_D: 0.7332 Loss_G: 2.0835 D(x): 0.5996 D(G(z)): 0.0574 / 0.0570\n", + "[488/1600][2/8] Loss_D: 0.6583 Loss_G: 2.0683 D(x): 0.6342 D(G(z)): 0.0586 / 0.0605\n", + "[488/1600][4/8] Loss_D: 0.7688 Loss_G: 2.0590 D(x): 0.6361 D(G(z)): 0.0609 / 0.0589\n", + "[488/1600][6/8] Loss_D: 0.7614 Loss_G: 2.1041 D(x): 0.6682 D(G(z)): 0.0588 / 0.0568\n", + "[489/1600][0/8] Loss_D: 0.7327 Loss_G: 2.1545 D(x): 0.6560 D(G(z)): 0.0533 / 0.0520\n", + "[489/1600][2/8] Loss_D: 0.7524 Loss_G: 2.2077 D(x): 0.5938 D(G(z)): 0.0497 / 0.0486\n", + "[489/1600][4/8] Loss_D: 0.7353 Loss_G: 2.1693 D(x): 0.6387 D(G(z)): 0.0534 / 0.0521\n", + "[489/1600][6/8] Loss_D: 0.7429 Loss_G: 2.0856 D(x): 0.5851 D(G(z)): 0.0565 / 0.0579\n", + "[490/1600][0/8] Loss_D: 0.7175 Loss_G: 2.1840 D(x): 0.6141 D(G(z)): 0.0494 / 0.0500\n", + "[490/1600][2/8] Loss_D: 0.7111 Loss_G: 2.0885 D(x): 0.6355 D(G(z)): 0.0570 / 0.0582\n", + "[490/1600][4/8] Loss_D: 0.7495 Loss_G: 2.0575 D(x): 0.6399 D(G(z)): 0.0620 / 0.0607\n", + "[490/1600][6/8] Loss_D: 0.7024 Loss_G: 2.1331 D(x): 0.6225 D(G(z)): 0.0562 / 0.0556\n", + "[491/1600][0/8] Loss_D: 0.7086 Loss_G: 2.1156 D(x): 0.6100 D(G(z)): 0.0539 / 0.0542\n", + "[491/1600][2/8] Loss_D: 0.6696 Loss_G: 2.1004 D(x): 0.6409 D(G(z)): 0.0551 / 0.0557\n", + "[491/1600][4/8] Loss_D: 0.7518 Loss_G: 2.0323 D(x): 0.6443 D(G(z)): 0.0632 / 0.0623\n", + "[491/1600][6/8] Loss_D: 0.6886 Loss_G: 2.1384 D(x): 0.6221 D(G(z)): 0.0541 / 0.0546\n", + "[492/1600][0/8] Loss_D: 0.7391 Loss_G: 2.0620 D(x): 0.6125 D(G(z)): 0.0607 / 0.0607\n", + "[492/1600][2/8] Loss_D: 0.7637 Loss_G: 2.1860 D(x): 0.6721 D(G(z)): 0.0529 / 0.0505\n", + "[492/1600][4/8] Loss_D: 0.7328 Loss_G: 2.1599 D(x): 0.6184 D(G(z)): 0.0545 / 0.0531\n", + "[492/1600][6/8] Loss_D: 0.7164 Loss_G: 2.2142 D(x): 0.6323 D(G(z)): 0.0492 / 0.0485\n", + "[493/1600][0/8] Loss_D: 0.6870 Loss_G: 2.1332 D(x): 0.5984 D(G(z)): 0.0532 / 0.0560\n", + "[493/1600][2/8] Loss_D: 0.7023 Loss_G: 2.1143 D(x): 0.6107 D(G(z)): 0.0535 / 0.0554\n", + "[493/1600][4/8] Loss_D: 0.7779 Loss_G: 2.0597 D(x): 0.6586 D(G(z)): 0.0632 / 0.0619\n", + "[493/1600][6/8] Loss_D: 0.7656 Loss_G: 2.0748 D(x): 0.6598 D(G(z)): 0.0608 / 0.0586\n", + "[494/1600][0/8] Loss_D: 0.7266 Loss_G: 2.1797 D(x): 0.6413 D(G(z)): 0.0520 / 0.0505\n", + "[494/1600][2/8] Loss_D: 0.6772 Loss_G: 2.0908 D(x): 0.6230 D(G(z)): 0.0575 / 0.0582\n", + "[494/1600][4/8] Loss_D: 0.6841 Loss_G: 2.0683 D(x): 0.6428 D(G(z)): 0.0591 / 0.0599\n", + "[494/1600][6/8] Loss_D: 0.6978 Loss_G: 2.1364 D(x): 0.6228 D(G(z)): 0.0537 / 0.0542\n", + "[495/1600][0/8] Loss_D: 0.7700 Loss_G: 2.1305 D(x): 0.6342 D(G(z)): 0.0541 / 0.0526\n", + "[495/1600][2/8] Loss_D: 0.7296 Loss_G: 2.1628 D(x): 0.6120 D(G(z)): 0.0535 / 0.0525\n", + "[495/1600][4/8] Loss_D: 0.7106 Loss_G: 2.1305 D(x): 0.6110 D(G(z)): 0.0525 / 0.0536\n", + "[495/1600][6/8] Loss_D: 0.6972 Loss_G: 2.1499 D(x): 0.6156 D(G(z)): 0.0526 / 0.0532\n", + "[496/1600][0/8] Loss_D: 0.7038 Loss_G: 2.1403 D(x): 0.6198 D(G(z)): 0.0538 / 0.0544\n", + "[496/1600][2/8] Loss_D: 0.6396 Loss_G: 2.0786 D(x): 0.6595 D(G(z)): 0.0571 / 0.0580\n", + "[496/1600][4/8] Loss_D: 0.7121 Loss_G: 2.0418 D(x): 0.5900 D(G(z)): 0.0594 / 0.0622\n", + "[496/1600][6/8] Loss_D: 0.7066 Loss_G: 2.0292 D(x): 0.6455 D(G(z)): 0.0628 / 0.0640\n", + "[497/1600][0/8] Loss_D: 0.7233 Loss_G: 2.0060 D(x): 0.6212 D(G(z)): 0.0652 / 0.0651\n", + "[497/1600][2/8] Loss_D: 0.7465 Loss_G: 2.0134 D(x): 0.6203 D(G(z)): 0.0659 / 0.0647\n", + "[497/1600][4/8] Loss_D: 0.7414 Loss_G: 2.0051 D(x): 0.6262 D(G(z)): 0.0645 / 0.0641\n", + "[497/1600][6/8] Loss_D: 0.6395 Loss_G: 2.0021 D(x): 0.6741 D(G(z)): 0.0640 / 0.0649\n", + "[498/1600][0/8] Loss_D: 0.7065 Loss_G: 1.9724 D(x): 0.6585 D(G(z)): 0.0701 / 0.0703\n", + "[498/1600][2/8] Loss_D: 0.6987 Loss_G: 2.0104 D(x): 0.6455 D(G(z)): 0.0655 / 0.0660\n", + "[498/1600][4/8] Loss_D: 0.6606 Loss_G: 1.9723 D(x): 0.6719 D(G(z)): 0.0685 / 0.0681\n", + "[498/1600][6/8] Loss_D: 0.6881 Loss_G: 2.0712 D(x): 0.6713 D(G(z)): 0.0599 / 0.0583\n", + "[499/1600][0/8] Loss_D: 0.7149 Loss_G: 2.0591 D(x): 0.6979 D(G(z)): 0.0622 / 0.0595\n", + "[499/1600][2/8] Loss_D: 0.7562 Loss_G: 2.0580 D(x): 0.6621 D(G(z)): 0.0625 / 0.0604\n", + "[499/1600][4/8] Loss_D: 0.6685 Loss_G: 2.0429 D(x): 0.6368 D(G(z)): 0.0602 / 0.0614\n", + "[499/1600][6/8] Loss_D: 0.7555 Loss_G: 2.0748 D(x): 0.6712 D(G(z)): 0.0620 / 0.0607\n", + "[500/1600][0/8] Loss_D: 0.7133 Loss_G: 2.1025 D(x): 0.6704 D(G(z)): 0.0580 / 0.0575\n", + "[500/1600][2/8] Loss_D: 0.6937 Loss_G: 2.1039 D(x): 0.6016 D(G(z)): 0.0544 / 0.0563\n", + "[500/1600][4/8] Loss_D: 0.7444 Loss_G: 2.0190 D(x): 0.6669 D(G(z)): 0.0646 / 0.0637\n", + "[500/1600][6/8] Loss_D: 0.7576 Loss_G: 2.0217 D(x): 0.6225 D(G(z)): 0.0658 / 0.0635\n", + "[501/1600][0/8] Loss_D: 0.6781 Loss_G: 2.0397 D(x): 0.6589 D(G(z)): 0.0617 / 0.0623\n", + "[501/1600][2/8] Loss_D: 0.7224 Loss_G: 2.0026 D(x): 0.6142 D(G(z)): 0.0669 / 0.0671\n", + "[501/1600][4/8] Loss_D: 0.6885 Loss_G: 2.0169 D(x): 0.6565 D(G(z)): 0.0635 / 0.0642\n", + "[501/1600][6/8] Loss_D: 0.6991 Loss_G: 1.9786 D(x): 0.6677 D(G(z)): 0.0671 / 0.0673\n", + "[502/1600][0/8] Loss_D: 0.7186 Loss_G: 2.0001 D(x): 0.6717 D(G(z)): 0.0660 / 0.0648\n", + "[502/1600][2/8] Loss_D: 0.7156 Loss_G: 2.0276 D(x): 0.6719 D(G(z)): 0.0637 / 0.0622\n", + "[502/1600][4/8] Loss_D: 0.7572 Loss_G: 1.9688 D(x): 0.6094 D(G(z)): 0.0686 / 0.0679\n", + "[502/1600][6/8] Loss_D: 0.7158 Loss_G: 2.0369 D(x): 0.6413 D(G(z)): 0.0633 / 0.0632\n", + "[503/1600][0/8] Loss_D: 0.7449 Loss_G: 2.0215 D(x): 0.6543 D(G(z)): 0.0644 / 0.0625\n", + "[503/1600][2/8] Loss_D: 0.7488 Loss_G: 2.0581 D(x): 0.6345 D(G(z)): 0.0616 / 0.0607\n", + "[503/1600][4/8] Loss_D: 0.6545 Loss_G: 2.0693 D(x): 0.6324 D(G(z)): 0.0594 / 0.0605\n", + "[503/1600][6/8] Loss_D: 0.7201 Loss_G: 2.0298 D(x): 0.6444 D(G(z)): 0.0638 / 0.0626\n", + "[504/1600][0/8] Loss_D: 0.6893 Loss_G: 1.9910 D(x): 0.6141 D(G(z)): 0.0656 / 0.0671\n", + "[504/1600][2/8] Loss_D: 0.6704 Loss_G: 2.0585 D(x): 0.6246 D(G(z)): 0.0584 / 0.0599\n", + "[504/1600][4/8] Loss_D: 0.7419 Loss_G: 1.9880 D(x): 0.6769 D(G(z)): 0.0659 / 0.0654\n", + "[504/1600][6/8] Loss_D: 0.6916 Loss_G: 2.0075 D(x): 0.6608 D(G(z)): 0.0660 / 0.0653\n", + "[505/1600][0/8] Loss_D: 0.6676 Loss_G: 1.9738 D(x): 0.6470 D(G(z)): 0.0661 / 0.0678\n", + "[505/1600][2/8] Loss_D: 0.7158 Loss_G: 2.0493 D(x): 0.6700 D(G(z)): 0.0621 / 0.0612\n", + "[505/1600][4/8] Loss_D: 0.7004 Loss_G: 2.0181 D(x): 0.6059 D(G(z)): 0.0633 / 0.0635\n", + "[505/1600][6/8] Loss_D: 0.6852 Loss_G: 2.0027 D(x): 0.6886 D(G(z)): 0.0660 / 0.0659\n", + "[506/1600][0/8] Loss_D: 0.7390 Loss_G: 2.0546 D(x): 0.6456 D(G(z)): 0.0621 / 0.0608\n", + "[506/1600][2/8] Loss_D: 0.6775 Loss_G: 2.1090 D(x): 0.6642 D(G(z)): 0.0588 / 0.0575\n", + "[506/1600][4/8] Loss_D: 0.7728 Loss_G: 2.0557 D(x): 0.6410 D(G(z)): 0.0622 / 0.0613\n", + "[506/1600][6/8] Loss_D: 0.6981 Loss_G: 2.0194 D(x): 0.6544 D(G(z)): 0.0637 / 0.0637\n", + "[507/1600][0/8] Loss_D: 0.7718 Loss_G: 1.9927 D(x): 0.6249 D(G(z)): 0.0664 / 0.0652\n", + "[507/1600][2/8] Loss_D: 0.6789 Loss_G: 2.0422 D(x): 0.6527 D(G(z)): 0.0635 / 0.0636\n", + "[507/1600][4/8] Loss_D: 0.7076 Loss_G: 2.0099 D(x): 0.6042 D(G(z)): 0.0639 / 0.0645\n", + "[507/1600][6/8] Loss_D: 0.6900 Loss_G: 2.0561 D(x): 0.6173 D(G(z)): 0.0600 / 0.0607\n", + "[508/1600][0/8] Loss_D: 0.6330 Loss_G: 2.0291 D(x): 0.6706 D(G(z)): 0.0617 / 0.0628\n", + "[508/1600][2/8] Loss_D: 0.6727 Loss_G: 2.0494 D(x): 0.6726 D(G(z)): 0.0613 / 0.0613\n", + "[508/1600][4/8] Loss_D: 0.7472 Loss_G: 2.0221 D(x): 0.6344 D(G(z)): 0.0667 / 0.0651\n", + "[508/1600][6/8] Loss_D: 0.7456 Loss_G: 2.0736 D(x): 0.6026 D(G(z)): 0.0619 / 0.0609\n", + "[509/1600][0/8] Loss_D: 0.6510 Loss_G: 2.1049 D(x): 0.6597 D(G(z)): 0.0572 / 0.0569\n", + "[509/1600][2/8] Loss_D: 0.6610 Loss_G: 2.0293 D(x): 0.6279 D(G(z)): 0.0604 / 0.0627\n", + "[509/1600][4/8] Loss_D: 0.7349 Loss_G: 2.0005 D(x): 0.6042 D(G(z)): 0.0652 / 0.0657\n", + "[509/1600][6/8] Loss_D: 0.7699 Loss_G: 2.0415 D(x): 0.6673 D(G(z)): 0.0643 / 0.0624\n", + "[510/1600][0/8] Loss_D: 0.6588 Loss_G: 2.0188 D(x): 0.6432 D(G(z)): 0.0620 / 0.0633\n", + "[510/1600][2/8] Loss_D: 0.7338 Loss_G: 2.0780 D(x): 0.6533 D(G(z)): 0.0586 / 0.0577\n", + "[510/1600][4/8] Loss_D: 0.7674 Loss_G: 2.0054 D(x): 0.6186 D(G(z)): 0.0670 / 0.0647\n", + "[510/1600][6/8] Loss_D: 0.7096 Loss_G: 2.0013 D(x): 0.6037 D(G(z)): 0.0633 / 0.0643\n", + "[511/1600][0/8] Loss_D: 0.6665 Loss_G: 2.0234 D(x): 0.6325 D(G(z)): 0.0627 / 0.0646\n", + "[511/1600][2/8] Loss_D: 0.7280 Loss_G: 2.0210 D(x): 0.6666 D(G(z)): 0.0629 / 0.0621\n", + "[511/1600][4/8] Loss_D: 0.7262 Loss_G: 2.0577 D(x): 0.6389 D(G(z)): 0.0622 / 0.0605\n", + "[511/1600][6/8] Loss_D: 0.7214 Loss_G: 1.9801 D(x): 0.6458 D(G(z)): 0.0689 / 0.0684\n", + "[512/1600][0/8] Loss_D: 0.7574 Loss_G: 2.0132 D(x): 0.6084 D(G(z)): 0.0649 / 0.0648\n", + "[512/1600][2/8] Loss_D: 0.7867 Loss_G: 1.9965 D(x): 0.6421 D(G(z)): 0.0701 / 0.0664\n", + "[512/1600][4/8] Loss_D: 0.7422 Loss_G: 2.0775 D(x): 0.6375 D(G(z)): 0.0620 / 0.0598\n", + "[512/1600][6/8] Loss_D: 0.7077 Loss_G: 2.1523 D(x): 0.6049 D(G(z)): 0.0516 / 0.0517\n", + "[513/1600][0/8] Loss_D: 0.7417 Loss_G: 2.0766 D(x): 0.6166 D(G(z)): 0.0595 / 0.0604\n", + "[513/1600][2/8] Loss_D: 0.7054 Loss_G: 2.0026 D(x): 0.6230 D(G(z)): 0.0634 / 0.0646\n", + "[513/1600][4/8] Loss_D: 0.7553 Loss_G: 1.9884 D(x): 0.6275 D(G(z)): 0.0672 / 0.0663\n", + "[513/1600][6/8] Loss_D: 0.7185 Loss_G: 1.9979 D(x): 0.5949 D(G(z)): 0.0642 / 0.0664\n", + "[514/1600][0/8] Loss_D: 0.7529 Loss_G: 1.9594 D(x): 0.6298 D(G(z)): 0.0707 / 0.0692\n", + "[514/1600][2/8] Loss_D: 0.7641 Loss_G: 2.1228 D(x): 0.6150 D(G(z)): 0.0584 / 0.0560\n", + "[514/1600][4/8] Loss_D: 0.6982 Loss_G: 2.1053 D(x): 0.6372 D(G(z)): 0.0558 / 0.0552\n", + "[514/1600][6/8] Loss_D: 0.6819 Loss_G: 2.0859 D(x): 0.6113 D(G(z)): 0.0558 / 0.0577\n", + "[515/1600][0/8] Loss_D: 0.7396 Loss_G: 2.0352 D(x): 0.6071 D(G(z)): 0.0622 / 0.0620\n", + "[515/1600][2/8] Loss_D: 0.7040 Loss_G: 2.1016 D(x): 0.6296 D(G(z)): 0.0556 / 0.0558\n", + "[515/1600][4/8] Loss_D: 0.6841 Loss_G: 2.0392 D(x): 0.6336 D(G(z)): 0.0603 / 0.0610\n", + "[515/1600][6/8] Loss_D: 0.7154 Loss_G: 2.0230 D(x): 0.6025 D(G(z)): 0.0629 / 0.0635\n", + "[516/1600][0/8] Loss_D: 0.6764 Loss_G: 2.0604 D(x): 0.6668 D(G(z)): 0.0598 / 0.0605\n", + "[516/1600][2/8] Loss_D: 0.7373 Loss_G: 2.0315 D(x): 0.6408 D(G(z)): 0.0638 / 0.0623\n", + "[516/1600][4/8] Loss_D: 0.6702 Loss_G: 2.0694 D(x): 0.6259 D(G(z)): 0.0598 / 0.0608\n", + "[516/1600][6/8] Loss_D: 0.7727 Loss_G: 2.0597 D(x): 0.6399 D(G(z)): 0.0610 / 0.0604\n", + "[517/1600][0/8] Loss_D: 0.7411 Loss_G: 2.0840 D(x): 0.6434 D(G(z)): 0.0592 / 0.0574\n", + "[517/1600][2/8] Loss_D: 0.7674 Loss_G: 2.0656 D(x): 0.6445 D(G(z)): 0.0614 / 0.0596\n", + "[517/1600][4/8] Loss_D: 0.6856 Loss_G: 2.0990 D(x): 0.6173 D(G(z)): 0.0549 / 0.0567\n", + "[517/1600][6/8] Loss_D: 0.6928 Loss_G: 1.9573 D(x): 0.6784 D(G(z)): 0.0677 / 0.0683\n", + "[518/1600][0/8] Loss_D: 0.7834 Loss_G: 1.9309 D(x): 0.6129 D(G(z)): 0.0743 / 0.0719\n", + "[518/1600][2/8] Loss_D: 0.7216 Loss_G: 1.9817 D(x): 0.6491 D(G(z)): 0.0667 / 0.0657\n", + "[518/1600][4/8] Loss_D: 0.7468 Loss_G: 2.0424 D(x): 0.6113 D(G(z)): 0.0625 / 0.0621\n", + "[518/1600][6/8] Loss_D: 0.7039 Loss_G: 1.9570 D(x): 0.6518 D(G(z)): 0.0684 / 0.0691\n", + "[519/1600][0/8] Loss_D: 0.7684 Loss_G: 2.0503 D(x): 0.6274 D(G(z)): 0.0608 / 0.0590\n", + "[519/1600][2/8] Loss_D: 0.7773 Loss_G: 2.0021 D(x): 0.6777 D(G(z)): 0.0679 / 0.0654\n", + "[519/1600][4/8] Loss_D: 0.7174 Loss_G: 2.0066 D(x): 0.6186 D(G(z)): 0.0650 / 0.0654\n", + "[519/1600][6/8] Loss_D: 0.7105 Loss_G: 2.0387 D(x): 0.6031 D(G(z)): 0.0614 / 0.0626\n", + "[520/1600][0/8] Loss_D: 0.7817 Loss_G: 1.9969 D(x): 0.6343 D(G(z)): 0.0681 / 0.0667\n", + "[520/1600][2/8] Loss_D: 0.7167 Loss_G: 2.0448 D(x): 0.6608 D(G(z)): 0.0644 / 0.0620\n", + "[520/1600][4/8] Loss_D: 0.7761 Loss_G: 2.0336 D(x): 0.6410 D(G(z)): 0.0638 / 0.0615\n", + "[520/1600][6/8] Loss_D: 0.6834 Loss_G: 2.0149 D(x): 0.6172 D(G(z)): 0.0630 / 0.0642\n", + "[521/1600][0/8] Loss_D: 0.7766 Loss_G: 1.9800 D(x): 0.6072 D(G(z)): 0.0686 / 0.0689\n", + "[521/1600][2/8] Loss_D: 0.7764 Loss_G: 1.9931 D(x): 0.6025 D(G(z)): 0.0682 / 0.0659\n", + "[521/1600][4/8] Loss_D: 0.6544 Loss_G: 2.1060 D(x): 0.6568 D(G(z)): 0.0555 / 0.0555\n", + "[521/1600][6/8] Loss_D: 0.6979 Loss_G: 2.0246 D(x): 0.6074 D(G(z)): 0.0604 / 0.0627\n", + "[522/1600][0/8] Loss_D: 0.6474 Loss_G: 1.9362 D(x): 0.6605 D(G(z)): 0.0695 / 0.0718\n", + "[522/1600][2/8] Loss_D: 0.6884 Loss_G: 1.8780 D(x): 0.6535 D(G(z)): 0.0780 / 0.0787\n", + "[522/1600][4/8] Loss_D: 0.6705 Loss_G: 1.9366 D(x): 0.6437 D(G(z)): 0.0705 / 0.0714\n", + "[522/1600][6/8] Loss_D: 0.7455 Loss_G: 1.8944 D(x): 0.6012 D(G(z)): 0.0754 / 0.0762\n", + "[523/1600][0/8] Loss_D: 0.6976 Loss_G: 1.8724 D(x): 0.6261 D(G(z)): 0.0756 / 0.0774\n", + "[523/1600][2/8] Loss_D: 0.7736 Loss_G: 1.8249 D(x): 0.6447 D(G(z)): 0.0884 / 0.0851\n", + "[523/1600][4/8] Loss_D: 0.7256 Loss_G: 1.9045 D(x): 0.5955 D(G(z)): 0.0776 / 0.0774\n", + "[523/1600][6/8] Loss_D: 0.7177 Loss_G: 1.9258 D(x): 0.6599 D(G(z)): 0.0734 / 0.0715\n", + "[524/1600][0/8] Loss_D: 0.7195 Loss_G: 1.9684 D(x): 0.6080 D(G(z)): 0.0678 / 0.0679\n", + "[524/1600][2/8] Loss_D: 0.7438 Loss_G: 1.9595 D(x): 0.6223 D(G(z)): 0.0709 / 0.0696\n", + "[524/1600][4/8] Loss_D: 0.7588 Loss_G: 1.9624 D(x): 0.6447 D(G(z)): 0.0708 / 0.0689\n", + "[524/1600][6/8] Loss_D: 0.7418 Loss_G: 1.9474 D(x): 0.5992 D(G(z)): 0.0721 / 0.0725\n", + "[525/1600][0/8] Loss_D: 0.7570 Loss_G: 1.9354 D(x): 0.6366 D(G(z)): 0.0729 / 0.0719\n", + "[525/1600][2/8] Loss_D: 0.7169 Loss_G: 1.8594 D(x): 0.6375 D(G(z)): 0.0786 / 0.0795\n", + "[525/1600][4/8] Loss_D: 0.6783 Loss_G: 1.8457 D(x): 0.6394 D(G(z)): 0.0789 / 0.0811\n", + "[525/1600][6/8] Loss_D: 0.7131 Loss_G: 1.8654 D(x): 0.6602 D(G(z)): 0.0810 / 0.0806\n", + "[526/1600][0/8] Loss_D: 0.7251 Loss_G: 1.8483 D(x): 0.6802 D(G(z)): 0.0824 / 0.0807\n", + "[526/1600][2/8] Loss_D: 0.7332 Loss_G: 1.9178 D(x): 0.6397 D(G(z)): 0.0758 / 0.0738\n", + "[526/1600][4/8] Loss_D: 0.7478 Loss_G: 1.8572 D(x): 0.5934 D(G(z)): 0.0829 / 0.0816\n", + "[526/1600][6/8] Loss_D: 0.7375 Loss_G: 2.0141 D(x): 0.6440 D(G(z)): 0.0658 / 0.0644\n", + "[527/1600][0/8] Loss_D: 0.6865 Loss_G: 1.9285 D(x): 0.6463 D(G(z)): 0.0730 / 0.0727\n", + "[527/1600][2/8] Loss_D: 0.7152 Loss_G: 1.9380 D(x): 0.6228 D(G(z)): 0.0694 / 0.0700\n", + "[527/1600][4/8] Loss_D: 0.7719 Loss_G: 1.9684 D(x): 0.6449 D(G(z)): 0.0691 / 0.0673\n", + "[527/1600][6/8] Loss_D: 0.7596 Loss_G: 1.9775 D(x): 0.5950 D(G(z)): 0.0704 / 0.0689\n", + "[528/1600][0/8] Loss_D: 0.7788 Loss_G: 1.9732 D(x): 0.6096 D(G(z)): 0.0684 / 0.0661\n", + "[528/1600][2/8] Loss_D: 0.7560 Loss_G: 1.9492 D(x): 0.5779 D(G(z)): 0.0677 / 0.0691\n", + "[528/1600][4/8] Loss_D: 0.7524 Loss_G: 1.9841 D(x): 0.6373 D(G(z)): 0.0679 / 0.0672\n", + "[528/1600][6/8] Loss_D: 0.7665 Loss_G: 1.9449 D(x): 0.6142 D(G(z)): 0.0715 / 0.0709\n", + "[529/1600][0/8] Loss_D: 0.7801 Loss_G: 1.9643 D(x): 0.5649 D(G(z)): 0.0695 / 0.0697\n", + "[529/1600][2/8] Loss_D: 0.7283 Loss_G: 1.8264 D(x): 0.6250 D(G(z)): 0.0842 / 0.0850\n", + "[529/1600][4/8] Loss_D: 0.7227 Loss_G: 1.8269 D(x): 0.6357 D(G(z)): 0.0833 / 0.0847\n", + "[529/1600][6/8] Loss_D: 0.6782 Loss_G: 1.7948 D(x): 0.6403 D(G(z)): 0.0845 / 0.0871\n", + "[530/1600][0/8] Loss_D: 0.6709 Loss_G: 1.7650 D(x): 0.6560 D(G(z)): 0.0911 / 0.0911\n", + "[530/1600][2/8] Loss_D: 0.7735 Loss_G: 1.7631 D(x): 0.6818 D(G(z)): 0.0949 / 0.0905\n", + "[530/1600][4/8] Loss_D: 0.7420 Loss_G: 1.8450 D(x): 0.6545 D(G(z)): 0.0840 / 0.0803\n", + "[530/1600][6/8] Loss_D: 0.7906 Loss_G: 1.8418 D(x): 0.6243 D(G(z)): 0.0836 / 0.0809\n", + "[531/1600][0/8] Loss_D: 0.7860 Loss_G: 1.8731 D(x): 0.6244 D(G(z)): 0.0814 / 0.0781\n", + "[531/1600][2/8] Loss_D: 0.6993 Loss_G: 1.8969 D(x): 0.6371 D(G(z)): 0.0739 / 0.0749\n", + "[531/1600][4/8] Loss_D: 0.7574 Loss_G: 1.9347 D(x): 0.6204 D(G(z)): 0.0751 / 0.0733\n", + "[531/1600][6/8] Loss_D: 0.7591 Loss_G: 1.8756 D(x): 0.5996 D(G(z)): 0.0771 / 0.0776\n", + "[532/1600][0/8] Loss_D: 0.6942 Loss_G: 1.8569 D(x): 0.6161 D(G(z)): 0.0793 / 0.0808\n", + "[532/1600][2/8] Loss_D: 0.7433 Loss_G: 1.8439 D(x): 0.6491 D(G(z)): 0.0811 / 0.0794\n", + "[532/1600][4/8] Loss_D: 0.7679 Loss_G: 1.8939 D(x): 0.6164 D(G(z)): 0.0780 / 0.0756\n", + "[532/1600][6/8] Loss_D: 0.7281 Loss_G: 1.9389 D(x): 0.5707 D(G(z)): 0.0697 / 0.0712\n", + "[533/1600][0/8] Loss_D: 0.6920 Loss_G: 1.9039 D(x): 0.6451 D(G(z)): 0.0746 / 0.0751\n", + "[533/1600][2/8] Loss_D: 0.7438 Loss_G: 1.8406 D(x): 0.5600 D(G(z)): 0.0783 / 0.0821\n", + "[533/1600][4/8] Loss_D: 0.7571 Loss_G: 1.7523 D(x): 0.6260 D(G(z)): 0.0958 / 0.0949\n", + "[533/1600][6/8] Loss_D: 0.7472 Loss_G: 1.8892 D(x): 0.6049 D(G(z)): 0.0793 / 0.0785\n", + "[534/1600][0/8] Loss_D: 0.7387 Loss_G: 1.8176 D(x): 0.6005 D(G(z)): 0.0833 / 0.0843\n", + "[534/1600][2/8] Loss_D: 0.7447 Loss_G: 1.7741 D(x): 0.6271 D(G(z)): 0.0904 / 0.0907\n", + "[534/1600][4/8] Loss_D: 0.7928 Loss_G: 1.7659 D(x): 0.6238 D(G(z)): 0.0935 / 0.0904\n", + "[534/1600][6/8] Loss_D: 0.7655 Loss_G: 1.8058 D(x): 0.5730 D(G(z)): 0.0865 / 0.0866\n", + "[535/1600][0/8] Loss_D: 0.7666 Loss_G: 1.7672 D(x): 0.6189 D(G(z)): 0.0930 / 0.0925\n", + "[535/1600][2/8] Loss_D: 0.7670 Loss_G: 1.8028 D(x): 0.5820 D(G(z)): 0.0872 / 0.0875\n", + "[535/1600][4/8] Loss_D: 0.7253 Loss_G: 1.8132 D(x): 0.6400 D(G(z)): 0.0858 / 0.0860\n", + "[535/1600][6/8] Loss_D: 0.7958 Loss_G: 1.7942 D(x): 0.6194 D(G(z)): 0.0918 / 0.0880\n", + "[536/1600][0/8] Loss_D: 0.7839 Loss_G: 1.8674 D(x): 0.6630 D(G(z)): 0.0833 / 0.0795\n", + "[536/1600][2/8] Loss_D: 0.7378 Loss_G: 1.8814 D(x): 0.6332 D(G(z)): 0.0767 / 0.0764\n", + "[536/1600][4/8] Loss_D: 0.7522 Loss_G: 1.8129 D(x): 0.6169 D(G(z)): 0.0855 / 0.0859\n", + "[536/1600][6/8] Loss_D: 0.7778 Loss_G: 1.8106 D(x): 0.6253 D(G(z)): 0.0882 / 0.0850\n", + "[537/1600][0/8] Loss_D: 0.7289 Loss_G: 1.8165 D(x): 0.6378 D(G(z)): 0.0850 / 0.0851\n", + "[537/1600][2/8] Loss_D: 0.7639 Loss_G: 1.9003 D(x): 0.6146 D(G(z)): 0.0760 / 0.0745\n", + "[537/1600][4/8] Loss_D: 0.7622 Loss_G: 1.7412 D(x): 0.6465 D(G(z)): 0.0965 / 0.0953\n", + "[537/1600][6/8] Loss_D: 0.7405 Loss_G: 1.7654 D(x): 0.6310 D(G(z)): 0.0921 / 0.0909\n", + "[538/1600][0/8] Loss_D: 0.7765 Loss_G: 1.7451 D(x): 0.6111 D(G(z)): 0.0949 / 0.0946\n", + "[538/1600][2/8] Loss_D: 0.7590 Loss_G: 1.7940 D(x): 0.6055 D(G(z)): 0.0879 / 0.0879\n", + "[538/1600][4/8] Loss_D: 0.7625 Loss_G: 1.7770 D(x): 0.5960 D(G(z)): 0.0923 / 0.0909\n", + "[538/1600][6/8] Loss_D: 0.7377 Loss_G: 1.8509 D(x): 0.6490 D(G(z)): 0.0803 / 0.0794\n", + "[539/1600][0/8] Loss_D: 0.7453 Loss_G: 1.7724 D(x): 0.6035 D(G(z)): 0.0888 / 0.0889\n", + "[539/1600][2/8] Loss_D: 0.6823 Loss_G: 1.7883 D(x): 0.6444 D(G(z)): 0.0876 / 0.0884\n", + "[539/1600][4/8] Loss_D: 0.6924 Loss_G: 1.8047 D(x): 0.6325 D(G(z)): 0.0866 / 0.0873\n", + "[539/1600][6/8] Loss_D: 0.6984 Loss_G: 1.7885 D(x): 0.6237 D(G(z)): 0.0866 / 0.0876\n", + "[540/1600][0/8] Loss_D: 0.8174 Loss_G: 1.7622 D(x): 0.6557 D(G(z)): 0.0982 / 0.0931\n", + "[540/1600][2/8] Loss_D: 0.7961 Loss_G: 1.7990 D(x): 0.5531 D(G(z)): 0.0888 / 0.0870\n", + "[540/1600][4/8] Loss_D: 0.8038 Loss_G: 1.7838 D(x): 0.6024 D(G(z)): 0.0939 / 0.0914\n", + "[540/1600][6/8] Loss_D: 0.7225 Loss_G: 1.8473 D(x): 0.5960 D(G(z)): 0.0810 / 0.0810\n", + "[541/1600][0/8] Loss_D: 0.7111 Loss_G: 1.7836 D(x): 0.6258 D(G(z)): 0.0869 / 0.0882\n", + "[541/1600][2/8] Loss_D: 0.7657 Loss_G: 1.8111 D(x): 0.5712 D(G(z)): 0.0850 / 0.0863\n", + "[541/1600][4/8] Loss_D: 0.7287 Loss_G: 1.8039 D(x): 0.6085 D(G(z)): 0.0868 / 0.0873\n", + "[541/1600][6/8] Loss_D: 0.7765 Loss_G: 1.7892 D(x): 0.6231 D(G(z)): 0.0898 / 0.0869\n", + "[542/1600][0/8] Loss_D: 0.7587 Loss_G: 1.7805 D(x): 0.5910 D(G(z)): 0.0892 / 0.0890\n", + "[542/1600][2/8] Loss_D: 0.7630 Loss_G: 1.7553 D(x): 0.6194 D(G(z)): 0.0965 / 0.0948\n", + "[542/1600][4/8] Loss_D: 0.7389 Loss_G: 1.8223 D(x): 0.6188 D(G(z)): 0.0866 / 0.0842\n", + "[542/1600][6/8] Loss_D: 0.7796 Loss_G: 1.8403 D(x): 0.5944 D(G(z)): 0.0824 / 0.0816\n", + "[543/1600][0/8] Loss_D: 0.7589 Loss_G: 1.8102 D(x): 0.5886 D(G(z)): 0.0837 / 0.0854\n", + "[543/1600][2/8] Loss_D: 0.7922 Loss_G: 1.7865 D(x): 0.5862 D(G(z)): 0.0897 / 0.0870\n", + "[543/1600][4/8] Loss_D: 0.8132 Loss_G: 1.7053 D(x): 0.5925 D(G(z)): 0.1031 / 0.0993\n", + "[543/1600][6/8] Loss_D: 0.7868 Loss_G: 1.7835 D(x): 0.5699 D(G(z)): 0.0898 / 0.0884\n", + "[544/1600][0/8] Loss_D: 0.7863 Loss_G: 1.7569 D(x): 0.6080 D(G(z)): 0.0953 / 0.0927\n", + "[544/1600][2/8] Loss_D: 0.7748 Loss_G: 1.8089 D(x): 0.6108 D(G(z)): 0.0884 / 0.0868\n", + "[544/1600][4/8] Loss_D: 0.6853 Loss_G: 1.7675 D(x): 0.6395 D(G(z)): 0.0906 / 0.0924\n", + "[544/1600][6/8] Loss_D: 0.7084 Loss_G: 1.7332 D(x): 0.6179 D(G(z)): 0.0948 / 0.0974\n", + "[545/1600][0/8] Loss_D: 0.8362 Loss_G: 1.6501 D(x): 0.5486 D(G(z)): 0.1085 / 0.1084\n", + "[545/1600][2/8] Loss_D: 0.8265 Loss_G: 1.6202 D(x): 0.5998 D(G(z)): 0.1174 / 0.1135\n", + "[545/1600][4/8] Loss_D: 0.7546 Loss_G: 1.6740 D(x): 0.6102 D(G(z)): 0.1038 / 0.1038\n", + "[545/1600][6/8] Loss_D: 0.7621 Loss_G: 1.7350 D(x): 0.6058 D(G(z)): 0.0978 / 0.0974\n", + "[546/1600][0/8] Loss_D: 0.7899 Loss_G: 1.6483 D(x): 0.6255 D(G(z)): 0.1106 / 0.1089\n", + "[546/1600][2/8] Loss_D: 0.7503 Loss_G: 1.6642 D(x): 0.5902 D(G(z)): 0.1041 / 0.1044\n", + "[546/1600][4/8] Loss_D: 0.7742 Loss_G: 1.6975 D(x): 0.6181 D(G(z)): 0.1036 / 0.1008\n", + "[546/1600][6/8] Loss_D: 0.8062 Loss_G: 1.6927 D(x): 0.5821 D(G(z)): 0.1055 / 0.1019\n", + "[547/1600][0/8] Loss_D: 0.7809 Loss_G: 1.7638 D(x): 0.5904 D(G(z)): 0.0948 / 0.0928\n", + "[547/1600][2/8] Loss_D: 0.8073 Loss_G: 1.7414 D(x): 0.5393 D(G(z)): 0.0960 / 0.0965\n", + "[547/1600][4/8] Loss_D: 0.7807 Loss_G: 1.7062 D(x): 0.5926 D(G(z)): 0.0989 / 0.0993\n", + "[547/1600][6/8] Loss_D: 0.8056 Loss_G: 1.7189 D(x): 0.5779 D(G(z)): 0.1010 / 0.0975\n", + "[548/1600][0/8] Loss_D: 0.8136 Loss_G: 1.7097 D(x): 0.5569 D(G(z)): 0.1007 / 0.0998\n", + "[548/1600][2/8] Loss_D: 0.8058 Loss_G: 1.6436 D(x): 0.5950 D(G(z)): 0.1087 / 0.1077\n", + "[548/1600][4/8] Loss_D: 0.8224 Loss_G: 1.7202 D(x): 0.5545 D(G(z)): 0.0992 / 0.0968\n", + "[548/1600][6/8] Loss_D: 0.8129 Loss_G: 1.7195 D(x): 0.5460 D(G(z)): 0.0974 / 0.0963\n", + "[549/1600][0/8] Loss_D: 0.8082 Loss_G: 1.6499 D(x): 0.5567 D(G(z)): 0.1068 / 0.1075\n", + "[549/1600][2/8] Loss_D: 0.8102 Loss_G: 1.5947 D(x): 0.5709 D(G(z)): 0.1161 / 0.1167\n", + "[549/1600][4/8] Loss_D: 0.7431 Loss_G: 1.5510 D(x): 0.6141 D(G(z)): 0.1227 / 0.1245\n", + "[549/1600][6/8] Loss_D: 0.8075 Loss_G: 1.6696 D(x): 0.6532 D(G(z)): 0.1087 / 0.1046\n", + "[550/1600][0/8] Loss_D: 0.7977 Loss_G: 1.5741 D(x): 0.5943 D(G(z)): 0.1210 / 0.1181\n", + "[550/1600][2/8] Loss_D: 0.8222 Loss_G: 1.6327 D(x): 0.5953 D(G(z)): 0.1148 / 0.1093\n", + "[550/1600][4/8] Loss_D: 0.8035 Loss_G: 1.7032 D(x): 0.5875 D(G(z)): 0.1019 / 0.0991\n", + "[550/1600][6/8] Loss_D: 0.8220 Loss_G: 1.7008 D(x): 0.5233 D(G(z)): 0.0985 / 0.0976\n", + "[551/1600][0/8] Loss_D: 0.7507 Loss_G: 1.7046 D(x): 0.5925 D(G(z)): 0.1004 / 0.1006\n", + "[551/1600][2/8] Loss_D: 0.8328 Loss_G: 1.6492 D(x): 0.5141 D(G(z)): 0.1048 / 0.1084\n", + "[551/1600][4/8] Loss_D: 0.8253 Loss_G: 1.6011 D(x): 0.5750 D(G(z)): 0.1185 / 0.1151\n", + "[551/1600][6/8] Loss_D: 0.7525 Loss_G: 1.7038 D(x): 0.5926 D(G(z)): 0.1028 / 0.1024\n", + "[552/1600][0/8] Loss_D: 0.7686 Loss_G: 1.6507 D(x): 0.5914 D(G(z)): 0.1061 / 0.1068\n", + "[552/1600][2/8] Loss_D: 0.8237 Loss_G: 1.6737 D(x): 0.5340 D(G(z)): 0.1037 / 0.1033\n", + "[552/1600][4/8] Loss_D: 0.7558 Loss_G: 1.6198 D(x): 0.6016 D(G(z)): 0.1128 / 0.1126\n", + "[552/1600][6/8] Loss_D: 0.7999 Loss_G: 1.6312 D(x): 0.5731 D(G(z)): 0.1133 / 0.1119\n", + "[553/1600][0/8] Loss_D: 0.7608 Loss_G: 1.6749 D(x): 0.5971 D(G(z)): 0.1038 / 0.1032\n", + "[553/1600][2/8] Loss_D: 0.7872 Loss_G: 1.6410 D(x): 0.5886 D(G(z)): 0.1085 / 0.1095\n", + "[553/1600][4/8] Loss_D: 0.8044 Loss_G: 1.5522 D(x): 0.5905 D(G(z)): 0.1254 / 0.1261\n", + "[553/1600][6/8] Loss_D: 0.8415 Loss_G: 1.5872 D(x): 0.6320 D(G(z)): 0.1262 / 0.1186\n", + "[554/1600][0/8] Loss_D: 0.7929 Loss_G: 1.6314 D(x): 0.6114 D(G(z)): 0.1129 / 0.1100\n", + "[554/1600][2/8] Loss_D: 0.8295 Loss_G: 1.6778 D(x): 0.5833 D(G(z)): 0.1070 / 0.1040\n", + "[554/1600][4/8] Loss_D: 0.8095 Loss_G: 1.6759 D(x): 0.5700 D(G(z)): 0.1059 / 0.1038\n", + "[554/1600][6/8] Loss_D: 0.7802 Loss_G: 1.6754 D(x): 0.5895 D(G(z)): 0.1050 / 0.1043\n", + "[555/1600][0/8] Loss_D: 0.8066 Loss_G: 1.7486 D(x): 0.5916 D(G(z)): 0.0948 / 0.0923\n", + "[555/1600][2/8] Loss_D: 0.7501 Loss_G: 1.6831 D(x): 0.6363 D(G(z)): 0.1050 / 0.1025\n", + "[555/1600][4/8] Loss_D: 0.7789 Loss_G: 1.6296 D(x): 0.5798 D(G(z)): 0.1084 / 0.1107\n", + "[555/1600][6/8] Loss_D: 0.8246 Loss_G: 1.6277 D(x): 0.5982 D(G(z)): 0.1133 / 0.1121\n", + "[556/1600][0/8] Loss_D: 0.7962 Loss_G: 1.6011 D(x): 0.5741 D(G(z)): 0.1136 / 0.1129\n", + "[556/1600][2/8] Loss_D: 0.7282 Loss_G: 1.6588 D(x): 0.6414 D(G(z)): 0.1068 / 0.1050\n", + "[556/1600][4/8] Loss_D: 0.7921 Loss_G: 1.7072 D(x): 0.6202 D(G(z)): 0.1007 / 0.0987\n", + "[556/1600][6/8] Loss_D: 0.7355 Loss_G: 1.6729 D(x): 0.6002 D(G(z)): 0.1025 / 0.1037\n", + "[557/1600][0/8] Loss_D: 0.7981 Loss_G: 1.6987 D(x): 0.5979 D(G(z)): 0.1028 / 0.0998\n", + "[557/1600][2/8] Loss_D: 0.7814 Loss_G: 1.7517 D(x): 0.5968 D(G(z)): 0.0946 / 0.0924\n", + "[557/1600][4/8] Loss_D: 0.7989 Loss_G: 1.6460 D(x): 0.5678 D(G(z)): 0.1081 / 0.1099\n", + "[557/1600][6/8] Loss_D: 0.7543 Loss_G: 1.7368 D(x): 0.6339 D(G(z)): 0.0935 / 0.0933\n", + "[558/1600][0/8] Loss_D: 0.7297 Loss_G: 1.6808 D(x): 0.6176 D(G(z)): 0.0996 / 0.1016\n", + "[558/1600][2/8] Loss_D: 0.7853 Loss_G: 1.6213 D(x): 0.6192 D(G(z)): 0.1177 / 0.1146\n", + "[558/1600][4/8] Loss_D: 0.7818 Loss_G: 1.6464 D(x): 0.5908 D(G(z)): 0.1069 / 0.1060\n", + "[558/1600][6/8] Loss_D: 0.8037 Loss_G: 1.6623 D(x): 0.6054 D(G(z)): 0.1074 / 0.1044\n", + "[559/1600][0/8] Loss_D: 0.8038 Loss_G: 1.7018 D(x): 0.6105 D(G(z)): 0.1025 / 0.0988\n", + "[559/1600][2/8] Loss_D: 0.7974 Loss_G: 1.7636 D(x): 0.6114 D(G(z)): 0.0943 / 0.0903\n", + "[559/1600][4/8] Loss_D: 0.8134 Loss_G: 1.7523 D(x): 0.5716 D(G(z)): 0.0968 / 0.0944\n", + "[559/1600][6/8] Loss_D: 0.8071 Loss_G: 1.7119 D(x): 0.5270 D(G(z)): 0.0975 / 0.0986\n", + "[560/1600][0/8] Loss_D: 0.7793 Loss_G: 1.7928 D(x): 0.5792 D(G(z)): 0.0842 / 0.0859\n", + "[560/1600][2/8] Loss_D: 0.7934 Loss_G: 1.6710 D(x): 0.6148 D(G(z)): 0.1041 / 0.1023\n", + "[560/1600][4/8] Loss_D: 0.8264 Loss_G: 1.7175 D(x): 0.5972 D(G(z)): 0.1025 / 0.0978\n", + "[560/1600][6/8] Loss_D: 0.7763 Loss_G: 1.7737 D(x): 0.5570 D(G(z)): 0.0882 / 0.0887\n", + "[561/1600][0/8] Loss_D: 0.8275 Loss_G: 1.5891 D(x): 0.5608 D(G(z)): 0.1199 / 0.1197\n", + "[561/1600][2/8] Loss_D: 0.7866 Loss_G: 1.6948 D(x): 0.5835 D(G(z)): 0.1017 / 0.1019\n", + "[561/1600][4/8] Loss_D: 0.7879 Loss_G: 1.6105 D(x): 0.5871 D(G(z)): 0.1134 / 0.1141\n", + "[561/1600][6/8] Loss_D: 0.8068 Loss_G: 1.7032 D(x): 0.6119 D(G(z)): 0.1024 / 0.0988\n", + "[562/1600][0/8] Loss_D: 0.7786 Loss_G: 1.7185 D(x): 0.6029 D(G(z)): 0.0982 / 0.0962\n", + "[562/1600][2/8] Loss_D: 0.7635 Loss_G: 1.6642 D(x): 0.5874 D(G(z)): 0.1027 / 0.1050\n", + "[562/1600][4/8] Loss_D: 0.8144 Loss_G: 1.7181 D(x): 0.5881 D(G(z)): 0.0987 / 0.0971\n", + "[562/1600][6/8] Loss_D: 0.7334 Loss_G: 1.6447 D(x): 0.6539 D(G(z)): 0.1121 / 0.1097\n", + "[563/1600][0/8] Loss_D: 0.7948 Loss_G: 1.7134 D(x): 0.5696 D(G(z)): 0.0994 / 0.0975\n", + "[563/1600][2/8] Loss_D: 0.7968 Loss_G: 1.7464 D(x): 0.5913 D(G(z)): 0.0966 / 0.0938\n", + "[563/1600][4/8] Loss_D: 0.7262 Loss_G: 1.7447 D(x): 0.6030 D(G(z)): 0.0923 / 0.0925\n", + "[563/1600][6/8] Loss_D: 0.7875 Loss_G: 1.6145 D(x): 0.5738 D(G(z)): 0.1105 / 0.1130\n", + "[564/1600][0/8] Loss_D: 0.7754 Loss_G: 1.7018 D(x): 0.5985 D(G(z)): 0.1003 / 0.0991\n", + "[564/1600][2/8] Loss_D: 0.8129 Loss_G: 1.7609 D(x): 0.5442 D(G(z)): 0.0909 / 0.0898\n", + "[564/1600][4/8] Loss_D: 0.8035 Loss_G: 1.7543 D(x): 0.6315 D(G(z)): 0.0957 / 0.0907\n", + "[564/1600][6/8] Loss_D: 0.7923 Loss_G: 1.7792 D(x): 0.5771 D(G(z)): 0.0895 / 0.0872\n", + "[565/1600][0/8] Loss_D: 0.7968 Loss_G: 1.7321 D(x): 0.5560 D(G(z)): 0.0948 / 0.0955\n", + "[565/1600][2/8] Loss_D: 0.7518 Loss_G: 1.7479 D(x): 0.5745 D(G(z)): 0.0888 / 0.0930\n", + "[565/1600][4/8] Loss_D: 0.8206 Loss_G: 1.6402 D(x): 0.5482 D(G(z)): 0.1069 / 0.1086\n", + "[565/1600][6/8] Loss_D: 0.8003 Loss_G: 1.7685 D(x): 0.5997 D(G(z)): 0.0940 / 0.0909\n", + "[566/1600][0/8] Loss_D: 0.8127 Loss_G: 1.7263 D(x): 0.6072 D(G(z)): 0.0989 / 0.0967\n", + "[566/1600][2/8] Loss_D: 0.8052 Loss_G: 1.6702 D(x): 0.5846 D(G(z)): 0.1048 / 0.1051\n", + "[566/1600][4/8] Loss_D: 0.7558 Loss_G: 1.6005 D(x): 0.5936 D(G(z)): 0.1163 / 0.1181\n", + "[566/1600][6/8] Loss_D: 0.8189 Loss_G: 1.6864 D(x): 0.6322 D(G(z)): 0.1044 / 0.1008\n", + "[567/1600][0/8] Loss_D: 0.7402 Loss_G: 1.7152 D(x): 0.5978 D(G(z)): 0.0953 / 0.0964\n", + "[567/1600][2/8] Loss_D: 0.7614 Loss_G: 1.6012 D(x): 0.6006 D(G(z)): 0.1146 / 0.1155\n", + "[567/1600][4/8] Loss_D: 0.7841 Loss_G: 1.5951 D(x): 0.6301 D(G(z)): 0.1178 / 0.1157\n", + "[567/1600][6/8] Loss_D: 0.8289 Loss_G: 1.7027 D(x): 0.6277 D(G(z)): 0.1068 / 0.1004\n", + "[568/1600][0/8] Loss_D: 0.7960 Loss_G: 1.7069 D(x): 0.5719 D(G(z)): 0.0997 / 0.0992\n", + "[568/1600][2/8] Loss_D: 0.8066 Loss_G: 1.7189 D(x): 0.5339 D(G(z)): 0.0961 / 0.0989\n", + "[568/1600][4/8] Loss_D: 0.8017 Loss_G: 1.6140 D(x): 0.6195 D(G(z)): 0.1139 / 0.1125\n", + "[568/1600][6/8] Loss_D: 0.8092 Loss_G: 1.7017 D(x): 0.5588 D(G(z)): 0.1008 / 0.0997\n", + "[569/1600][0/8] Loss_D: 0.7963 Loss_G: 1.7488 D(x): 0.6067 D(G(z)): 0.0958 / 0.0923\n", + "[569/1600][2/8] Loss_D: 0.7639 Loss_G: 1.7809 D(x): 0.6070 D(G(z)): 0.0894 / 0.0881\n", + "[569/1600][4/8] Loss_D: 0.7846 Loss_G: 1.7137 D(x): 0.6189 D(G(z)): 0.0999 / 0.0979\n", + "[569/1600][6/8] Loss_D: 0.8155 Loss_G: 1.6157 D(x): 0.5517 D(G(z)): 0.1117 / 0.1139\n", + "[570/1600][0/8] Loss_D: 0.8073 Loss_G: 1.6976 D(x): 0.5990 D(G(z)): 0.1034 / 0.1007\n", + "[570/1600][2/8] Loss_D: 0.7941 Loss_G: 1.7262 D(x): 0.6054 D(G(z)): 0.0981 / 0.0941\n", + "[570/1600][4/8] Loss_D: 0.7994 Loss_G: 1.7080 D(x): 0.5502 D(G(z)): 0.0950 / 0.0966\n", + "[570/1600][6/8] Loss_D: 0.8083 Loss_G: 1.6932 D(x): 0.5382 D(G(z)): 0.0990 / 0.1012\n", + "[571/1600][0/8] Loss_D: 0.7786 Loss_G: 1.6335 D(x): 0.5682 D(G(z)): 0.1046 / 0.1097\n", + "[571/1600][2/8] Loss_D: 0.7940 Loss_G: 1.6069 D(x): 0.6792 D(G(z)): 0.1179 / 0.1145\n", + "[571/1600][4/8] Loss_D: 0.7615 Loss_G: 1.6365 D(x): 0.6172 D(G(z)): 0.1113 / 0.1093\n", + "[571/1600][6/8] Loss_D: 0.7829 Loss_G: 1.6404 D(x): 0.6279 D(G(z)): 0.1142 / 0.1103\n", + "[572/1600][0/8] Loss_D: 0.7562 Loss_G: 1.6924 D(x): 0.6329 D(G(z)): 0.1014 / 0.1003\n", + "[572/1600][2/8] Loss_D: 0.8130 Loss_G: 1.7401 D(x): 0.5669 D(G(z)): 0.0946 / 0.0938\n", + "[572/1600][4/8] Loss_D: 0.7559 Loss_G: 1.6498 D(x): 0.6059 D(G(z)): 0.1046 / 0.1073\n", + "[572/1600][6/8] Loss_D: 0.7968 Loss_G: 1.6773 D(x): 0.5951 D(G(z)): 0.1047 / 0.1029\n", + "[573/1600][0/8] Loss_D: 0.8253 Loss_G: 1.6750 D(x): 0.5928 D(G(z)): 0.1073 / 0.1031\n", + "[573/1600][2/8] Loss_D: 0.7899 Loss_G: 1.7022 D(x): 0.5558 D(G(z)): 0.0986 / 0.0999\n", + "[573/1600][4/8] Loss_D: 0.7231 Loss_G: 1.6485 D(x): 0.6390 D(G(z)): 0.1059 / 0.1072\n", + "[573/1600][6/8] Loss_D: 0.7229 Loss_G: 1.6307 D(x): 0.6274 D(G(z)): 0.1074 / 0.1098\n", + "[574/1600][0/8] Loss_D: 0.8124 Loss_G: 1.5886 D(x): 0.6128 D(G(z)): 0.1187 / 0.1159\n", + "[574/1600][2/8] Loss_D: 0.8320 Loss_G: 1.6670 D(x): 0.6073 D(G(z)): 0.1092 / 0.1034\n", + "[574/1600][4/8] Loss_D: 0.8094 Loss_G: 1.7026 D(x): 0.5664 D(G(z)): 0.1014 / 0.0981\n", + "[574/1600][6/8] Loss_D: 0.7818 Loss_G: 1.7459 D(x): 0.6043 D(G(z)): 0.0962 / 0.0948\n", + "[575/1600][0/8] Loss_D: 0.7714 Loss_G: 1.7439 D(x): 0.5634 D(G(z)): 0.0906 / 0.0910\n", + "[575/1600][2/8] Loss_D: 0.7860 Loss_G: 1.6275 D(x): 0.5594 D(G(z)): 0.1048 / 0.1093\n", + "[575/1600][4/8] Loss_D: 0.7288 Loss_G: 1.6349 D(x): 0.6140 D(G(z)): 0.1053 / 0.1088\n", + "[575/1600][6/8] Loss_D: 0.7586 Loss_G: 1.6233 D(x): 0.6061 D(G(z)): 0.1110 / 0.1126\n", + "[576/1600][0/8] Loss_D: 0.7198 Loss_G: 1.5700 D(x): 0.6519 D(G(z)): 0.1183 / 0.1185\n", + "[576/1600][2/8] Loss_D: 0.7536 Loss_G: 1.5491 D(x): 0.6056 D(G(z)): 0.1243 / 0.1245\n", + "[576/1600][4/8] Loss_D: 0.7510 Loss_G: 1.5946 D(x): 0.6265 D(G(z)): 0.1180 / 0.1166\n", + "[576/1600][6/8] Loss_D: 0.8505 Loss_G: 1.5476 D(x): 0.6264 D(G(z)): 0.1300 / 0.1237\n", + "[577/1600][0/8] Loss_D: 0.7965 Loss_G: 1.6486 D(x): 0.5945 D(G(z)): 0.1140 / 0.1095\n", + "[577/1600][2/8] Loss_D: 0.8109 Loss_G: 1.7348 D(x): 0.6276 D(G(z)): 0.0967 / 0.0931\n", + "[577/1600][4/8] Loss_D: 0.7425 Loss_G: 1.7346 D(x): 0.5858 D(G(z)): 0.0925 / 0.0936\n", + "[577/1600][6/8] Loss_D: 0.7986 Loss_G: 1.6760 D(x): 0.5617 D(G(z)): 0.1028 / 0.1033\n", + "[578/1600][0/8] Loss_D: 0.7298 Loss_G: 1.6314 D(x): 0.6300 D(G(z)): 0.1082 / 0.1094\n", + "[578/1600][2/8] Loss_D: 0.8444 Loss_G: 1.6543 D(x): 0.5466 D(G(z)): 0.1082 / 0.1070\n", + "[578/1600][4/8] Loss_D: 0.7923 Loss_G: 1.6717 D(x): 0.6322 D(G(z)): 0.1056 / 0.1027\n", + "[578/1600][6/8] Loss_D: 0.7691 Loss_G: 1.6748 D(x): 0.6304 D(G(z)): 0.1052 / 0.1028\n", + "[579/1600][0/8] Loss_D: 0.8088 Loss_G: 1.7332 D(x): 0.6129 D(G(z)): 0.0978 / 0.0942\n", + "[579/1600][2/8] Loss_D: 0.7995 Loss_G: 1.7289 D(x): 0.5967 D(G(z)): 0.0988 / 0.0952\n", + "[579/1600][4/8] Loss_D: 0.8015 Loss_G: 1.7368 D(x): 0.5850 D(G(z)): 0.0957 / 0.0946\n", + "[579/1600][6/8] Loss_D: 0.7965 Loss_G: 1.6596 D(x): 0.5815 D(G(z)): 0.1034 / 0.1039\n", + "[580/1600][0/8] Loss_D: 0.7509 Loss_G: 1.6709 D(x): 0.6130 D(G(z)): 0.1028 / 0.1053\n", + "[580/1600][2/8] Loss_D: 0.8120 Loss_G: 1.6825 D(x): 0.6262 D(G(z)): 0.1056 / 0.1023\n", + "[580/1600][4/8] Loss_D: 0.7900 Loss_G: 1.6580 D(x): 0.5657 D(G(z)): 0.1052 / 0.1056\n", + "[580/1600][6/8] Loss_D: 0.7815 Loss_G: 1.7446 D(x): 0.6138 D(G(z)): 0.0939 / 0.0925\n", + "[581/1600][0/8] Loss_D: 0.7973 Loss_G: 1.7271 D(x): 0.5579 D(G(z)): 0.0970 / 0.0954\n", + "[581/1600][2/8] Loss_D: 0.7713 Loss_G: 1.7097 D(x): 0.5785 D(G(z)): 0.0966 / 0.0989\n", + "[581/1600][4/8] Loss_D: 0.7805 Loss_G: 1.6743 D(x): 0.6199 D(G(z)): 0.1028 / 0.1017\n", + "[581/1600][6/8] Loss_D: 0.7876 Loss_G: 1.6881 D(x): 0.5902 D(G(z)): 0.1016 / 0.1015\n", + "[582/1600][0/8] Loss_D: 0.7934 Loss_G: 1.6685 D(x): 0.5460 D(G(z)): 0.1006 / 0.1051\n", + "[582/1600][2/8] Loss_D: 0.8075 Loss_G: 1.6522 D(x): 0.6298 D(G(z)): 0.1083 / 0.1068\n", + "[582/1600][4/8] Loss_D: 0.7470 Loss_G: 1.5931 D(x): 0.6436 D(G(z)): 0.1170 / 0.1164\n", + "[582/1600][6/8] Loss_D: 0.8158 Loss_G: 1.6451 D(x): 0.6263 D(G(z)): 0.1130 / 0.1066\n", + "[583/1600][0/8] Loss_D: 0.7574 Loss_G: 1.6193 D(x): 0.6501 D(G(z)): 0.1143 / 0.1102\n", + "[583/1600][2/8] Loss_D: 0.7541 Loss_G: 1.7007 D(x): 0.5993 D(G(z)): 0.0969 / 0.0975\n", + "[583/1600][4/8] Loss_D: 0.8135 Loss_G: 1.6907 D(x): 0.6502 D(G(z)): 0.1036 / 0.0998\n", + "[583/1600][6/8] Loss_D: 0.8016 Loss_G: 1.7763 D(x): 0.5703 D(G(z)): 0.0886 / 0.0874\n", + "[584/1600][0/8] Loss_D: 0.8107 Loss_G: 1.6656 D(x): 0.5814 D(G(z)): 0.1042 / 0.1043\n", + "[584/1600][2/8] Loss_D: 0.7706 Loss_G: 1.7210 D(x): 0.6113 D(G(z)): 0.0953 / 0.0942\n", + "[584/1600][4/8] Loss_D: 0.7467 Loss_G: 1.6956 D(x): 0.6363 D(G(z)): 0.1004 / 0.0990\n", + "[584/1600][6/8] Loss_D: 0.7705 Loss_G: 1.6851 D(x): 0.5940 D(G(z)): 0.1001 / 0.1003\n", + "[585/1600][0/8] Loss_D: 0.8310 Loss_G: 1.7047 D(x): 0.6047 D(G(z)): 0.0998 / 0.0977\n", + "[585/1600][2/8] Loss_D: 0.7473 Loss_G: 1.6724 D(x): 0.6231 D(G(z)): 0.1048 / 0.1040\n", + "[585/1600][4/8] Loss_D: 0.7488 Loss_G: 1.6589 D(x): 0.6047 D(G(z)): 0.1051 / 0.1045\n", + "[585/1600][6/8] Loss_D: 0.7942 Loss_G: 1.6971 D(x): 0.5497 D(G(z)): 0.0984 / 0.1004\n", + "[586/1600][0/8] Loss_D: 0.7702 Loss_G: 1.6919 D(x): 0.6180 D(G(z)): 0.0991 / 0.0991\n", + "[586/1600][2/8] Loss_D: 0.8003 Loss_G: 1.7322 D(x): 0.5784 D(G(z)): 0.0966 / 0.0948\n", + "[586/1600][4/8] Loss_D: 0.7551 Loss_G: 1.6629 D(x): 0.6144 D(G(z)): 0.1048 / 0.1054\n", + "[586/1600][6/8] Loss_D: 0.8287 Loss_G: 1.6606 D(x): 0.6175 D(G(z)): 0.1081 / 0.1043\n", + "[587/1600][0/8] Loss_D: 0.7626 Loss_G: 1.7927 D(x): 0.5717 D(G(z)): 0.0852 / 0.0848\n", + "[587/1600][2/8] Loss_D: 0.8290 Loss_G: 1.7280 D(x): 0.6032 D(G(z)): 0.1004 / 0.0978\n", + "[587/1600][4/8] Loss_D: 0.7862 Loss_G: 1.6520 D(x): 0.6078 D(G(z)): 0.1083 / 0.1074\n", + "[587/1600][6/8] Loss_D: 0.7824 Loss_G: 1.6447 D(x): 0.6251 D(G(z)): 0.1087 / 0.1079\n", + "[588/1600][0/8] Loss_D: 0.8016 Loss_G: 1.7256 D(x): 0.6122 D(G(z)): 0.0968 / 0.0954\n", + "[588/1600][2/8] Loss_D: 0.8167 Loss_G: 1.7426 D(x): 0.6007 D(G(z)): 0.0962 / 0.0922\n", + "[588/1600][4/8] Loss_D: 0.7484 Loss_G: 1.7641 D(x): 0.6121 D(G(z)): 0.0905 / 0.0900\n", + "[588/1600][6/8] Loss_D: 0.8016 Loss_G: 1.7005 D(x): 0.6071 D(G(z)): 0.1009 / 0.0990\n", + "[589/1600][0/8] Loss_D: 0.8049 Loss_G: 1.8234 D(x): 0.5735 D(G(z)): 0.0856 / 0.0840\n", + "[589/1600][2/8] Loss_D: 0.7302 Loss_G: 1.7880 D(x): 0.6183 D(G(z)): 0.0890 / 0.0885\n", + "[589/1600][4/8] Loss_D: 0.7880 Loss_G: 1.7934 D(x): 0.5390 D(G(z)): 0.0846 / 0.0872\n", + "[589/1600][6/8] Loss_D: 0.7576 Loss_G: 1.7053 D(x): 0.5864 D(G(z)): 0.0949 / 0.0991\n", + "[590/1600][0/8] Loss_D: 0.7278 Loss_G: 1.6713 D(x): 0.6298 D(G(z)): 0.1013 / 0.1030\n", + "[590/1600][2/8] Loss_D: 0.7673 Loss_G: 1.7332 D(x): 0.6318 D(G(z)): 0.0976 / 0.0948\n", + "[590/1600][4/8] Loss_D: 0.7428 Loss_G: 1.6697 D(x): 0.6431 D(G(z)): 0.1046 / 0.1037\n", + "[590/1600][6/8] Loss_D: 0.7724 Loss_G: 1.6709 D(x): 0.5909 D(G(z)): 0.1021 / 0.1018\n", + "[591/1600][0/8] Loss_D: 0.7852 Loss_G: 1.8197 D(x): 0.6052 D(G(z)): 0.0866 / 0.0824\n", + "[591/1600][2/8] Loss_D: 0.7819 Loss_G: 1.8423 D(x): 0.5637 D(G(z)): 0.0824 / 0.0816\n", + "[591/1600][4/8] Loss_D: 0.7920 Loss_G: 1.7639 D(x): 0.5635 D(G(z)): 0.0898 / 0.0909\n", + "[591/1600][6/8] Loss_D: 0.8075 Loss_G: 1.7489 D(x): 0.5669 D(G(z)): 0.0937 / 0.0923\n", + "[592/1600][0/8] Loss_D: 0.7685 Loss_G: 1.7460 D(x): 0.6088 D(G(z)): 0.0918 / 0.0916\n", + "[592/1600][2/8] Loss_D: 0.7574 Loss_G: 1.7244 D(x): 0.5993 D(G(z)): 0.0934 / 0.0950\n", + "[592/1600][4/8] Loss_D: 0.7265 Loss_G: 1.7073 D(x): 0.6414 D(G(z)): 0.0967 / 0.0981\n", + "[592/1600][6/8] Loss_D: 0.7623 Loss_G: 1.7035 D(x): 0.6398 D(G(z)): 0.0997 / 0.0984\n", + "[593/1600][0/8] Loss_D: 0.6883 Loss_G: 1.7306 D(x): 0.6734 D(G(z)): 0.0951 / 0.0944\n", + "[593/1600][2/8] Loss_D: 0.7280 Loss_G: 1.6120 D(x): 0.6260 D(G(z)): 0.1110 / 0.1127\n", + "[593/1600][4/8] Loss_D: 0.8082 Loss_G: 1.7154 D(x): 0.6077 D(G(z)): 0.1003 / 0.0962\n", + "[593/1600][6/8] Loss_D: 0.7107 Loss_G: 1.7384 D(x): 0.6426 D(G(z)): 0.0943 / 0.0940\n", + "[594/1600][0/8] Loss_D: 0.8058 Loss_G: 1.7987 D(x): 0.6043 D(G(z)): 0.0895 / 0.0851\n", + "[594/1600][2/8] Loss_D: 0.7888 Loss_G: 1.8253 D(x): 0.5916 D(G(z)): 0.0840 / 0.0821\n", + "[594/1600][4/8] Loss_D: 0.7862 Loss_G: 1.7384 D(x): 0.5828 D(G(z)): 0.0941 / 0.0949\n", + "[594/1600][6/8] Loss_D: 0.7528 Loss_G: 1.7671 D(x): 0.6277 D(G(z)): 0.0884 / 0.0891\n", + "[595/1600][0/8] Loss_D: 0.7877 Loss_G: 1.7194 D(x): 0.6009 D(G(z)): 0.0978 / 0.0969\n", + "[595/1600][2/8] Loss_D: 0.7856 Loss_G: 1.7809 D(x): 0.6409 D(G(z)): 0.0908 / 0.0864\n", + "[595/1600][4/8] Loss_D: 0.8048 Loss_G: 1.8375 D(x): 0.5865 D(G(z)): 0.0849 / 0.0825\n", + "[595/1600][6/8] Loss_D: 0.7635 Loss_G: 1.8591 D(x): 0.5929 D(G(z)): 0.0790 / 0.0788\n", + "[596/1600][0/8] Loss_D: 0.7659 Loss_G: 1.8146 D(x): 0.6296 D(G(z)): 0.0853 / 0.0845\n", + "[596/1600][2/8] Loss_D: 0.8104 Loss_G: 1.7840 D(x): 0.5857 D(G(z)): 0.0889 / 0.0883\n", + "[596/1600][4/8] Loss_D: 0.7227 Loss_G: 1.7274 D(x): 0.6026 D(G(z)): 0.0925 / 0.0965\n", + "[596/1600][6/8] Loss_D: 0.7873 Loss_G: 1.7182 D(x): 0.6108 D(G(z)): 0.0986 / 0.0962\n", + "[597/1600][0/8] Loss_D: 0.7415 Loss_G: 1.7739 D(x): 0.6246 D(G(z)): 0.0903 / 0.0894\n", + "[597/1600][2/8] Loss_D: 0.7909 Loss_G: 1.7713 D(x): 0.6138 D(G(z)): 0.0940 / 0.0897\n", + "[597/1600][4/8] Loss_D: 0.7735 Loss_G: 1.8732 D(x): 0.5997 D(G(z)): 0.0802 / 0.0784\n", + "[597/1600][6/8] Loss_D: 0.7715 Loss_G: 1.8794 D(x): 0.5600 D(G(z)): 0.0775 / 0.0780\n", + "[598/1600][0/8] Loss_D: 0.7385 Loss_G: 1.9036 D(x): 0.5803 D(G(z)): 0.0713 / 0.0729\n", + "[598/1600][2/8] Loss_D: 0.7852 Loss_G: 1.8475 D(x): 0.5750 D(G(z)): 0.0777 / 0.0783\n", + "[598/1600][4/8] Loss_D: 0.7178 Loss_G: 1.7843 D(x): 0.6251 D(G(z)): 0.0874 / 0.0891\n", + "[598/1600][6/8] Loss_D: 0.7753 Loss_G: 1.8091 D(x): 0.5977 D(G(z)): 0.0837 / 0.0838\n", + "[599/1600][0/8] Loss_D: 0.7626 Loss_G: 1.7584 D(x): 0.6260 D(G(z)): 0.0920 / 0.0910\n", + "[599/1600][2/8] Loss_D: 0.8043 Loss_G: 1.8511 D(x): 0.6062 D(G(z)): 0.0827 / 0.0798\n", + "[599/1600][4/8] Loss_D: 0.7497 Loss_G: 1.8016 D(x): 0.5811 D(G(z)): 0.0835 / 0.0848\n", + "[599/1600][6/8] Loss_D: 0.7868 Loss_G: 1.7688 D(x): 0.6059 D(G(z)): 0.0921 / 0.0896\n", + "[600/1600][0/8] Loss_D: 0.6959 Loss_G: 1.8688 D(x): 0.6255 D(G(z)): 0.0772 / 0.0776\n", + "[600/1600][2/8] Loss_D: 0.7875 Loss_G: 1.8324 D(x): 0.5653 D(G(z)): 0.0825 / 0.0821\n", + "[600/1600][4/8] Loss_D: 0.7312 Loss_G: 1.8692 D(x): 0.6009 D(G(z)): 0.0758 / 0.0774\n", + "[600/1600][6/8] Loss_D: 0.7523 Loss_G: 1.8181 D(x): 0.6080 D(G(z)): 0.0829 / 0.0831\n", + "[601/1600][0/8] Loss_D: 0.8011 Loss_G: 1.8097 D(x): 0.6064 D(G(z)): 0.0877 / 0.0840\n", + "[601/1600][2/8] Loss_D: 0.7681 Loss_G: 1.8884 D(x): 0.5497 D(G(z)): 0.0729 / 0.0737\n", + "[601/1600][4/8] Loss_D: 0.7830 Loss_G: 1.8418 D(x): 0.6190 D(G(z)): 0.0811 / 0.0808\n", + "[601/1600][6/8] Loss_D: 0.7514 Loss_G: 1.7661 D(x): 0.5800 D(G(z)): 0.0885 / 0.0916\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[602/1600][0/8] Loss_D: 0.7314 Loss_G: 1.7729 D(x): 0.6242 D(G(z)): 0.0871 / 0.0893\n", + "[602/1600][2/8] Loss_D: 0.7724 Loss_G: 1.7005 D(x): 0.6388 D(G(z)): 0.0995 / 0.0981\n", + "[602/1600][4/8] Loss_D: 0.7715 Loss_G: 1.7649 D(x): 0.6507 D(G(z)): 0.0930 / 0.0896\n", + "[602/1600][6/8] Loss_D: 0.8178 Loss_G: 1.7516 D(x): 0.6327 D(G(z)): 0.0951 / 0.0908\n", + "[603/1600][0/8] Loss_D: 0.7973 Loss_G: 1.8584 D(x): 0.5958 D(G(z)): 0.0831 / 0.0795\n", + "[603/1600][2/8] Loss_D: 0.7080 Loss_G: 1.8648 D(x): 0.6033 D(G(z)): 0.0756 / 0.0772\n", + "[603/1600][4/8] Loss_D: 0.7488 Loss_G: 1.8462 D(x): 0.6344 D(G(z)): 0.0783 / 0.0789\n", + "[603/1600][6/8] Loss_D: 0.7708 Loss_G: 1.8218 D(x): 0.6450 D(G(z)): 0.0855 / 0.0836\n", + "[604/1600][0/8] Loss_D: 0.7423 Loss_G: 1.7815 D(x): 0.6234 D(G(z)): 0.0884 / 0.0883\n", + "[604/1600][2/8] Loss_D: 0.7902 Loss_G: 1.8174 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D(x): 0.6043 D(G(z)): 0.0716 / 0.0697\n", + "[634/1600][6/8] Loss_D: 0.8013 Loss_G: 1.9410 D(x): 0.6283 D(G(z)): 0.0721 / 0.0702\n", + "[635/1600][0/8] Loss_D: 0.7466 Loss_G: 1.8832 D(x): 0.5945 D(G(z)): 0.0745 / 0.0762\n", + "[635/1600][2/8] Loss_D: 0.7039 Loss_G: 1.8293 D(x): 0.6227 D(G(z)): 0.0821 / 0.0843\n", + "[635/1600][4/8] Loss_D: 0.7832 Loss_G: 1.7940 D(x): 0.6165 D(G(z)): 0.0883 / 0.0885\n", + "[635/1600][6/8] Loss_D: 0.7873 Loss_G: 1.8549 D(x): 0.6784 D(G(z)): 0.0844 / 0.0808\n", + "[636/1600][0/8] Loss_D: 0.7422 Loss_G: 1.8801 D(x): 0.6446 D(G(z)): 0.0777 / 0.0770\n", + "[636/1600][2/8] Loss_D: 0.6831 Loss_G: 1.8265 D(x): 0.6592 D(G(z)): 0.0834 / 0.0832\n", + "[636/1600][4/8] Loss_D: 0.7371 Loss_G: 1.9033 D(x): 0.6495 D(G(z)): 0.0744 / 0.0732\n", + "[636/1600][6/8] Loss_D: 0.7473 Loss_G: 1.8999 D(x): 0.5976 D(G(z)): 0.0755 / 0.0752\n", + "[637/1600][0/8] Loss_D: 0.7288 Loss_G: 1.8649 D(x): 0.6086 D(G(z)): 0.0792 / 0.0797\n", + "[637/1600][2/8] Loss_D: 0.7340 Loss_G: 1.8167 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D(x): 0.6634 D(G(z)): 0.0857 / 0.0829\n", + "[640/1600][2/8] Loss_D: 0.7665 Loss_G: 1.9479 D(x): 0.6167 D(G(z)): 0.0705 / 0.0687\n", + "[640/1600][4/8] Loss_D: 0.6765 Loss_G: 1.9014 D(x): 0.6689 D(G(z)): 0.0750 / 0.0748\n", + "[640/1600][6/8] Loss_D: 0.7093 Loss_G: 1.8859 D(x): 0.6357 D(G(z)): 0.0760 / 0.0775\n", + "[641/1600][0/8] Loss_D: 0.7236 Loss_G: 1.9006 D(x): 0.6639 D(G(z)): 0.0753 / 0.0755\n", + "[641/1600][2/8] Loss_D: 0.7693 Loss_G: 1.9120 D(x): 0.6286 D(G(z)): 0.0772 / 0.0752\n", + "[641/1600][4/8] Loss_D: 0.6959 Loss_G: 1.8833 D(x): 0.6212 D(G(z)): 0.0761 / 0.0766\n", + "[641/1600][6/8] Loss_D: 0.7934 Loss_G: 1.9268 D(x): 0.6437 D(G(z)): 0.0758 / 0.0732\n", + "[642/1600][0/8] Loss_D: 0.7410 Loss_G: 1.8247 D(x): 0.6321 D(G(z)): 0.0818 / 0.0821\n", + "[642/1600][2/8] Loss_D: 0.7278 Loss_G: 1.8706 D(x): 0.6428 D(G(z)): 0.0763 / 0.0773\n", + "[642/1600][4/8] Loss_D: 0.6721 Loss_G: 1.8858 D(x): 0.6761 D(G(z)): 0.0758 / 0.0758\n", + "[642/1600][6/8] Loss_D: 0.7630 Loss_G: 1.9292 D(x): 0.6446 D(G(z)): 0.0741 / 0.0723\n", + "[643/1600][0/8] Loss_D: 0.6896 Loss_G: 1.8551 D(x): 0.6280 D(G(z)): 0.0794 / 0.0802\n", + "[643/1600][2/8] Loss_D: 0.7271 Loss_G: 1.8666 D(x): 0.6529 D(G(z)): 0.0792 / 0.0797\n", + "[643/1600][4/8] Loss_D: 0.7550 Loss_G: 1.8537 D(x): 0.6617 D(G(z)): 0.0825 / 0.0801\n", + "[643/1600][6/8] Loss_D: 0.7890 Loss_G: 1.9174 D(x): 0.6577 D(G(z)): 0.0746 / 0.0714\n", + "[644/1600][0/8] Loss_D: 0.7358 Loss_G: 1.9287 D(x): 0.6179 D(G(z)): 0.0719 / 0.0718\n", + "[644/1600][2/8] Loss_D: 0.7832 Loss_G: 1.9070 D(x): 0.6267 D(G(z)): 0.0776 / 0.0759\n", + "[644/1600][4/8] Loss_D: 0.6991 Loss_G: 1.9081 D(x): 0.6239 D(G(z)): 0.0733 / 0.0744\n", + "[644/1600][6/8] Loss_D: 0.6834 Loss_G: 1.9336 D(x): 0.6387 D(G(z)): 0.0696 / 0.0703\n", + "[645/1600][0/8] Loss_D: 0.7697 Loss_G: 1.9534 D(x): 0.6402 D(G(z)): 0.0729 / 0.0703\n", + "[645/1600][2/8] Loss_D: 0.7017 Loss_G: 1.9561 D(x): 0.6035 D(G(z)): 0.0656 / 0.0685\n", + "[645/1600][4/8] Loss_D: 0.6811 Loss_G: 1.8829 D(x): 0.6367 D(G(z)): 0.0744 / 0.0776\n", + "[645/1600][6/8] Loss_D: 0.7596 Loss_G: 1.8259 D(x): 0.6230 D(G(z)): 0.0827 / 0.0829\n", + "[646/1600][0/8] Loss_D: 0.7256 Loss_G: 1.8571 D(x): 0.6553 D(G(z)): 0.0797 / 0.0788\n", + "[646/1600][2/8] Loss_D: 0.8002 Loss_G: 1.7944 D(x): 0.6669 D(G(z)): 0.0902 / 0.0870\n", + "[646/1600][4/8] Loss_D: 0.7385 Loss_G: 1.9332 D(x): 0.6357 D(G(z)): 0.0727 / 0.0706\n", + "[646/1600][6/8] Loss_D: 0.8116 Loss_G: 1.9217 D(x): 0.6483 D(G(z)): 0.0761 / 0.0726\n", + "[647/1600][0/8] Loss_D: 0.7613 Loss_G: 1.9578 D(x): 0.6289 D(G(z)): 0.0720 / 0.0686\n", + "[647/1600][2/8] Loss_D: 0.7834 Loss_G: 2.0484 D(x): 0.5848 D(G(z)): 0.0619 / 0.0604\n", + "[647/1600][4/8] Loss_D: 0.7427 Loss_G: 1.9686 D(x): 0.5541 D(G(z)): 0.0659 / 0.0686\n", + "[647/1600][6/8] Loss_D: 0.7319 Loss_G: 1.9741 D(x): 0.6136 D(G(z)): 0.0675 / 0.0688\n", + "[648/1600][0/8] Loss_D: 0.7896 Loss_G: 1.9260 D(x): 0.6294 D(G(z)): 0.0751 / 0.0738\n", + "[648/1600][2/8] Loss_D: 0.7394 Loss_G: 1.9549 D(x): 0.5845 D(G(z)): 0.0676 / 0.0693\n", + "[648/1600][4/8] Loss_D: 0.7149 Loss_G: 1.9205 D(x): 0.5984 D(G(z)): 0.0722 / 0.0740\n", + "[648/1600][6/8] Loss_D: 0.8012 Loss_G: 1.9415 D(x): 0.6545 D(G(z)): 0.0742 / 0.0723\n", + "[649/1600][0/8] Loss_D: 0.7316 Loss_G: 1.9443 D(x): 0.6285 D(G(z)): 0.0706 / 0.0708\n", + "[649/1600][2/8] Loss_D: 0.7288 Loss_G: 1.9421 D(x): 0.6612 D(G(z)): 0.0709 / 0.0697\n", + "[649/1600][4/8] Loss_D: 0.7726 Loss_G: 1.9293 D(x): 0.6136 D(G(z)): 0.0716 / 0.0710\n", + "[649/1600][6/8] Loss_D: 0.7123 Loss_G: 1.8894 D(x): 0.6139 D(G(z)): 0.0724 / 0.0744\n", + "[650/1600][0/8] Loss_D: 0.6894 Loss_G: 1.8384 D(x): 0.6411 D(G(z)): 0.0831 / 0.0832\n", + "[650/1600][2/8] Loss_D: 0.7030 Loss_G: 1.9042 D(x): 0.6391 D(G(z)): 0.0760 / 0.0756\n", + "[650/1600][4/8] Loss_D: 0.7215 Loss_G: 1.9326 D(x): 0.6231 D(G(z)): 0.0726 / 0.0736\n", + "[650/1600][6/8] Loss_D: 0.7388 Loss_G: 1.9368 D(x): 0.6165 D(G(z)): 0.0712 / 0.0705\n", + "[651/1600][0/8] Loss_D: 0.7715 Loss_G: 1.9699 D(x): 0.6167 D(G(z)): 0.0697 / 0.0680\n", + "[651/1600][2/8] Loss_D: 0.6842 Loss_G: 1.9201 D(x): 0.6270 D(G(z)): 0.0699 / 0.0712\n", + "[651/1600][4/8] Loss_D: 0.7412 Loss_G: 2.0117 D(x): 0.5904 D(G(z)): 0.0631 / 0.0639\n", + "[651/1600][6/8] Loss_D: 0.7713 Loss_G: 1.9035 D(x): 0.6523 D(G(z)): 0.0758 / 0.0744\n", + "[652/1600][0/8] Loss_D: 0.7296 Loss_G: 1.9063 D(x): 0.6119 D(G(z)): 0.0716 / 0.0732\n", + "[652/1600][2/8] Loss_D: 0.7751 Loss_G: 1.9160 D(x): 0.6618 D(G(z)): 0.0752 / 0.0725\n", + "[652/1600][4/8] Loss_D: 0.7048 Loss_G: 1.9211 D(x): 0.6388 D(G(z)): 0.0734 / 0.0736\n", + "[652/1600][6/8] Loss_D: 0.7950 Loss_G: 1.9766 D(x): 0.5932 D(G(z)): 0.0686 / 0.0668\n", + "[653/1600][0/8] Loss_D: 0.7005 Loss_G: 1.9992 D(x): 0.6706 D(G(z)): 0.0672 / 0.0653\n", + "[653/1600][2/8] Loss_D: 0.7680 Loss_G: 1.9654 D(x): 0.5851 D(G(z)): 0.0692 / 0.0699\n", + "[653/1600][4/8] Loss_D: 0.7043 Loss_G: 2.0165 D(x): 0.6341 D(G(z)): 0.0615 / 0.0623\n", + "[653/1600][6/8] Loss_D: 0.7737 Loss_G: 1.9281 D(x): 0.6266 D(G(z)): 0.0721 / 0.0722\n", + "[654/1600][0/8] Loss_D: 0.7593 Loss_G: 1.9308 D(x): 0.6071 D(G(z)): 0.0717 / 0.0718\n", + "[654/1600][2/8] Loss_D: 0.6821 Loss_G: 1.9789 D(x): 0.6357 D(G(z)): 0.0652 / 0.0665\n", + "[654/1600][4/8] Loss_D: 0.7776 Loss_G: 1.8733 D(x): 0.6830 D(G(z)): 0.0808 / 0.0780\n", + "[654/1600][6/8] Loss_D: 0.7381 Loss_G: 1.9206 D(x): 0.6345 D(G(z)): 0.0740 / 0.0718\n", + "[655/1600][0/8] Loss_D: 0.7671 Loss_G: 1.9436 D(x): 0.6615 D(G(z)): 0.0725 / 0.0708\n", + "[655/1600][2/8] Loss_D: 0.6992 Loss_G: 1.9197 D(x): 0.6369 D(G(z)): 0.0741 / 0.0745\n", + "[655/1600][4/8] Loss_D: 0.7218 Loss_G: 1.8925 D(x): 0.6266 D(G(z)): 0.0738 / 0.0756\n", + "[655/1600][6/8] Loss_D: 0.7621 Loss_G: 1.9106 D(x): 0.6196 D(G(z)): 0.0748 / 0.0742\n", + "[656/1600][0/8] Loss_D: 0.7777 Loss_G: 1.8340 D(x): 0.6372 D(G(z)): 0.0840 / 0.0818\n", + "[656/1600][2/8] Loss_D: 0.7962 Loss_G: 1.9281 D(x): 0.5797 D(G(z)): 0.0747 / 0.0731\n", + "[656/1600][4/8] Loss_D: 0.7388 Loss_G: 1.9917 D(x): 0.5840 D(G(z)): 0.0652 / 0.0652\n", + "[656/1600][6/8] Loss_D: 0.7842 Loss_G: 1.9575 D(x): 0.6247 D(G(z)): 0.0716 / 0.0696\n", + "[657/1600][0/8] Loss_D: 0.7465 Loss_G: 2.0288 D(x): 0.5762 D(G(z)): 0.0611 / 0.0620\n", + "[657/1600][2/8] Loss_D: 0.7735 Loss_G: 2.0751 D(x): 0.6226 D(G(z)): 0.0597 / 0.0580\n", + "[657/1600][4/8] Loss_D: 0.6989 Loss_G: 1.9605 D(x): 0.6275 D(G(z)): 0.0658 / 0.0676\n", + "[657/1600][6/8] Loss_D: 0.7034 Loss_G: 1.9895 D(x): 0.6428 D(G(z)): 0.0644 / 0.0652\n", + "[658/1600][0/8] Loss_D: 0.7506 Loss_G: 2.0036 D(x): 0.6478 D(G(z)): 0.0664 / 0.0644\n", + "[658/1600][2/8] Loss_D: 0.7264 Loss_G: 1.9923 D(x): 0.6266 D(G(z)): 0.0661 / 0.0663\n", + "[658/1600][4/8] Loss_D: 0.7452 Loss_G: 1.9789 D(x): 0.5914 D(G(z)): 0.0650 / 0.0667\n", + "[658/1600][6/8] Loss_D: 0.7432 Loss_G: 1.9328 D(x): 0.6316 D(G(z)): 0.0705 / 0.0717\n", + "[659/1600][0/8] Loss_D: 0.6837 Loss_G: 1.8386 D(x): 0.6663 D(G(z)): 0.0827 / 0.0834\n", + "[659/1600][2/8] Loss_D: 0.6522 Loss_G: 1.8642 D(x): 0.6874 D(G(z)): 0.0779 / 0.0785\n", + "[659/1600][4/8] Loss_D: 0.7742 Loss_G: 1.8357 D(x): 0.5990 D(G(z)): 0.0822 / 0.0814\n", + "[659/1600][6/8] Loss_D: 0.8049 Loss_G: 1.8907 D(x): 0.6396 D(G(z)): 0.0784 / 0.0755\n", + "[660/1600][0/8] Loss_D: 0.6812 Loss_G: 1.9117 D(x): 0.6457 D(G(z)): 0.0718 / 0.0724\n", + "[660/1600][2/8] Loss_D: 0.7767 Loss_G: 1.8565 D(x): 0.7002 D(G(z)): 0.0828 / 0.0804\n", + "[660/1600][4/8] Loss_D: 0.7108 Loss_G: 1.9750 D(x): 0.6440 D(G(z)): 0.0682 / 0.0674\n", + "[660/1600][6/8] Loss_D: 0.7859 Loss_G: 1.9891 D(x): 0.6396 D(G(z)): 0.0686 / 0.0664\n", + "[661/1600][0/8] Loss_D: 0.7757 Loss_G: 1.9680 D(x): 0.6182 D(G(z)): 0.0708 / 0.0680\n", + "[661/1600][2/8] Loss_D: 0.7849 Loss_G: 2.0596 D(x): 0.6197 D(G(z)): 0.0636 / 0.0606\n", + "[661/1600][4/8] Loss_D: 0.7297 Loss_G: 2.0145 D(x): 0.5658 D(G(z)): 0.0597 / 0.0631\n", + "[661/1600][6/8] Loss_D: 0.7553 Loss_G: 1.9891 D(x): 0.6161 D(G(z)): 0.0645 / 0.0654\n", + "[662/1600][0/8] Loss_D: 0.6643 Loss_G: 1.9172 D(x): 0.6598 D(G(z)): 0.0705 / 0.0726\n", + "[662/1600][2/8] Loss_D: 0.7367 Loss_G: 1.9636 D(x): 0.6506 D(G(z)): 0.0698 / 0.0679\n", + "[662/1600][4/8] Loss_D: 0.7276 Loss_G: 1.9993 D(x): 0.6370 D(G(z)): 0.0652 / 0.0648\n", + "[662/1600][6/8] Loss_D: 0.6980 Loss_G: 1.9879 D(x): 0.6244 D(G(z)): 0.0664 / 0.0667\n", + "[663/1600][0/8] Loss_D: 0.7391 Loss_G: 1.9398 D(x): 0.6275 D(G(z)): 0.0698 / 0.0702\n", + "[663/1600][2/8] Loss_D: 0.7457 Loss_G: 1.8342 D(x): 0.6337 D(G(z)): 0.0827 / 0.0831\n", + "[663/1600][4/8] Loss_D: 0.7650 Loss_G: 1.9805 D(x): 0.6182 D(G(z)): 0.0684 / 0.0661\n", + "[663/1600][6/8] Loss_D: 0.7483 Loss_G: 1.9877 D(x): 0.6330 D(G(z)): 0.0681 / 0.0665\n", + "[664/1600][0/8] Loss_D: 0.6885 Loss_G: 2.0270 D(x): 0.6490 D(G(z)): 0.0624 / 0.0627\n", + "[664/1600][2/8] Loss_D: 0.7800 Loss_G: 1.9963 D(x): 0.6330 D(G(z)): 0.0653 / 0.0639\n", + "[664/1600][4/8] Loss_D: 0.7000 Loss_G: 1.9893 D(x): 0.5997 D(G(z)): 0.0632 / 0.0652\n", + "[664/1600][6/8] Loss_D: 0.7388 Loss_G: 2.0054 D(x): 0.6305 D(G(z)): 0.0640 / 0.0634\n", + "[665/1600][0/8] Loss_D: 0.6903 Loss_G: 1.9447 D(x): 0.6178 D(G(z)): 0.0674 / 0.0696\n", + "[665/1600][2/8] Loss_D: 0.7689 Loss_G: 1.9673 D(x): 0.6540 D(G(z)): 0.0685 / 0.0674\n", + "[665/1600][4/8] Loss_D: 0.7844 Loss_G: 1.9701 D(x): 0.6245 D(G(z)): 0.0685 / 0.0667\n", + "[665/1600][6/8] Loss_D: 0.7316 Loss_G: 2.0304 D(x): 0.6196 D(G(z)): 0.0634 / 0.0622\n", + "[666/1600][0/8] Loss_D: 0.7228 Loss_G: 1.9746 D(x): 0.6098 D(G(z)): 0.0648 / 0.0661\n", + "[666/1600][2/8] Loss_D: 0.7604 Loss_G: 1.9762 D(x): 0.6287 D(G(z)): 0.0666 / 0.0655\n", + "[666/1600][4/8] Loss_D: 0.6650 Loss_G: 1.9008 D(x): 0.6445 D(G(z)): 0.0723 / 0.0741\n", + "[666/1600][6/8] Loss_D: 0.6747 Loss_G: 1.8825 D(x): 0.6814 D(G(z)): 0.0748 / 0.0766\n", + "[667/1600][0/8] Loss_D: 0.7492 Loss_G: 1.9034 D(x): 0.6247 D(G(z)): 0.0746 / 0.0737\n", + "[667/1600][2/8] Loss_D: 0.7274 Loss_G: 1.9379 D(x): 0.6814 D(G(z)): 0.0720 / 0.0699\n", + "[667/1600][4/8] Loss_D: 0.7260 Loss_G: 1.8255 D(x): 0.6718 D(G(z)): 0.0843 / 0.0835\n", + "[667/1600][6/8] Loss_D: 0.7166 Loss_G: 1.9951 D(x): 0.6095 D(G(z)): 0.0662 / 0.0661\n", + "[668/1600][0/8] Loss_D: 0.7786 Loss_G: 1.9719 D(x): 0.6648 D(G(z)): 0.0713 / 0.0691\n", + "[668/1600][2/8] Loss_D: 0.6890 Loss_G: 2.0025 D(x): 0.6256 D(G(z)): 0.0642 / 0.0640\n", + "[668/1600][4/8] Loss_D: 0.7357 Loss_G: 2.0106 D(x): 0.6075 D(G(z)): 0.0642 / 0.0638\n", + "[668/1600][6/8] Loss_D: 0.7714 Loss_G: 2.0143 D(x): 0.5410 D(G(z)): 0.0626 / 0.0634\n", + "[669/1600][0/8] Loss_D: 0.7039 Loss_G: 1.9777 D(x): 0.6159 D(G(z)): 0.0650 / 0.0665\n", + "[669/1600][2/8] Loss_D: 0.6599 Loss_G: 1.9452 D(x): 0.6563 D(G(z)): 0.0690 / 0.0711\n", + "[669/1600][4/8] Loss_D: 0.6762 Loss_G: 1.8719 D(x): 0.6387 D(G(z)): 0.0744 / 0.0775\n", + "[669/1600][6/8] Loss_D: 0.7869 Loss_G: 1.8436 D(x): 0.6852 D(G(z)): 0.0828 / 0.0807\n", + "[670/1600][0/8] Loss_D: 0.7976 Loss_G: 1.9362 D(x): 0.6711 D(G(z)): 0.0780 / 0.0728\n", + "[670/1600][2/8] Loss_D: 0.7293 Loss_G: 1.9038 D(x): 0.6274 D(G(z)): 0.0766 / 0.0755\n", + "[670/1600][4/8] Loss_D: 0.6947 Loss_G: 1.9507 D(x): 0.6115 D(G(z)): 0.0671 / 0.0690\n", + "[670/1600][6/8] Loss_D: 0.7611 Loss_G: 1.9054 D(x): 0.6431 D(G(z)): 0.0756 / 0.0754\n", + "[671/1600][0/8] Loss_D: 0.7298 Loss_G: 1.9730 D(x): 0.6171 D(G(z)): 0.0680 / 0.0679\n", + "[671/1600][2/8] Loss_D: 0.6953 Loss_G: 1.9517 D(x): 0.6493 D(G(z)): 0.0682 / 0.0682\n", + "[671/1600][4/8] Loss_D: 0.7856 Loss_G: 1.9331 D(x): 0.6085 D(G(z)): 0.0729 / 0.0715\n", + "[671/1600][6/8] Loss_D: 0.7087 Loss_G: 1.9485 D(x): 0.6205 D(G(z)): 0.0689 / 0.0695\n", + "[672/1600][0/8] Loss_D: 0.7059 Loss_G: 1.9720 D(x): 0.6066 D(G(z)): 0.0663 / 0.0677\n", + "[672/1600][2/8] Loss_D: 0.7796 Loss_G: 1.9529 D(x): 0.6213 D(G(z)): 0.0711 / 0.0691\n", + "[672/1600][4/8] Loss_D: 0.7267 Loss_G: 2.0594 D(x): 0.6154 D(G(z)): 0.0590 / 0.0581\n", + "[672/1600][6/8] Loss_D: 0.7432 Loss_G: 1.9889 D(x): 0.5967 D(G(z)): 0.0638 / 0.0652\n", + "[673/1600][0/8] Loss_D: 0.7336 Loss_G: 1.9407 D(x): 0.6064 D(G(z)): 0.0680 / 0.0689\n", + "[673/1600][2/8] Loss_D: 0.7652 Loss_G: 1.9388 D(x): 0.6349 D(G(z)): 0.0731 / 0.0705\n", + "[673/1600][4/8] Loss_D: 0.7804 Loss_G: 1.9849 D(x): 0.5999 D(G(z)): 0.0688 / 0.0664\n", + "[673/1600][6/8] Loss_D: 0.6992 Loss_G: 1.9978 D(x): 0.6213 D(G(z)): 0.0627 / 0.0643\n", + "[674/1600][0/8] Loss_D: 0.7461 Loss_G: 1.9343 D(x): 0.6089 D(G(z)): 0.0691 / 0.0702\n", + "[674/1600][2/8] Loss_D: 0.7465 Loss_G: 1.9724 D(x): 0.6360 D(G(z)): 0.0691 / 0.0687\n", + "[674/1600][4/8] Loss_D: 0.7792 Loss_G: 1.9061 D(x): 0.5924 D(G(z)): 0.0741 / 0.0727\n", + "[674/1600][6/8] Loss_D: 0.6841 Loss_G: 1.9647 D(x): 0.6197 D(G(z)): 0.0640 / 0.0668\n", + "[675/1600][0/8] Loss_D: 0.7076 Loss_G: 1.9146 D(x): 0.6481 D(G(z)): 0.0716 / 0.0732\n", + "[675/1600][2/8] Loss_D: 0.7023 Loss_G: 1.8647 D(x): 0.6701 D(G(z)): 0.0790 / 0.0780\n", + "[675/1600][4/8] Loss_D: 0.6973 Loss_G: 1.9263 D(x): 0.6378 D(G(z)): 0.0710 / 0.0719\n", + "[675/1600][6/8] Loss_D: 0.7477 Loss_G: 1.9218 D(x): 0.6494 D(G(z)): 0.0733 / 0.0714\n", + "[676/1600][0/8] Loss_D: 0.7595 Loss_G: 1.8664 D(x): 0.6258 D(G(z)): 0.0813 / 0.0799\n", + "[676/1600][2/8] Loss_D: 0.6639 Loss_G: 1.9843 D(x): 0.6455 D(G(z)): 0.0651 / 0.0655\n", + "[676/1600][4/8] Loss_D: 0.7041 Loss_G: 1.9183 D(x): 0.6139 D(G(z)): 0.0708 / 0.0718\n", + "[676/1600][6/8] Loss_D: 0.7856 Loss_G: 1.9231 D(x): 0.6088 D(G(z)): 0.0736 / 0.0722\n", + "[677/1600][0/8] Loss_D: 0.6788 Loss_G: 1.9443 D(x): 0.6367 D(G(z)): 0.0676 / 0.0690\n", + "[677/1600][2/8] Loss_D: 0.7417 Loss_G: 1.9310 D(x): 0.6235 D(G(z)): 0.0726 / 0.0718\n", + "[677/1600][4/8] Loss_D: 0.7296 Loss_G: 1.8820 D(x): 0.6173 D(G(z)): 0.0734 / 0.0761\n", + "[677/1600][6/8] Loss_D: 0.6686 Loss_G: 1.9705 D(x): 0.6822 D(G(z)): 0.0689 / 0.0681\n", + "[678/1600][0/8] Loss_D: 0.7449 Loss_G: 1.9601 D(x): 0.6623 D(G(z)): 0.0698 / 0.0672\n", + "[678/1600][2/8] Loss_D: 0.7210 Loss_G: 1.9636 D(x): 0.6366 D(G(z)): 0.0687 / 0.0685\n", + "[678/1600][4/8] Loss_D: 0.7187 Loss_G: 1.9033 D(x): 0.6065 D(G(z)): 0.0699 / 0.0722\n", + "[678/1600][6/8] Loss_D: 0.7841 Loss_G: 1.8864 D(x): 0.5936 D(G(z)): 0.0762 / 0.0766\n", + "[679/1600][0/8] Loss_D: 0.6589 Loss_G: 1.8819 D(x): 0.6741 D(G(z)): 0.0761 / 0.0764\n", + "[679/1600][2/8] Loss_D: 0.7262 Loss_G: 1.8998 D(x): 0.6312 D(G(z)): 0.0742 / 0.0742\n", + "[679/1600][4/8] Loss_D: 0.7053 Loss_G: 1.9421 D(x): 0.6369 D(G(z)): 0.0702 / 0.0697\n", + "[679/1600][6/8] Loss_D: 0.6793 Loss_G: 1.9037 D(x): 0.6265 D(G(z)): 0.0701 / 0.0728\n", + "[680/1600][0/8] Loss_D: 0.7854 Loss_G: 1.9737 D(x): 0.6480 D(G(z)): 0.0700 / 0.0667\n", + "[680/1600][2/8] Loss_D: 0.7155 Loss_G: 2.0456 D(x): 0.6372 D(G(z)): 0.0626 / 0.0606\n", + "[680/1600][4/8] Loss_D: 0.7334 Loss_G: 2.0458 D(x): 0.5974 D(G(z)): 0.0613 / 0.0612\n", + "[680/1600][6/8] Loss_D: 0.7508 Loss_G: 1.9133 D(x): 0.5962 D(G(z)): 0.0707 / 0.0721\n", + "[681/1600][0/8] Loss_D: 0.7364 Loss_G: 2.0815 D(x): 0.6214 D(G(z)): 0.0575 / 0.0568\n", + "[681/1600][2/8] Loss_D: 0.7327 Loss_G: 1.9431 D(x): 0.6104 D(G(z)): 0.0683 / 0.0682\n", + "[681/1600][4/8] Loss_D: 0.7120 Loss_G: 1.9958 D(x): 0.5990 D(G(z)): 0.0634 / 0.0648\n", + "[681/1600][6/8] Loss_D: 0.6695 Loss_G: 1.9332 D(x): 0.6653 D(G(z)): 0.0700 / 0.0719\n", + "[682/1600][0/8] Loss_D: 0.7907 Loss_G: 1.9407 D(x): 0.6591 D(G(z)): 0.0738 / 0.0713\n", + "[682/1600][2/8] Loss_D: 0.7144 Loss_G: 2.0332 D(x): 0.6431 D(G(z)): 0.0630 / 0.0613\n", + "[682/1600][4/8] Loss_D: 0.7280 Loss_G: 2.0090 D(x): 0.5959 D(G(z)): 0.0612 / 0.0629\n", + "[682/1600][6/8] Loss_D: 0.6804 Loss_G: 1.9710 D(x): 0.6508 D(G(z)): 0.0655 / 0.0669\n", + "[683/1600][0/8] Loss_D: 0.7748 Loss_G: 1.9703 D(x): 0.5927 D(G(z)): 0.0691 / 0.0675\n", + "[683/1600][2/8] Loss_D: 0.7849 Loss_G: 1.9260 D(x): 0.6218 D(G(z)): 0.0735 / 0.0722\n", + "[683/1600][4/8] Loss_D: 0.6833 Loss_G: 1.9351 D(x): 0.6917 D(G(z)): 0.0710 / 0.0701\n", + "[683/1600][6/8] Loss_D: 0.7212 Loss_G: 1.9920 D(x): 0.5960 D(G(z)): 0.0625 / 0.0635\n", + "[684/1600][0/8] Loss_D: 0.7084 Loss_G: 1.9611 D(x): 0.6409 D(G(z)): 0.0681 / 0.0684\n", + "[684/1600][2/8] Loss_D: 0.7740 Loss_G: 1.9117 D(x): 0.6668 D(G(z)): 0.0735 / 0.0719\n", + "[684/1600][4/8] Loss_D: 0.6673 Loss_G: 1.9000 D(x): 0.6569 D(G(z)): 0.0742 / 0.0756\n", + "[684/1600][6/8] Loss_D: 0.7264 Loss_G: 1.9672 D(x): 0.6220 D(G(z)): 0.0671 / 0.0666\n", + "[685/1600][0/8] Loss_D: 0.7488 Loss_G: 1.9902 D(x): 0.6460 D(G(z)): 0.0674 / 0.0656\n", + "[685/1600][2/8] Loss_D: 0.7499 Loss_G: 2.0973 D(x): 0.6304 D(G(z)): 0.0586 / 0.0562\n", + "[685/1600][4/8] Loss_D: 0.7585 Loss_G: 2.0601 D(x): 0.5994 D(G(z)): 0.0605 / 0.0590\n", + "[685/1600][6/8] Loss_D: 0.7357 Loss_G: 2.0103 D(x): 0.6279 D(G(z)): 0.0626 / 0.0638\n", + "[686/1600][0/8] Loss_D: 0.7564 Loss_G: 1.9752 D(x): 0.6098 D(G(z)): 0.0658 / 0.0664\n", + "[686/1600][2/8] Loss_D: 0.6979 Loss_G: 1.9642 D(x): 0.6081 D(G(z)): 0.0654 / 0.0677\n", + "[686/1600][4/8] Loss_D: 0.7085 Loss_G: 1.9406 D(x): 0.6499 D(G(z)): 0.0700 / 0.0700\n", + "[686/1600][6/8] Loss_D: 0.7586 Loss_G: 1.9952 D(x): 0.6273 D(G(z)): 0.0659 / 0.0648\n", + "[687/1600][0/8] Loss_D: 0.6647 Loss_G: 1.9486 D(x): 0.6647 D(G(z)): 0.0689 / 0.0702\n", + "[687/1600][2/8] Loss_D: 0.7275 Loss_G: 1.8911 D(x): 0.6047 D(G(z)): 0.0742 / 0.0754\n", + "[687/1600][4/8] Loss_D: 0.7575 Loss_G: 1.9196 D(x): 0.6292 D(G(z)): 0.0728 / 0.0723\n", + "[687/1600][6/8] Loss_D: 0.6951 Loss_G: 1.9099 D(x): 0.6314 D(G(z)): 0.0739 / 0.0737\n", + "[688/1600][0/8] Loss_D: 0.6820 Loss_G: 1.9596 D(x): 0.6435 D(G(z)): 0.0665 / 0.0674\n", + "[688/1600][2/8] Loss_D: 0.6983 Loss_G: 1.8849 D(x): 0.6231 D(G(z)): 0.0727 / 0.0759\n", + "[688/1600][4/8] Loss_D: 0.7292 Loss_G: 1.8701 D(x): 0.6517 D(G(z)): 0.0789 / 0.0776\n", + "[688/1600][6/8] Loss_D: 0.7961 Loss_G: 1.9417 D(x): 0.6529 D(G(z)): 0.0742 / 0.0705\n", + "[689/1600][0/8] Loss_D: 0.7029 Loss_G: 1.9337 D(x): 0.6451 D(G(z)): 0.0721 / 0.0715\n", + "[689/1600][2/8] Loss_D: 0.6578 Loss_G: 2.0109 D(x): 0.6451 D(G(z)): 0.0605 / 0.0621\n", + "[689/1600][4/8] Loss_D: 0.7073 Loss_G: 1.9123 D(x): 0.6540 D(G(z)): 0.0722 / 0.0732\n", + "[689/1600][6/8] Loss_D: 0.6923 Loss_G: 1.9320 D(x): 0.6763 D(G(z)): 0.0719 / 0.0710\n", + "[690/1600][0/8] Loss_D: 0.7455 Loss_G: 1.8668 D(x): 0.6530 D(G(z)): 0.0798 / 0.0789\n", + "[690/1600][2/8] Loss_D: 0.6841 Loss_G: 1.9765 D(x): 0.6324 D(G(z)): 0.0665 / 0.0665\n", + "[690/1600][4/8] Loss_D: 0.7500 Loss_G: 1.9358 D(x): 0.6114 D(G(z)): 0.0716 / 0.0707\n", + "[690/1600][6/8] Loss_D: 0.7240 Loss_G: 1.9079 D(x): 0.6284 D(G(z)): 0.0733 / 0.0736\n", + "[691/1600][0/8] Loss_D: 0.7293 Loss_G: 1.8694 D(x): 0.6438 D(G(z)): 0.0782 / 0.0784\n", + "[691/1600][2/8] Loss_D: 0.7565 Loss_G: 1.8962 D(x): 0.6542 D(G(z)): 0.0758 / 0.0746\n", + "[691/1600][4/8] Loss_D: 0.7442 Loss_G: 1.9744 D(x): 0.6434 D(G(z)): 0.0689 / 0.0663\n", + "[691/1600][6/8] Loss_D: 0.7061 Loss_G: 1.9806 D(x): 0.6275 D(G(z)): 0.0667 / 0.0662\n", + "[692/1600][0/8] Loss_D: 0.7212 Loss_G: 1.9666 D(x): 0.6230 D(G(z)): 0.0664 / 0.0683\n", + "[692/1600][2/8] Loss_D: 0.7013 Loss_G: 1.9422 D(x): 0.6283 D(G(z)): 0.0682 / 0.0689\n", + "[692/1600][4/8] Loss_D: 0.8089 Loss_G: 1.8403 D(x): 0.6820 D(G(z)): 0.0842 / 0.0813\n", + "[692/1600][6/8] Loss_D: 0.7246 Loss_G: 1.9614 D(x): 0.6694 D(G(z)): 0.0705 / 0.0675\n", + "[693/1600][0/8] Loss_D: 0.7504 Loss_G: 1.9986 D(x): 0.5867 D(G(z)): 0.0653 / 0.0645\n", + "[693/1600][2/8] Loss_D: 0.7586 Loss_G: 1.9827 D(x): 0.5860 D(G(z)): 0.0659 / 0.0659\n", + "[693/1600][4/8] Loss_D: 0.6854 Loss_G: 1.9793 D(x): 0.6124 D(G(z)): 0.0641 / 0.0660\n", + "[693/1600][6/8] Loss_D: 0.7076 Loss_G: 1.9705 D(x): 0.5990 D(G(z)): 0.0649 / 0.0667\n", + "[694/1600][0/8] Loss_D: 0.7544 Loss_G: 1.9141 D(x): 0.6193 D(G(z)): 0.0719 / 0.0731\n", + "[694/1600][2/8] Loss_D: 0.7940 Loss_G: 1.8988 D(x): 0.6516 D(G(z)): 0.0766 / 0.0745\n", + "[694/1600][4/8] Loss_D: 0.7091 Loss_G: 1.9199 D(x): 0.6427 D(G(z)): 0.0729 / 0.0725\n", + "[694/1600][6/8] Loss_D: 0.7527 Loss_G: 1.9354 D(x): 0.6311 D(G(z)): 0.0713 / 0.0708\n", + "[695/1600][0/8] Loss_D: 0.6831 Loss_G: 1.9745 D(x): 0.6511 D(G(z)): 0.0653 / 0.0658\n", + "[695/1600][2/8] Loss_D: 0.7659 Loss_G: 1.8761 D(x): 0.6600 D(G(z)): 0.0785 / 0.0763\n", + "[695/1600][4/8] Loss_D: 0.7887 Loss_G: 1.9197 D(x): 0.6290 D(G(z)): 0.0753 / 0.0725\n", + "[695/1600][6/8] Loss_D: 0.6971 Loss_G: 1.9727 D(x): 0.6112 D(G(z)): 0.0651 / 0.0667\n", + "[696/1600][0/8] Loss_D: 0.7409 Loss_G: 1.8546 D(x): 0.5863 D(G(z)): 0.0764 / 0.0796\n", + "[696/1600][2/8] Loss_D: 0.7949 Loss_G: 1.9454 D(x): 0.6742 D(G(z)): 0.0730 / 0.0692\n", + "[696/1600][4/8] Loss_D: 0.7414 Loss_G: 1.9461 D(x): 0.6284 D(G(z)): 0.0696 / 0.0685\n", + "[696/1600][6/8] Loss_D: 0.7515 Loss_G: 1.9679 D(x): 0.5977 D(G(z)): 0.0676 / 0.0673\n", + "[697/1600][0/8] Loss_D: 0.7600 Loss_G: 1.9307 D(x): 0.6321 D(G(z)): 0.0733 / 0.0719\n", + "[697/1600][2/8] Loss_D: 0.7379 Loss_G: 1.8832 D(x): 0.5818 D(G(z)): 0.0729 / 0.0755\n", + "[697/1600][4/8] Loss_D: 0.8042 Loss_G: 1.9355 D(x): 0.6640 D(G(z)): 0.0732 / 0.0692\n", + "[697/1600][6/8] Loss_D: 0.6936 Loss_G: 1.9943 D(x): 0.6216 D(G(z)): 0.0633 / 0.0640\n", + "[698/1600][0/8] Loss_D: 0.7482 Loss_G: 1.9730 D(x): 0.6331 D(G(z)): 0.0678 / 0.0665\n", + "[698/1600][2/8] Loss_D: 0.6640 Loss_G: 1.9974 D(x): 0.6320 D(G(z)): 0.0631 / 0.0651\n", + "[698/1600][4/8] Loss_D: 0.7741 Loss_G: 1.9034 D(x): 0.5977 D(G(z)): 0.0742 / 0.0748\n", + "[698/1600][6/8] Loss_D: 0.7614 Loss_G: 1.8513 D(x): 0.6439 D(G(z)): 0.0817 / 0.0802\n", + "[699/1600][0/8] Loss_D: 0.7627 Loss_G: 1.8955 D(x): 0.5711 D(G(z)): 0.0747 / 0.0746\n", + "[699/1600][2/8] Loss_D: 0.6958 Loss_G: 1.8676 D(x): 0.6152 D(G(z)): 0.0745 / 0.0775\n", + "[699/1600][4/8] Loss_D: 0.7158 Loss_G: 1.8761 D(x): 0.6634 D(G(z)): 0.0761 / 0.0767\n", + "[699/1600][6/8] Loss_D: 0.7030 Loss_G: 1.8513 D(x): 0.6775 D(G(z)): 0.0804 / 0.0795\n", + "[700/1600][0/8] Loss_D: 0.7247 Loss_G: 1.7991 D(x): 0.6551 D(G(z)): 0.0856 / 0.0848\n", + "[700/1600][2/8] Loss_D: 0.7667 Loss_G: 1.8980 D(x): 0.6156 D(G(z)): 0.0776 / 0.0751\n", + "[700/1600][4/8] Loss_D: 0.7696 Loss_G: 1.9318 D(x): 0.6257 D(G(z)): 0.0721 / 0.0704\n", + "[700/1600][6/8] Loss_D: 0.6775 Loss_G: 1.9689 D(x): 0.6593 D(G(z)): 0.0673 / 0.0670\n", + "[701/1600][0/8] Loss_D: 0.7581 Loss_G: 1.9639 D(x): 0.6112 D(G(z)): 0.0678 / 0.0663\n", + "[701/1600][2/8] Loss_D: 0.6838 Loss_G: 1.9195 D(x): 0.6315 D(G(z)): 0.0692 / 0.0711\n", + "[701/1600][4/8] Loss_D: 0.7923 Loss_G: 1.9208 D(x): 0.6446 D(G(z)): 0.0733 / 0.0708\n", + "[701/1600][6/8] Loss_D: 0.7140 Loss_G: 1.9865 D(x): 0.6198 D(G(z)): 0.0659 / 0.0659\n", + "[702/1600][0/8] Loss_D: 0.7378 Loss_G: 1.9662 D(x): 0.6192 D(G(z)): 0.0674 / 0.0678\n", + "[702/1600][2/8] Loss_D: 0.7840 Loss_G: 1.9768 D(x): 0.6345 D(G(z)): 0.0687 / 0.0664\n", + "[702/1600][4/8] Loss_D: 0.7413 Loss_G: 1.9665 D(x): 0.6494 D(G(z)): 0.0691 / 0.0683\n", + "[702/1600][6/8] Loss_D: 0.7586 Loss_G: 1.9894 D(x): 0.6336 D(G(z)): 0.0673 / 0.0664\n", + "[703/1600][0/8] Loss_D: 0.7706 Loss_G: 1.9929 D(x): 0.6302 D(G(z)): 0.0648 / 0.0638\n", + "[703/1600][2/8] Loss_D: 0.7434 Loss_G: 1.9642 D(x): 0.5783 D(G(z)): 0.0678 / 0.0677\n", + "[703/1600][4/8] Loss_D: 0.7495 Loss_G: 2.0315 D(x): 0.6335 D(G(z)): 0.0637 / 0.0621\n", + "[703/1600][6/8] Loss_D: 0.7560 Loss_G: 2.0012 D(x): 0.6009 D(G(z)): 0.0651 / 0.0639\n", + "[704/1600][0/8] Loss_D: 0.7579 Loss_G: 2.0415 D(x): 0.5688 D(G(z)): 0.0589 / 0.0594\n", + "[704/1600][2/8] Loss_D: 0.6923 Loss_G: 1.9358 D(x): 0.6144 D(G(z)): 0.0689 / 0.0714\n", + "[704/1600][4/8] Loss_D: 0.7907 Loss_G: 1.9594 D(x): 0.6085 D(G(z)): 0.0707 / 0.0690\n", + "[704/1600][6/8] Loss_D: 0.7649 Loss_G: 1.9210 D(x): 0.6057 D(G(z)): 0.0719 / 0.0722\n", + "[705/1600][0/8] Loss_D: 0.7290 Loss_G: 1.8701 D(x): 0.5917 D(G(z)): 0.0757 / 0.0779\n", + "[705/1600][2/8] Loss_D: 0.6539 Loss_G: 1.9023 D(x): 0.6737 D(G(z)): 0.0737 / 0.0746\n", + "[705/1600][4/8] Loss_D: 0.7297 Loss_G: 1.9488 D(x): 0.6501 D(G(z)): 0.0736 / 0.0725\n", + "[705/1600][6/8] Loss_D: 0.7590 Loss_G: 1.9178 D(x): 0.6712 D(G(z)): 0.0754 / 0.0722\n", + "[706/1600][0/8] Loss_D: 0.7617 Loss_G: 1.9583 D(x): 0.6106 D(G(z)): 0.0677 / 0.0676\n", + "[706/1600][2/8] Loss_D: 0.6936 Loss_G: 1.8628 D(x): 0.6316 D(G(z)): 0.0755 / 0.0781\n", + "[706/1600][4/8] Loss_D: 0.7397 Loss_G: 1.8411 D(x): 0.6415 D(G(z)): 0.0802 / 0.0806\n", + "[706/1600][6/8] Loss_D: 0.7325 Loss_G: 1.9111 D(x): 0.6398 D(G(z)): 0.0735 / 0.0730\n", + "[707/1600][0/8] Loss_D: 0.7446 Loss_G: 1.8864 D(x): 0.6168 D(G(z)): 0.0773 / 0.0755\n", + "[707/1600][2/8] Loss_D: 0.7481 Loss_G: 1.8433 D(x): 0.6585 D(G(z)): 0.0810 / 0.0793\n", + "[707/1600][4/8] Loss_D: 0.8000 Loss_G: 1.9259 D(x): 0.6539 D(G(z)): 0.0752 / 0.0717\n", + "[707/1600][6/8] Loss_D: 0.6923 Loss_G: 2.0098 D(x): 0.6584 D(G(z)): 0.0640 / 0.0644\n", + "[708/1600][0/8] Loss_D: 0.7206 Loss_G: 1.9699 D(x): 0.6478 D(G(z)): 0.0676 / 0.0658\n", + "[708/1600][2/8] Loss_D: 0.7392 Loss_G: 1.9323 D(x): 0.6008 D(G(z)): 0.0698 / 0.0718\n", + "[708/1600][4/8] Loss_D: 0.7255 Loss_G: 1.8825 D(x): 0.6201 D(G(z)): 0.0750 / 0.0752\n", + "[708/1600][6/8] Loss_D: 0.6874 Loss_G: 1.9113 D(x): 0.6240 D(G(z)): 0.0720 / 0.0734\n", + "[709/1600][0/8] Loss_D: 0.6689 Loss_G: 1.9214 D(x): 0.6707 D(G(z)): 0.0709 / 0.0708\n", + "[709/1600][2/8] Loss_D: 0.6747 Loss_G: 1.8397 D(x): 0.6731 D(G(z)): 0.0792 / 0.0817\n", + "[709/1600][4/8] Loss_D: 0.7589 Loss_G: 1.8643 D(x): 0.6793 D(G(z)): 0.0816 / 0.0779\n", + "[709/1600][6/8] Loss_D: 0.7537 Loss_G: 1.8820 D(x): 0.6225 D(G(z)): 0.0772 / 0.0763\n", + "[710/1600][0/8] Loss_D: 0.7470 Loss_G: 1.9208 D(x): 0.6280 D(G(z)): 0.0738 / 0.0721\n", + "[710/1600][2/8] Loss_D: 0.7881 Loss_G: 1.8890 D(x): 0.5719 D(G(z)): 0.0755 / 0.0758\n", + "[710/1600][4/8] Loss_D: 0.7690 Loss_G: 1.9036 D(x): 0.6998 D(G(z)): 0.0776 / 0.0743\n", + "[710/1600][6/8] Loss_D: 0.7747 Loss_G: 1.9565 D(x): 0.6483 D(G(z)): 0.0716 / 0.0686\n", + "[711/1600][0/8] Loss_D: 0.7078 Loss_G: 1.9549 D(x): 0.6086 D(G(z)): 0.0673 / 0.0690\n", + "[711/1600][2/8] Loss_D: 0.7710 Loss_G: 1.8549 D(x): 0.6501 D(G(z)): 0.0802 / 0.0793\n", + "[711/1600][4/8] Loss_D: 0.7456 Loss_G: 1.9281 D(x): 0.6458 D(G(z)): 0.0740 / 0.0722\n", + "[711/1600][6/8] Loss_D: 0.7059 Loss_G: 1.8542 D(x): 0.6275 D(G(z)): 0.0772 / 0.0788\n", + "[712/1600][0/8] Loss_D: 0.7427 Loss_G: 1.8231 D(x): 0.5780 D(G(z)): 0.0799 / 0.0833\n", + "[712/1600][2/8] Loss_D: 0.7485 Loss_G: 1.8589 D(x): 0.6619 D(G(z)): 0.0822 / 0.0795\n", + "[712/1600][4/8] Loss_D: 0.7647 Loss_G: 1.8523 D(x): 0.6434 D(G(z)): 0.0819 / 0.0790\n", + "[712/1600][6/8] Loss_D: 0.6593 Loss_G: 1.9667 D(x): 0.6407 D(G(z)): 0.0673 / 0.0673\n", + "[713/1600][0/8] Loss_D: 0.7063 Loss_G: 1.9927 D(x): 0.6454 D(G(z)): 0.0661 / 0.0652\n", + "[713/1600][2/8] Loss_D: 0.7126 Loss_G: 1.8953 D(x): 0.6248 D(G(z)): 0.0729 / 0.0745\n", + "[713/1600][4/8] Loss_D: 0.7718 Loss_G: 1.9523 D(x): 0.6317 D(G(z)): 0.0715 / 0.0695\n", + "[713/1600][6/8] Loss_D: 0.7231 Loss_G: 1.8724 D(x): 0.5881 D(G(z)): 0.0731 / 0.0763\n", + "[714/1600][0/8] Loss_D: 0.7447 Loss_G: 1.8579 D(x): 0.6183 D(G(z)): 0.0812 / 0.0808\n", + "[714/1600][2/8] Loss_D: 0.7873 Loss_G: 1.9047 D(x): 0.5842 D(G(z)): 0.0751 / 0.0728\n", + "[714/1600][4/8] Loss_D: 0.6643 Loss_G: 1.9458 D(x): 0.6536 D(G(z)): 0.0681 / 0.0688\n", + "[714/1600][6/8] Loss_D: 0.6936 Loss_G: 1.9156 D(x): 0.6604 D(G(z)): 0.0718 / 0.0726\n", + "[715/1600][0/8] Loss_D: 0.6910 Loss_G: 1.8712 D(x): 0.6412 D(G(z)): 0.0747 / 0.0775\n", + "[715/1600][2/8] Loss_D: 0.7937 Loss_G: 1.9350 D(x): 0.6297 D(G(z)): 0.0734 / 0.0701\n", + "[715/1600][4/8] Loss_D: 0.6730 Loss_G: 1.9223 D(x): 0.6400 D(G(z)): 0.0733 / 0.0728\n", + "[715/1600][6/8] Loss_D: 0.7151 Loss_G: 1.8988 D(x): 0.6473 D(G(z)): 0.0732 / 0.0729\n", + "[716/1600][0/8] Loss_D: 0.7681 Loss_G: 1.8756 D(x): 0.6496 D(G(z)): 0.0792 / 0.0784\n", + "[716/1600][2/8] Loss_D: 0.7428 Loss_G: 1.9326 D(x): 0.5842 D(G(z)): 0.0699 / 0.0698\n", + "[716/1600][4/8] Loss_D: 0.7961 Loss_G: 1.9345 D(x): 0.6388 D(G(z)): 0.0726 / 0.0705\n", + "[716/1600][6/8] Loss_D: 0.7858 Loss_G: 1.9398 D(x): 0.6136 D(G(z)): 0.0741 / 0.0705\n", + "[717/1600][0/8] Loss_D: 0.7662 Loss_G: 1.9117 D(x): 0.5689 D(G(z)): 0.0713 / 0.0736\n", + "[717/1600][2/8] Loss_D: 0.7681 Loss_G: 1.9088 D(x): 0.6325 D(G(z)): 0.0732 / 0.0728\n", + "[717/1600][4/8] Loss_D: 0.7105 Loss_G: 1.8748 D(x): 0.6368 D(G(z)): 0.0756 / 0.0769\n", + "[717/1600][6/8] Loss_D: 0.7600 Loss_G: 1.9681 D(x): 0.6373 D(G(z)): 0.0694 / 0.0665\n", + "[718/1600][0/8] Loss_D: 0.6762 Loss_G: 1.8968 D(x): 0.6293 D(G(z)): 0.0745 / 0.0750\n", + "[718/1600][2/8] Loss_D: 0.7294 Loss_G: 1.9346 D(x): 0.5945 D(G(z)): 0.0691 / 0.0710\n", + "[718/1600][4/8] Loss_D: 0.7072 Loss_G: 1.9185 D(x): 0.6527 D(G(z)): 0.0727 / 0.0724\n", + "[718/1600][6/8] Loss_D: 0.7726 Loss_G: 1.8850 D(x): 0.6254 D(G(z)): 0.0766 / 0.0743\n", + "[719/1600][0/8] Loss_D: 0.7268 Loss_G: 1.8841 D(x): 0.6080 D(G(z)): 0.0759 / 0.0768\n", + "[719/1600][2/8] Loss_D: 0.7359 Loss_G: 1.9530 D(x): 0.6387 D(G(z)): 0.0682 / 0.0684\n", + "[719/1600][4/8] Loss_D: 0.7875 Loss_G: 1.8884 D(x): 0.6107 D(G(z)): 0.0769 / 0.0752\n", + "[719/1600][6/8] Loss_D: 0.7662 Loss_G: 1.8796 D(x): 0.6282 D(G(z)): 0.0789 / 0.0758\n", + "[720/1600][0/8] Loss_D: 0.7177 Loss_G: 1.9316 D(x): 0.6311 D(G(z)): 0.0703 / 0.0700\n", + "[720/1600][2/8] Loss_D: 0.7471 Loss_G: 1.9095 D(x): 0.6297 D(G(z)): 0.0736 / 0.0736\n", + "[720/1600][4/8] Loss_D: 0.7117 Loss_G: 1.8879 D(x): 0.6104 D(G(z)): 0.0744 / 0.0753\n", + "[720/1600][6/8] Loss_D: 0.7518 Loss_G: 1.9385 D(x): 0.6204 D(G(z)): 0.0699 / 0.0698\n", + "[721/1600][0/8] Loss_D: 0.7074 Loss_G: 1.8076 D(x): 0.6801 D(G(z)): 0.0846 / 0.0844\n", + "[721/1600][2/8] Loss_D: 0.7222 Loss_G: 1.9093 D(x): 0.6179 D(G(z)): 0.0720 / 0.0723\n", + "[721/1600][4/8] Loss_D: 0.7391 Loss_G: 1.8908 D(x): 0.6068 D(G(z)): 0.0766 / 0.0758\n", + "[721/1600][6/8] Loss_D: 0.6900 Loss_G: 1.8417 D(x): 0.6320 D(G(z)): 0.0766 / 0.0798\n", + "[722/1600][0/8] Loss_D: 0.6808 Loss_G: 1.8384 D(x): 0.6592 D(G(z)): 0.0806 / 0.0811\n", + "[722/1600][2/8] Loss_D: 0.7110 Loss_G: 1.7813 D(x): 0.6265 D(G(z)): 0.0854 / 0.0869\n", + "[722/1600][4/8] Loss_D: 0.7518 Loss_G: 1.7808 D(x): 0.6651 D(G(z)): 0.0897 / 0.0879\n", + "[722/1600][6/8] Loss_D: 0.7427 Loss_G: 1.8289 D(x): 0.6519 D(G(z)): 0.0854 / 0.0831\n", + "[723/1600][0/8] Loss_D: 0.7190 Loss_G: 1.7999 D(x): 0.6348 D(G(z)): 0.0872 / 0.0864\n", + "[723/1600][2/8] Loss_D: 0.7098 Loss_G: 1.8548 D(x): 0.6124 D(G(z)): 0.0794 / 0.0809\n", + "[723/1600][4/8] Loss_D: 0.7379 Loss_G: 1.9282 D(x): 0.6454 D(G(z)): 0.0730 / 0.0705\n", + "[723/1600][6/8] Loss_D: 0.7215 Loss_G: 1.8869 D(x): 0.5959 D(G(z)): 0.0738 / 0.0760\n", + "[724/1600][0/8] Loss_D: 0.7748 Loss_G: 1.9634 D(x): 0.6229 D(G(z)): 0.0709 / 0.0681\n", + "[724/1600][2/8] Loss_D: 0.7700 Loss_G: 1.9740 D(x): 0.6277 D(G(z)): 0.0694 / 0.0656\n", + "[724/1600][4/8] Loss_D: 0.7472 Loss_G: 1.9589 D(x): 0.5742 D(G(z)): 0.0657 / 0.0680\n", + "[724/1600][6/8] Loss_D: 0.7756 Loss_G: 1.9485 D(x): 0.6180 D(G(z)): 0.0691 / 0.0685\n", + "[725/1600][0/8] Loss_D: 0.7342 Loss_G: 1.8653 D(x): 0.6014 D(G(z)): 0.0756 / 0.0782\n", + "[725/1600][2/8] Loss_D: 0.7195 Loss_G: 1.7933 D(x): 0.6233 D(G(z)): 0.0850 / 0.0864\n", + "[725/1600][4/8] Loss_D: 0.7563 Loss_G: 1.8617 D(x): 0.6551 D(G(z)): 0.0810 / 0.0781\n", + "[725/1600][6/8] Loss_D: 0.7332 Loss_G: 1.8512 D(x): 0.6343 D(G(z)): 0.0804 / 0.0803\n", + "[726/1600][0/8] Loss_D: 0.7598 Loss_G: 1.8221 D(x): 0.6282 D(G(z)): 0.0851 / 0.0834\n", + "[726/1600][2/8] Loss_D: 0.7535 Loss_G: 1.8616 D(x): 0.6273 D(G(z)): 0.0795 / 0.0792\n", + "[726/1600][4/8] Loss_D: 0.7910 Loss_G: 1.8469 D(x): 0.5924 D(G(z)): 0.0816 / 0.0786\n", + "[726/1600][6/8] Loss_D: 0.7684 Loss_G: 1.8809 D(x): 0.6319 D(G(z)): 0.0773 / 0.0756\n", + "[727/1600][0/8] Loss_D: 0.7514 Loss_G: 1.9730 D(x): 0.6197 D(G(z)): 0.0684 / 0.0661\n", + "[727/1600][2/8] Loss_D: 0.7328 Loss_G: 1.9381 D(x): 0.6041 D(G(z)): 0.0683 / 0.0697\n", + "[727/1600][4/8] Loss_D: 0.7633 Loss_G: 1.9602 D(x): 0.5931 D(G(z)): 0.0680 / 0.0663\n", + "[727/1600][6/8] Loss_D: 0.7488 Loss_G: 1.9698 D(x): 0.6223 D(G(z)): 0.0668 / 0.0669\n", + "[728/1600][0/8] Loss_D: 0.7263 Loss_G: 1.7965 D(x): 0.6103 D(G(z)): 0.0840 / 0.0862\n", + "[728/1600][2/8] Loss_D: 0.7321 Loss_G: 1.8949 D(x): 0.6085 D(G(z)): 0.0731 / 0.0744\n", + "[728/1600][4/8] Loss_D: 0.6790 Loss_G: 1.8204 D(x): 0.6375 D(G(z)): 0.0794 / 0.0813\n", + "[728/1600][6/8] Loss_D: 0.7752 Loss_G: 1.8456 D(x): 0.6547 D(G(z)): 0.0834 / 0.0809\n", + "[729/1600][0/8] Loss_D: 0.7915 Loss_G: 1.8885 D(x): 0.5955 D(G(z)): 0.0784 / 0.0747\n", + "[729/1600][2/8] Loss_D: 0.7768 Loss_G: 2.0107 D(x): 0.6115 D(G(z)): 0.0659 / 0.0634\n", + "[729/1600][4/8] Loss_D: 0.7197 Loss_G: 1.8530 D(x): 0.5817 D(G(z)): 0.0741 / 0.0781\n", + "[729/1600][6/8] Loss_D: 0.6909 Loss_G: 1.8948 D(x): 0.6341 D(G(z)): 0.0726 / 0.0743\n", + "[730/1600][0/8] Loss_D: 0.7128 Loss_G: 1.8418 D(x): 0.6151 D(G(z)): 0.0783 / 0.0803\n", + "[730/1600][2/8] Loss_D: 0.7243 Loss_G: 1.7907 D(x): 0.6238 D(G(z)): 0.0841 / 0.0859\n", + "[730/1600][4/8] Loss_D: 0.7788 Loss_G: 1.8547 D(x): 0.6415 D(G(z)): 0.0810 / 0.0776\n", + "[730/1600][6/8] Loss_D: 0.7588 Loss_G: 1.9201 D(x): 0.6595 D(G(z)): 0.0776 / 0.0730\n", + "[731/1600][0/8] Loss_D: 0.7670 Loss_G: 1.9979 D(x): 0.6278 D(G(z)): 0.0660 / 0.0639\n", + "[731/1600][2/8] Loss_D: 0.6883 Loss_G: 1.9010 D(x): 0.6209 D(G(z)): 0.0719 / 0.0742\n", + "[731/1600][4/8] Loss_D: 0.7493 Loss_G: 1.9759 D(x): 0.6040 D(G(z)): 0.0681 / 0.0673\n", + "[731/1600][6/8] Loss_D: 0.7137 Loss_G: 1.8840 D(x): 0.6110 D(G(z)): 0.0739 / 0.0757\n", + "[732/1600][0/8] Loss_D: 0.7441 Loss_G: 1.9043 D(x): 0.5991 D(G(z)): 0.0731 / 0.0733\n", + "[732/1600][2/8] Loss_D: 0.7835 Loss_G: 1.8624 D(x): 0.5969 D(G(z)): 0.0786 / 0.0784\n", + "[732/1600][4/8] Loss_D: 0.7783 Loss_G: 1.9049 D(x): 0.6358 D(G(z)): 0.0769 / 0.0730\n", + "[732/1600][6/8] Loss_D: 0.7492 Loss_G: 1.9038 D(x): 0.6096 D(G(z)): 0.0741 / 0.0740\n", + "[733/1600][0/8] Loss_D: 0.7185 Loss_G: 1.8705 D(x): 0.6036 D(G(z)): 0.0740 / 0.0763\n", + "[733/1600][2/8] Loss_D: 0.7893 Loss_G: 1.8874 D(x): 0.6401 D(G(z)): 0.0779 / 0.0746\n", + "[733/1600][4/8] Loss_D: 0.7816 Loss_G: 1.9403 D(x): 0.5944 D(G(z)): 0.0732 / 0.0711\n", + "[733/1600][6/8] Loss_D: 0.7283 Loss_G: 1.9389 D(x): 0.6141 D(G(z)): 0.0699 / 0.0702\n", + "[734/1600][0/8] Loss_D: 0.7733 Loss_G: 1.9484 D(x): 0.6251 D(G(z)): 0.0712 / 0.0691\n", + "[734/1600][2/8] Loss_D: 0.7679 Loss_G: 1.8770 D(x): 0.5840 D(G(z)): 0.0759 / 0.0764\n", + "[734/1600][4/8] Loss_D: 0.7587 Loss_G: 1.9011 D(x): 0.5562 D(G(z)): 0.0704 / 0.0726\n", + "[734/1600][6/8] Loss_D: 0.7615 Loss_G: 1.8654 D(x): 0.6393 D(G(z)): 0.0785 / 0.0779\n", + "[735/1600][0/8] Loss_D: 0.6954 Loss_G: 1.8795 D(x): 0.6296 D(G(z)): 0.0742 / 0.0750\n", + "[735/1600][2/8] Loss_D: 0.7529 Loss_G: 1.9531 D(x): 0.6158 D(G(z)): 0.0703 / 0.0677\n", + "[735/1600][4/8] Loss_D: 0.7135 Loss_G: 1.9091 D(x): 0.6086 D(G(z)): 0.0708 / 0.0727\n", + "[735/1600][6/8] Loss_D: 0.7240 Loss_G: 1.8789 D(x): 0.5844 D(G(z)): 0.0728 / 0.0756\n", + "[736/1600][0/8] Loss_D: 0.7130 Loss_G: 1.8279 D(x): 0.6413 D(G(z)): 0.0798 / 0.0818\n", + "[736/1600][2/8] Loss_D: 0.7507 Loss_G: 1.8064 D(x): 0.6604 D(G(z)): 0.0857 / 0.0840\n", + "[736/1600][4/8] Loss_D: 0.8080 Loss_G: 1.8152 D(x): 0.6112 D(G(z)): 0.0866 / 0.0821\n", + "[736/1600][6/8] Loss_D: 0.7647 Loss_G: 1.9159 D(x): 0.5976 D(G(z)): 0.0749 / 0.0717\n", + "[737/1600][0/8] Loss_D: 0.7765 Loss_G: 1.9155 D(x): 0.6110 D(G(z)): 0.0727 / 0.0716\n", + "[737/1600][2/8] Loss_D: 0.7602 Loss_G: 1.8731 D(x): 0.6189 D(G(z)): 0.0770 / 0.0769\n", + "[737/1600][4/8] Loss_D: 0.7029 Loss_G: 1.9004 D(x): 0.6409 D(G(z)): 0.0729 / 0.0735\n", + "[737/1600][6/8] Loss_D: 0.7547 Loss_G: 1.8616 D(x): 0.5909 D(G(z)): 0.0777 / 0.0790\n", + "[738/1600][0/8] Loss_D: 0.6691 Loss_G: 1.7758 D(x): 0.6611 D(G(z)): 0.0852 / 0.0882\n", + "[738/1600][2/8] Loss_D: 0.7699 Loss_G: 1.8318 D(x): 0.6364 D(G(z)): 0.0836 / 0.0809\n", + "[738/1600][4/8] Loss_D: 0.7816 Loss_G: 1.8290 D(x): 0.5835 D(G(z)): 0.0844 / 0.0807\n", + "[738/1600][6/8] Loss_D: 0.7256 Loss_G: 1.9091 D(x): 0.5677 D(G(z)): 0.0695 / 0.0722\n", + "[739/1600][0/8] Loss_D: 0.7930 Loss_G: 1.8450 D(x): 0.6013 D(G(z)): 0.0800 / 0.0790\n", + "[739/1600][2/8] Loss_D: 0.7803 Loss_G: 1.8114 D(x): 0.6401 D(G(z)): 0.0879 / 0.0858\n", + "[739/1600][4/8] Loss_D: 0.7493 Loss_G: 1.8542 D(x): 0.5852 D(G(z)): 0.0784 / 0.0780\n", + "[739/1600][6/8] Loss_D: 0.7022 Loss_G: 1.8290 D(x): 0.6136 D(G(z)): 0.0800 / 0.0821\n", + "[740/1600][0/8] Loss_D: 0.6815 Loss_G: 1.8823 D(x): 0.6466 D(G(z)): 0.0751 / 0.0756\n", + "[740/1600][2/8] Loss_D: 0.7803 Loss_G: 1.8454 D(x): 0.6322 D(G(z)): 0.0820 / 0.0791\n", + "[740/1600][4/8] Loss_D: 0.7075 Loss_G: 1.8578 D(x): 0.6193 D(G(z)): 0.0802 / 0.0803\n", + "[740/1600][6/8] Loss_D: 0.7395 Loss_G: 1.8730 D(x): 0.5930 D(G(z)): 0.0761 / 0.0774\n", + "[741/1600][0/8] Loss_D: 0.7842 Loss_G: 1.9523 D(x): 0.6219 D(G(z)): 0.0715 / 0.0679\n", + "[741/1600][2/8] Loss_D: 0.6864 Loss_G: 1.8483 D(x): 0.6217 D(G(z)): 0.0770 / 0.0795\n", + "[741/1600][4/8] Loss_D: 0.7621 Loss_G: 1.8012 D(x): 0.6431 D(G(z)): 0.0844 / 0.0849\n", + "[741/1600][6/8] Loss_D: 0.6971 Loss_G: 1.7523 D(x): 0.6402 D(G(z)): 0.0907 / 0.0911\n", + "[742/1600][0/8] Loss_D: 0.7424 Loss_G: 1.8446 D(x): 0.6228 D(G(z)): 0.0801 / 0.0799\n", + "[742/1600][2/8] Loss_D: 0.7161 Loss_G: 1.7980 D(x): 0.5954 D(G(z)): 0.0832 / 0.0852\n", + "[742/1600][4/8] Loss_D: 0.7196 Loss_G: 1.7541 D(x): 0.6240 D(G(z)): 0.0885 / 0.0909\n", + "[742/1600][6/8] Loss_D: 0.7209 Loss_G: 1.7674 D(x): 0.6683 D(G(z)): 0.0919 / 0.0888\n", + "[743/1600][0/8] Loss_D: 0.7636 Loss_G: 1.7924 D(x): 0.6381 D(G(z)): 0.0886 / 0.0859\n", + "[743/1600][2/8] Loss_D: 0.7097 Loss_G: 1.7969 D(x): 0.6198 D(G(z)): 0.0833 / 0.0846\n", + "[743/1600][4/8] Loss_D: 0.7402 Loss_G: 1.8042 D(x): 0.6592 D(G(z)): 0.0866 / 0.0851\n", + "[743/1600][6/8] Loss_D: 0.7422 Loss_G: 1.7829 D(x): 0.6027 D(G(z)): 0.0883 / 0.0888\n", + "[744/1600][0/8] Loss_D: 0.7346 Loss_G: 1.8327 D(x): 0.6439 D(G(z)): 0.0846 / 0.0823\n", + "[744/1600][2/8] Loss_D: 0.7850 Loss_G: 1.8995 D(x): 0.6493 D(G(z)): 0.0762 / 0.0732\n", + "[744/1600][4/8] Loss_D: 0.7790 Loss_G: 1.9253 D(x): 0.5580 D(G(z)): 0.0725 / 0.0708\n", + "[744/1600][6/8] Loss_D: 0.7866 Loss_G: 1.8797 D(x): 0.6386 D(G(z)): 0.0783 / 0.0758\n", + "[745/1600][0/8] Loss_D: 0.7396 Loss_G: 1.9104 D(x): 0.5720 D(G(z)): 0.0722 / 0.0744\n", + "[745/1600][2/8] Loss_D: 0.7649 Loss_G: 1.8487 D(x): 0.5650 D(G(z)): 0.0760 / 0.0784\n", + "[745/1600][4/8] Loss_D: 0.7809 Loss_G: 1.7795 D(x): 0.6222 D(G(z)): 0.0880 / 0.0873\n", + "[745/1600][6/8] Loss_D: 0.6954 Loss_G: 1.7443 D(x): 0.6536 D(G(z)): 0.0925 / 0.0932\n", + "[746/1600][0/8] Loss_D: 0.7502 Loss_G: 1.7822 D(x): 0.5847 D(G(z)): 0.0872 / 0.0883\n", + "[746/1600][2/8] Loss_D: 0.7703 Loss_G: 1.8228 D(x): 0.6455 D(G(z)): 0.0869 / 0.0831\n", + "[746/1600][4/8] Loss_D: 0.7489 Loss_G: 1.9021 D(x): 0.5975 D(G(z)): 0.0757 / 0.0738\n", + "[746/1600][6/8] Loss_D: 0.7788 Loss_G: 1.9233 D(x): 0.5922 D(G(z)): 0.0728 / 0.0711\n", + "[747/1600][0/8] Loss_D: 0.7266 Loss_G: 1.8584 D(x): 0.5917 D(G(z)): 0.0764 / 0.0781\n", + "[747/1600][2/8] Loss_D: 0.7876 Loss_G: 1.9162 D(x): 0.5808 D(G(z)): 0.0745 / 0.0726\n", + "[747/1600][4/8] Loss_D: 0.7180 Loss_G: 1.8926 D(x): 0.5824 D(G(z)): 0.0728 / 0.0750\n", + "[747/1600][6/8] Loss_D: 0.7446 Loss_G: 1.8978 D(x): 0.5884 D(G(z)): 0.0717 / 0.0730\n", + "[748/1600][0/8] Loss_D: 0.7943 Loss_G: 1.8382 D(x): 0.5989 D(G(z)): 0.0798 / 0.0800\n", + "[748/1600][2/8] Loss_D: 0.7022 Loss_G: 1.8461 D(x): 0.6717 D(G(z)): 0.0807 / 0.0809\n", + "[748/1600][4/8] Loss_D: 0.7061 Loss_G: 1.7755 D(x): 0.6052 D(G(z)): 0.0850 / 0.0878\n", + "[748/1600][6/8] Loss_D: 0.7407 Loss_G: 1.7817 D(x): 0.6452 D(G(z)): 0.0878 / 0.0864\n", + "[749/1600][0/8] Loss_D: 0.7770 Loss_G: 1.8175 D(x): 0.6783 D(G(z)): 0.0860 / 0.0828\n", + "[749/1600][2/8] Loss_D: 0.7490 Loss_G: 1.9144 D(x): 0.6199 D(G(z)): 0.0746 / 0.0725\n", + "[749/1600][4/8] Loss_D: 0.7495 Loss_G: 1.9186 D(x): 0.5754 D(G(z)): 0.0722 / 0.0715\n", + "[749/1600][6/8] Loss_D: 0.7635 Loss_G: 1.9631 D(x): 0.6364 D(G(z)): 0.0691 / 0.0669\n", + "[750/1600][0/8] Loss_D: 0.7507 Loss_G: 1.9967 D(x): 0.5845 D(G(z)): 0.0631 / 0.0635\n", + "[750/1600][2/8] Loss_D: 0.7073 Loss_G: 1.9116 D(x): 0.6347 D(G(z)): 0.0712 / 0.0721\n", + "[750/1600][4/8] Loss_D: 0.7154 Loss_G: 1.8948 D(x): 0.5826 D(G(z)): 0.0720 / 0.0749\n", + "[750/1600][6/8] Loss_D: 0.7145 Loss_G: 1.8159 D(x): 0.6012 D(G(z)): 0.0784 / 0.0831\n", + "[751/1600][0/8] Loss_D: 0.7321 Loss_G: 1.7358 D(x): 0.6603 D(G(z)): 0.0947 / 0.0953\n", + "[751/1600][2/8] Loss_D: 0.7418 Loss_G: 1.7437 D(x): 0.6420 D(G(z)): 0.0939 / 0.0911\n", + "[751/1600][4/8] Loss_D: 0.7444 Loss_G: 1.8567 D(x): 0.6210 D(G(z)): 0.0810 / 0.0788\n", + "[751/1600][6/8] Loss_D: 0.6921 Loss_G: 1.7977 D(x): 0.6349 D(G(z)): 0.0821 / 0.0839\n", + "[752/1600][0/8] Loss_D: 0.7895 Loss_G: 1.8473 D(x): 0.6031 D(G(z)): 0.0818 / 0.0789\n", + "[752/1600][2/8] Loss_D: 0.7547 Loss_G: 1.8289 D(x): 0.6172 D(G(z)): 0.0849 / 0.0818\n", + "[752/1600][4/8] Loss_D: 0.7511 Loss_G: 1.9232 D(x): 0.5863 D(G(z)): 0.0708 / 0.0712\n", + "[752/1600][6/8] Loss_D: 0.7653 Loss_G: 1.7930 D(x): 0.5803 D(G(z)): 0.0844 / 0.0859\n", + "[753/1600][0/8] Loss_D: 0.7204 Loss_G: 1.8361 D(x): 0.6032 D(G(z)): 0.0792 / 0.0810\n", + "[753/1600][2/8] Loss_D: 0.7624 Loss_G: 1.8280 D(x): 0.6378 D(G(z)): 0.0827 / 0.0816\n", + "[753/1600][4/8] Loss_D: 0.7321 Loss_G: 1.7763 D(x): 0.6462 D(G(z)): 0.0890 / 0.0880\n", + "[753/1600][6/8] Loss_D: 0.7024 Loss_G: 1.8098 D(x): 0.6639 D(G(z)): 0.0826 / 0.0833\n", + "[754/1600][0/8] Loss_D: 0.7087 Loss_G: 1.8161 D(x): 0.6357 D(G(z)): 0.0838 / 0.0836\n", + "[754/1600][2/8] Loss_D: 0.7130 Loss_G: 1.8584 D(x): 0.6283 D(G(z)): 0.0783 / 0.0774\n", + "[754/1600][4/8] Loss_D: 0.7128 Loss_G: 1.8513 D(x): 0.6082 D(G(z)): 0.0768 / 0.0786\n", + "[754/1600][6/8] Loss_D: 0.7503 Loss_G: 1.7777 D(x): 0.5638 D(G(z)): 0.0852 / 0.0881\n", + "[755/1600][0/8] Loss_D: 0.6627 Loss_G: 1.8127 D(x): 0.6689 D(G(z)): 0.0812 / 0.0821\n", + "[755/1600][2/8] Loss_D: 0.7003 Loss_G: 1.8002 D(x): 0.6246 D(G(z)): 0.0850 / 0.0857\n", + "[755/1600][4/8] Loss_D: 0.7871 Loss_G: 1.8252 D(x): 0.6082 D(G(z)): 0.0850 / 0.0821\n", + "[755/1600][6/8] Loss_D: 0.7720 Loss_G: 1.8492 D(x): 0.6288 D(G(z)): 0.0826 / 0.0791\n", + "[756/1600][0/8] Loss_D: 0.7386 Loss_G: 1.8819 D(x): 0.6041 D(G(z)): 0.0771 / 0.0752\n", + "[756/1600][2/8] Loss_D: 0.7834 Loss_G: 1.9346 D(x): 0.6409 D(G(z)): 0.0730 / 0.0690\n", + "[756/1600][4/8] Loss_D: 0.7514 Loss_G: 1.9310 D(x): 0.5533 D(G(z)): 0.0680 / 0.0700\n", + "[756/1600][6/8] Loss_D: 0.7710 Loss_G: 1.8912 D(x): 0.5957 D(G(z)): 0.0748 / 0.0743\n", + "[757/1600][0/8] Loss_D: 0.7623 Loss_G: 1.9151 D(x): 0.6115 D(G(z)): 0.0724 / 0.0719\n", + "[757/1600][2/8] Loss_D: 0.7467 Loss_G: 1.9132 D(x): 0.5945 D(G(z)): 0.0722 / 0.0727\n", + "[757/1600][4/8] Loss_D: 0.7654 Loss_G: 1.8261 D(x): 0.6253 D(G(z)): 0.0810 / 0.0813\n", + "[757/1600][6/8] Loss_D: 0.7491 Loss_G: 1.8400 D(x): 0.6299 D(G(z)): 0.0802 / 0.0801\n", + "[758/1600][0/8] Loss_D: 0.7703 Loss_G: 1.9036 D(x): 0.5985 D(G(z)): 0.0749 / 0.0735\n", + "[758/1600][2/8] Loss_D: 0.7349 Loss_G: 1.8636 D(x): 0.6292 D(G(z)): 0.0781 / 0.0785\n", + "[758/1600][4/8] Loss_D: 0.7727 Loss_G: 1.7723 D(x): 0.6063 D(G(z)): 0.0902 / 0.0896\n", + "[758/1600][6/8] Loss_D: 0.7644 Loss_G: 1.8195 D(x): 0.6188 D(G(z)): 0.0852 / 0.0837\n", + "[759/1600][0/8] Loss_D: 0.7260 Loss_G: 1.8498 D(x): 0.6162 D(G(z)): 0.0788 / 0.0781\n", + "[759/1600][2/8] Loss_D: 0.7642 Loss_G: 1.8914 D(x): 0.6068 D(G(z)): 0.0761 / 0.0750\n", + "[759/1600][4/8] Loss_D: 0.7171 Loss_G: 1.8481 D(x): 0.6135 D(G(z)): 0.0790 / 0.0790\n", + "[759/1600][6/8] Loss_D: 0.7423 Loss_G: 1.9126 D(x): 0.5967 D(G(z)): 0.0725 / 0.0728\n", + "[760/1600][0/8] Loss_D: 0.7618 Loss_G: 1.8149 D(x): 0.5770 D(G(z)): 0.0829 / 0.0840\n", + "[760/1600][2/8] Loss_D: 0.7757 Loss_G: 1.9599 D(x): 0.5696 D(G(z)): 0.0685 / 0.0670\n", + "[760/1600][4/8] Loss_D: 0.7725 Loss_G: 1.9101 D(x): 0.6357 D(G(z)): 0.0762 / 0.0732\n", + "[760/1600][6/8] Loss_D: 0.7937 Loss_G: 1.8834 D(x): 0.5550 D(G(z)): 0.0777 / 0.0769\n", + "[761/1600][0/8] Loss_D: 0.7501 Loss_G: 1.9783 D(x): 0.5738 D(G(z)): 0.0650 / 0.0653\n", + "[761/1600][2/8] Loss_D: 0.7515 Loss_G: 1.8800 D(x): 0.6189 D(G(z)): 0.0773 / 0.0773\n", + "[761/1600][4/8] Loss_D: 0.7503 Loss_G: 1.8791 D(x): 0.5814 D(G(z)): 0.0736 / 0.0761\n", + "[761/1600][6/8] Loss_D: 0.7869 Loss_G: 1.9098 D(x): 0.6385 D(G(z)): 0.0747 / 0.0718\n", + "[762/1600][0/8] Loss_D: 0.7818 Loss_G: 1.9241 D(x): 0.6352 D(G(z)): 0.0733 / 0.0706\n", + "[762/1600][2/8] Loss_D: 0.7610 Loss_G: 1.9330 D(x): 0.5840 D(G(z)): 0.0715 / 0.0705\n", + "[762/1600][4/8] Loss_D: 0.7863 Loss_G: 1.8778 D(x): 0.6004 D(G(z)): 0.0792 / 0.0771\n", + "[762/1600][6/8] Loss_D: 0.7704 Loss_G: 1.8520 D(x): 0.5506 D(G(z)): 0.0772 / 0.0797\n", + "[763/1600][0/8] Loss_D: 0.6968 Loss_G: 1.8188 D(x): 0.6178 D(G(z)): 0.0804 / 0.0839\n", + "[763/1600][2/8] Loss_D: 0.7369 Loss_G: 1.8018 D(x): 0.5862 D(G(z)): 0.0824 / 0.0847\n", + "[763/1600][4/8] Loss_D: 0.7683 Loss_G: 1.8856 D(x): 0.5804 D(G(z)): 0.0753 / 0.0747\n", + "[763/1600][6/8] Loss_D: 0.7891 Loss_G: 1.8975 D(x): 0.6444 D(G(z)): 0.0790 / 0.0742\n", + "[764/1600][0/8] Loss_D: 0.7488 Loss_G: 1.9175 D(x): 0.5953 D(G(z)): 0.0716 / 0.0719\n", + "[764/1600][2/8] Loss_D: 0.6711 Loss_G: 1.9189 D(x): 0.6320 D(G(z)): 0.0700 / 0.0723\n", + "[764/1600][4/8] Loss_D: 0.7394 Loss_G: 1.8430 D(x): 0.6392 D(G(z)): 0.0810 / 0.0810\n", + "[764/1600][6/8] Loss_D: 0.7810 Loss_G: 1.9022 D(x): 0.5915 D(G(z)): 0.0754 / 0.0723\n", + "[765/1600][0/8] Loss_D: 0.7153 Loss_G: 1.9169 D(x): 0.6000 D(G(z)): 0.0704 / 0.0709\n", + "[765/1600][2/8] Loss_D: 0.7235 Loss_G: 1.9107 D(x): 0.6143 D(G(z)): 0.0716 / 0.0720\n", + "[765/1600][4/8] Loss_D: 0.7753 Loss_G: 1.8910 D(x): 0.6356 D(G(z)): 0.0760 / 0.0736\n", + "[765/1600][6/8] Loss_D: 0.7361 Loss_G: 1.8143 D(x): 0.5929 D(G(z)): 0.0831 / 0.0841\n", + "[766/1600][0/8] Loss_D: 0.7901 Loss_G: 1.8394 D(x): 0.6180 D(G(z)): 0.0828 / 0.0807\n", + "[766/1600][2/8] Loss_D: 0.7754 Loss_G: 1.9014 D(x): 0.6285 D(G(z)): 0.0767 / 0.0750\n", + "[766/1600][4/8] Loss_D: 0.7671 Loss_G: 1.9483 D(x): 0.5725 D(G(z)): 0.0689 / 0.0680\n", + "[766/1600][6/8] Loss_D: 0.7123 Loss_G: 1.8647 D(x): 0.6219 D(G(z)): 0.0770 / 0.0785\n", + "[767/1600][0/8] Loss_D: 0.7492 Loss_G: 1.8807 D(x): 0.6399 D(G(z)): 0.0745 / 0.0742\n", + "[767/1600][2/8] Loss_D: 0.7181 Loss_G: 1.8137 D(x): 0.5922 D(G(z)): 0.0814 / 0.0848\n", + "[767/1600][4/8] Loss_D: 0.7320 Loss_G: 1.8132 D(x): 0.5962 D(G(z)): 0.0791 / 0.0825\n", + "[767/1600][6/8] Loss_D: 0.7404 Loss_G: 1.7829 D(x): 0.6125 D(G(z)): 0.0875 / 0.0867\n", + "[768/1600][0/8] Loss_D: 0.7132 Loss_G: 1.7349 D(x): 0.6027 D(G(z)): 0.0912 / 0.0945\n", + "[768/1600][2/8] Loss_D: 0.7102 Loss_G: 1.7281 D(x): 0.6667 D(G(z)): 0.0956 / 0.0943\n", + "[768/1600][4/8] Loss_D: 0.7606 Loss_G: 1.7449 D(x): 0.6309 D(G(z)): 0.0952 / 0.0933\n", + "[768/1600][6/8] Loss_D: 0.7543 Loss_G: 1.8697 D(x): 0.6701 D(G(z)): 0.0820 / 0.0778\n", + "[769/1600][0/8] Loss_D: 0.7810 Loss_G: 1.8837 D(x): 0.6225 D(G(z)): 0.0777 / 0.0748\n", + "[769/1600][2/8] Loss_D: 0.7143 Loss_G: 1.8666 D(x): 0.5957 D(G(z)): 0.0752 / 0.0774\n", + "[769/1600][4/8] Loss_D: 0.7336 Loss_G: 1.8437 D(x): 0.6399 D(G(z)): 0.0801 / 0.0797\n", + "[769/1600][6/8] Loss_D: 0.6827 Loss_G: 1.9016 D(x): 0.6290 D(G(z)): 0.0731 / 0.0740\n", + "[770/1600][0/8] Loss_D: 0.7466 Loss_G: 1.8052 D(x): 0.5641 D(G(z)): 0.0821 / 0.0855\n", + "[770/1600][2/8] Loss_D: 0.7789 Loss_G: 1.8754 D(x): 0.6244 D(G(z)): 0.0790 / 0.0767\n", + "[770/1600][4/8] Loss_D: 0.7495 Loss_G: 1.8699 D(x): 0.6388 D(G(z)): 0.0778 / 0.0761\n", + "[770/1600][6/8] Loss_D: 0.7483 Loss_G: 1.8956 D(x): 0.5983 D(G(z)): 0.0746 / 0.0746\n", + "[771/1600][0/8] Loss_D: 0.7564 Loss_G: 1.9385 D(x): 0.5842 D(G(z)): 0.0719 / 0.0710\n", + "[771/1600][2/8] Loss_D: 0.7653 Loss_G: 1.9478 D(x): 0.5898 D(G(z)): 0.0713 / 0.0700\n", + "[771/1600][4/8] Loss_D: 0.7142 Loss_G: 1.8705 D(x): 0.5889 D(G(z)): 0.0740 / 0.0770\n", + "[771/1600][6/8] Loss_D: 0.6871 Loss_G: 1.8663 D(x): 0.6315 D(G(z)): 0.0753 / 0.0782\n", + "[772/1600][0/8] Loss_D: 0.7693 Loss_G: 1.8579 D(x): 0.6191 D(G(z)): 0.0800 / 0.0779\n", + "[772/1600][2/8] Loss_D: 0.7522 Loss_G: 1.8381 D(x): 0.5814 D(G(z)): 0.0799 / 0.0815\n", + "[772/1600][4/8] Loss_D: 0.7526 Loss_G: 1.8140 D(x): 0.6202 D(G(z)): 0.0850 / 0.0840\n", + "[772/1600][6/8] Loss_D: 0.7845 Loss_G: 1.9298 D(x): 0.6299 D(G(z)): 0.0745 / 0.0703\n", + "[773/1600][0/8] Loss_D: 0.7566 Loss_G: 1.9390 D(x): 0.6297 D(G(z)): 0.0721 / 0.0702\n", + "[773/1600][2/8] Loss_D: 0.7028 Loss_G: 1.9223 D(x): 0.6038 D(G(z)): 0.0690 / 0.0708\n", + "[773/1600][4/8] Loss_D: 0.7089 Loss_G: 1.8931 D(x): 0.6143 D(G(z)): 0.0742 / 0.0761\n", + "[773/1600][6/8] Loss_D: 0.7299 Loss_G: 1.8160 D(x): 0.6037 D(G(z)): 0.0802 / 0.0818\n", + "[774/1600][0/8] Loss_D: 0.7721 Loss_G: 1.7794 D(x): 0.5996 D(G(z)): 0.0879 / 0.0875\n", + "[774/1600][2/8] Loss_D: 0.7649 Loss_G: 1.8574 D(x): 0.6038 D(G(z)): 0.0802 / 0.0778\n", + "[774/1600][4/8] Loss_D: 0.7932 Loss_G: 1.8661 D(x): 0.6447 D(G(z)): 0.0809 / 0.0779\n", + "[774/1600][6/8] Loss_D: 0.7602 Loss_G: 1.9022 D(x): 0.5873 D(G(z)): 0.0732 / 0.0729\n", + "[775/1600][0/8] Loss_D: 0.7473 Loss_G: 1.8960 D(x): 0.6103 D(G(z)): 0.0736 / 0.0734\n", + "[775/1600][2/8] Loss_D: 0.6859 Loss_G: 1.8881 D(x): 0.6203 D(G(z)): 0.0720 / 0.0746\n", + "[775/1600][4/8] Loss_D: 0.7225 Loss_G: 1.8225 D(x): 0.6264 D(G(z)): 0.0802 / 0.0814\n", + "[775/1600][6/8] Loss_D: 0.7373 Loss_G: 1.7344 D(x): 0.6519 D(G(z)): 0.0942 / 0.0942\n", + "[776/1600][0/8] Loss_D: 0.7142 Loss_G: 1.7411 D(x): 0.6348 D(G(z)): 0.0934 / 0.0936\n", + "[776/1600][2/8] Loss_D: 0.6794 Loss_G: 1.8023 D(x): 0.6394 D(G(z)): 0.0848 / 0.0854\n", + "[776/1600][4/8] Loss_D: 0.7068 Loss_G: 1.8157 D(x): 0.6022 D(G(z)): 0.0829 / 0.0837\n", + "[776/1600][6/8] Loss_D: 0.7747 Loss_G: 1.8758 D(x): 0.6747 D(G(z)): 0.0800 / 0.0768\n", + "[777/1600][0/8] Loss_D: 0.7853 Loss_G: 1.8947 D(x): 0.6391 D(G(z)): 0.0789 / 0.0751\n", + "[777/1600][2/8] Loss_D: 0.6856 Loss_G: 1.8751 D(x): 0.6216 D(G(z)): 0.0744 / 0.0763\n", + "[777/1600][4/8] Loss_D: 0.7084 Loss_G: 1.8943 D(x): 0.6485 D(G(z)): 0.0760 / 0.0756\n", + "[777/1600][6/8] Loss_D: 0.7586 Loss_G: 1.8779 D(x): 0.5888 D(G(z)): 0.0764 / 0.0763\n", + "[778/1600][0/8] Loss_D: 0.6803 Loss_G: 1.8183 D(x): 0.6482 D(G(z)): 0.0810 / 0.0834\n", + "[778/1600][2/8] Loss_D: 0.7516 Loss_G: 1.8373 D(x): 0.6033 D(G(z)): 0.0815 / 0.0813\n", + "[778/1600][4/8] Loss_D: 0.7036 Loss_G: 1.8823 D(x): 0.6232 D(G(z)): 0.0750 / 0.0750\n", + "[778/1600][6/8] Loss_D: 0.7821 Loss_G: 1.8559 D(x): 0.5880 D(G(z)): 0.0795 / 0.0791\n", + "[779/1600][0/8] Loss_D: 0.7209 Loss_G: 1.7759 D(x): 0.6242 D(G(z)): 0.0877 / 0.0881\n", + "[779/1600][2/8] Loss_D: 0.7875 Loss_G: 1.8860 D(x): 0.6364 D(G(z)): 0.0769 / 0.0746\n", + "[779/1600][4/8] Loss_D: 0.7740 Loss_G: 1.8674 D(x): 0.5941 D(G(z)): 0.0797 / 0.0777\n", + "[779/1600][6/8] Loss_D: 0.7692 Loss_G: 1.8802 D(x): 0.6449 D(G(z)): 0.0793 / 0.0763\n", + "[780/1600][0/8] Loss_D: 0.7640 Loss_G: 1.8900 D(x): 0.6259 D(G(z)): 0.0763 / 0.0757\n", + "[780/1600][2/8] Loss_D: 0.7352 Loss_G: 1.9054 D(x): 0.6024 D(G(z)): 0.0745 / 0.0738\n", + "[780/1600][4/8] Loss_D: 0.7654 Loss_G: 1.9049 D(x): 0.5709 D(G(z)): 0.0725 / 0.0729\n", + "[780/1600][6/8] Loss_D: 0.7688 Loss_G: 1.8895 D(x): 0.5888 D(G(z)): 0.0758 / 0.0758\n", + "[781/1600][0/8] Loss_D: 0.6957 Loss_G: 1.9262 D(x): 0.6234 D(G(z)): 0.0681 / 0.0697\n", + "[781/1600][2/8] Loss_D: 0.7242 Loss_G: 1.9000 D(x): 0.5871 D(G(z)): 0.0725 / 0.0741\n", + "[781/1600][4/8] Loss_D: 0.7621 Loss_G: 1.8522 D(x): 0.6280 D(G(z)): 0.0806 / 0.0788\n", + "[781/1600][6/8] Loss_D: 0.7452 Loss_G: 1.8878 D(x): 0.6246 D(G(z)): 0.0752 / 0.0744\n", + "[782/1600][0/8] Loss_D: 0.7115 Loss_G: 1.9124 D(x): 0.6159 D(G(z)): 0.0728 / 0.0720\n", + "[782/1600][2/8] Loss_D: 0.7071 Loss_G: 1.8783 D(x): 0.6159 D(G(z)): 0.0748 / 0.0764\n", + "[782/1600][4/8] Loss_D: 0.7472 Loss_G: 1.9025 D(x): 0.6092 D(G(z)): 0.0750 / 0.0734\n", + "[782/1600][6/8] Loss_D: 0.7653 Loss_G: 1.9315 D(x): 0.5838 D(G(z)): 0.0716 / 0.0716\n", + "[783/1600][0/8] Loss_D: 0.7683 Loss_G: 1.9133 D(x): 0.6125 D(G(z)): 0.0748 / 0.0720\n", + "[783/1600][2/8] Loss_D: 0.7437 Loss_G: 1.9370 D(x): 0.5759 D(G(z)): 0.0686 / 0.0698\n", + "[783/1600][4/8] Loss_D: 0.7778 Loss_G: 1.9386 D(x): 0.6456 D(G(z)): 0.0718 / 0.0692\n", + "[783/1600][6/8] Loss_D: 0.7749 Loss_G: 1.9035 D(x): 0.6118 D(G(z)): 0.0770 / 0.0749\n", + "[784/1600][0/8] Loss_D: 0.7230 Loss_G: 1.9497 D(x): 0.5937 D(G(z)): 0.0686 / 0.0682\n", + "[784/1600][2/8] Loss_D: 0.7718 Loss_G: 1.9986 D(x): 0.6169 D(G(z)): 0.0668 / 0.0648\n", + "[784/1600][4/8] Loss_D: 0.7422 Loss_G: 2.0286 D(x): 0.5510 D(G(z)): 0.0591 / 0.0611\n", + "[784/1600][6/8] Loss_D: 0.7616 Loss_G: 2.0141 D(x): 0.5922 D(G(z)): 0.0629 / 0.0618\n", + "[785/1600][0/8] Loss_D: 0.7574 Loss_G: 1.9548 D(x): 0.6160 D(G(z)): 0.0666 / 0.0676\n", + "[785/1600][2/8] Loss_D: 0.6966 Loss_G: 1.8721 D(x): 0.6595 D(G(z)): 0.0750 / 0.0760\n", + "[785/1600][4/8] Loss_D: 0.7741 Loss_G: 1.8754 D(x): 0.5640 D(G(z)): 0.0765 / 0.0769\n", + "[785/1600][6/8] Loss_D: 0.7419 Loss_G: 1.8692 D(x): 0.5768 D(G(z)): 0.0761 / 0.0784\n", + "[786/1600][0/8] Loss_D: 0.6909 Loss_G: 1.8074 D(x): 0.6736 D(G(z)): 0.0842 / 0.0848\n", + "[786/1600][2/8] Loss_D: 0.7470 Loss_G: 1.8395 D(x): 0.6605 D(G(z)): 0.0832 / 0.0818\n", + "[786/1600][4/8] Loss_D: 0.7293 Loss_G: 1.8262 D(x): 0.6244 D(G(z)): 0.0833 / 0.0829\n", + "[786/1600][6/8] Loss_D: 0.7955 Loss_G: 1.8364 D(x): 0.6182 D(G(z)): 0.0829 / 0.0815\n", + "[787/1600][0/8] Loss_D: 0.7957 Loss_G: 1.8657 D(x): 0.6427 D(G(z)): 0.0814 / 0.0774\n", + "[787/1600][2/8] Loss_D: 0.7125 Loss_G: 1.8787 D(x): 0.6221 D(G(z)): 0.0763 / 0.0759\n", + "[787/1600][4/8] Loss_D: 0.7650 Loss_G: 1.8779 D(x): 0.6410 D(G(z)): 0.0787 / 0.0772\n", + "[787/1600][6/8] Loss_D: 0.6840 Loss_G: 1.8891 D(x): 0.6214 D(G(z)): 0.0730 / 0.0742\n", + "[788/1600][0/8] Loss_D: 0.7703 Loss_G: 1.8032 D(x): 0.6124 D(G(z)): 0.0861 / 0.0867\n", + "[788/1600][2/8] Loss_D: 0.7154 Loss_G: 1.9073 D(x): 0.6000 D(G(z)): 0.0723 / 0.0724\n", + "[788/1600][4/8] Loss_D: 0.7537 Loss_G: 1.9144 D(x): 0.6326 D(G(z)): 0.0737 / 0.0723\n", + "[788/1600][6/8] Loss_D: 0.7075 Loss_G: 1.9750 D(x): 0.5845 D(G(z)): 0.0641 / 0.0659\n", + "[789/1600][0/8] Loss_D: 0.6858 Loss_G: 1.8939 D(x): 0.6346 D(G(z)): 0.0723 / 0.0742\n", + "[789/1600][2/8] Loss_D: 0.7673 Loss_G: 1.8871 D(x): 0.6302 D(G(z)): 0.0775 / 0.0752\n", + "[789/1600][4/8] Loss_D: 0.7909 Loss_G: 1.8463 D(x): 0.6220 D(G(z)): 0.0834 / 0.0809\n", + "[789/1600][6/8] Loss_D: 0.7155 Loss_G: 1.9338 D(x): 0.5995 D(G(z)): 0.0695 / 0.0697\n", + "[790/1600][0/8] Loss_D: 0.7544 Loss_G: 1.8328 D(x): 0.6205 D(G(z)): 0.0818 / 0.0816\n", + "[790/1600][2/8] Loss_D: 0.7287 Loss_G: 1.8827 D(x): 0.6147 D(G(z)): 0.0763 / 0.0757\n", + "[790/1600][4/8] Loss_D: 0.7841 Loss_G: 1.8892 D(x): 0.6377 D(G(z)): 0.0776 / 0.0743\n", + "[790/1600][6/8] Loss_D: 0.7172 Loss_G: 1.8706 D(x): 0.6054 D(G(z)): 0.0760 / 0.0772\n", + "[791/1600][0/8] Loss_D: 0.7596 Loss_G: 1.8644 D(x): 0.5820 D(G(z)): 0.0759 / 0.0770\n", + "[791/1600][2/8] Loss_D: 0.7440 Loss_G: 1.8917 D(x): 0.6510 D(G(z)): 0.0755 / 0.0746\n", + "[791/1600][4/8] Loss_D: 0.7032 Loss_G: 1.8735 D(x): 0.6379 D(G(z)): 0.0783 / 0.0771\n", + "[791/1600][6/8] Loss_D: 0.7572 Loss_G: 1.9285 D(x): 0.6085 D(G(z)): 0.0714 / 0.0703\n", + "[792/1600][0/8] Loss_D: 0.7256 Loss_G: 1.8733 D(x): 0.6007 D(G(z)): 0.0765 / 0.0773\n", + "[792/1600][2/8] Loss_D: 0.7660 Loss_G: 1.9267 D(x): 0.5856 D(G(z)): 0.0715 / 0.0713\n", + "[792/1600][4/8] Loss_D: 0.7427 Loss_G: 1.9272 D(x): 0.5877 D(G(z)): 0.0714 / 0.0722\n", + "[792/1600][6/8] Loss_D: 0.7488 Loss_G: 1.8743 D(x): 0.6669 D(G(z)): 0.0771 / 0.0767\n", + "[793/1600][0/8] Loss_D: 0.7215 Loss_G: 1.9275 D(x): 0.6241 D(G(z)): 0.0719 / 0.0704\n", + "[793/1600][2/8] Loss_D: 0.7462 Loss_G: 1.9478 D(x): 0.6175 D(G(z)): 0.0712 / 0.0691\n", + "[793/1600][4/8] Loss_D: 0.7400 Loss_G: 1.9739 D(x): 0.5598 D(G(z)): 0.0628 / 0.0664\n", + "[793/1600][6/8] Loss_D: 0.7276 Loss_G: 1.8849 D(x): 0.6519 D(G(z)): 0.0750 / 0.0753\n", + "[794/1600][0/8] Loss_D: 0.7021 Loss_G: 1.8343 D(x): 0.6477 D(G(z)): 0.0797 / 0.0801\n", + "[794/1600][2/8] Loss_D: 0.7393 Loss_G: 1.8478 D(x): 0.6146 D(G(z)): 0.0791 / 0.0787\n", + "[794/1600][4/8] Loss_D: 0.7662 Loss_G: 1.9224 D(x): 0.6476 D(G(z)): 0.0746 / 0.0722\n", + "[794/1600][6/8] Loss_D: 0.7284 Loss_G: 1.9113 D(x): 0.6346 D(G(z)): 0.0718 / 0.0719\n", + "[795/1600][0/8] Loss_D: 0.7716 Loss_G: 1.9139 D(x): 0.6161 D(G(z)): 0.0743 / 0.0725\n", + "[795/1600][2/8] Loss_D: 0.7448 Loss_G: 1.9313 D(x): 0.5939 D(G(z)): 0.0727 / 0.0715\n", + "[795/1600][4/8] Loss_D: 0.7230 Loss_G: 1.9081 D(x): 0.6109 D(G(z)): 0.0730 / 0.0731\n", + "[795/1600][6/8] Loss_D: 0.7823 Loss_G: 1.9533 D(x): 0.6138 D(G(z)): 0.0713 / 0.0683\n", + "[796/1600][0/8] Loss_D: 0.7404 Loss_G: 1.9431 D(x): 0.5816 D(G(z)): 0.0677 / 0.0695\n", + "[796/1600][2/8] Loss_D: 0.7078 Loss_G: 1.8792 D(x): 0.6355 D(G(z)): 0.0736 / 0.0760\n", + "[796/1600][4/8] Loss_D: 0.7140 Loss_G: 1.8733 D(x): 0.6522 D(G(z)): 0.0764 / 0.0767\n", + "[796/1600][6/8] Loss_D: 0.7220 Loss_G: 1.8931 D(x): 0.6124 D(G(z)): 0.0740 / 0.0742\n", + "[797/1600][0/8] Loss_D: 0.7650 Loss_G: 1.9265 D(x): 0.6476 D(G(z)): 0.0747 / 0.0717\n", + "[797/1600][2/8] Loss_D: 0.7081 Loss_G: 1.8992 D(x): 0.6279 D(G(z)): 0.0748 / 0.0751\n", + "[797/1600][4/8] Loss_D: 0.7069 Loss_G: 1.8438 D(x): 0.6049 D(G(z)): 0.0773 / 0.0803\n", + "[797/1600][6/8] Loss_D: 0.7612 Loss_G: 1.8936 D(x): 0.6159 D(G(z)): 0.0771 / 0.0743\n", + "[798/1600][0/8] Loss_D: 0.7679 Loss_G: 1.9388 D(x): 0.6130 D(G(z)): 0.0733 / 0.0698\n", + "[798/1600][2/8] Loss_D: 0.7590 Loss_G: 1.9665 D(x): 0.5596 D(G(z)): 0.0665 / 0.0677\n", + "[798/1600][4/8] Loss_D: 0.7754 Loss_G: 1.8883 D(x): 0.5968 D(G(z)): 0.0787 / 0.0768\n", + "[798/1600][6/8] Loss_D: 0.7206 Loss_G: 1.9170 D(x): 0.5918 D(G(z)): 0.0706 / 0.0723\n", + "[799/1600][0/8] Loss_D: 0.7622 Loss_G: 1.9025 D(x): 0.5682 D(G(z)): 0.0714 / 0.0723\n", + "[799/1600][2/8] Loss_D: 0.7698 Loss_G: 1.9265 D(x): 0.6679 D(G(z)): 0.0736 / 0.0712\n", + "[799/1600][4/8] Loss_D: 0.7718 Loss_G: 1.8727 D(x): 0.5959 D(G(z)): 0.0800 / 0.0783\n", + "[799/1600][6/8] Loss_D: 0.7376 Loss_G: 1.8751 D(x): 0.5776 D(G(z)): 0.0741 / 0.0769\n", + "[800/1600][0/8] Loss_D: 0.7265 Loss_G: 1.9068 D(x): 0.6193 D(G(z)): 0.0729 / 0.0733\n", + "[800/1600][2/8] Loss_D: 0.7611 Loss_G: 1.9095 D(x): 0.5608 D(G(z)): 0.0731 / 0.0733\n", + "[800/1600][4/8] Loss_D: 0.7231 Loss_G: 1.8727 D(x): 0.5848 D(G(z)): 0.0768 / 0.0785\n", + "[800/1600][6/8] Loss_D: 0.6686 Loss_G: 1.9227 D(x): 0.6580 D(G(z)): 0.0698 / 0.0705\n", + "[801/1600][0/8] Loss_D: 0.7166 Loss_G: 1.8678 D(x): 0.6427 D(G(z)): 0.0770 / 0.0773\n", + "[801/1600][2/8] Loss_D: 0.6915 Loss_G: 1.8222 D(x): 0.6456 D(G(z)): 0.0812 / 0.0821\n", + "[801/1600][4/8] Loss_D: 0.7492 Loss_G: 1.8565 D(x): 0.6035 D(G(z)): 0.0806 / 0.0794\n", + "[801/1600][6/8] Loss_D: 0.7645 Loss_G: 1.8974 D(x): 0.5776 D(G(z)): 0.0765 / 0.0752\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[802/1600][0/8] Loss_D: 0.7158 Loss_G: 1.9287 D(x): 0.5933 D(G(z)): 0.0699 / 0.0707\n", + "[802/1600][2/8] Loss_D: 0.7675 Loss_G: 1.9579 D(x): 0.5870 D(G(z)): 0.0685 / 0.0678\n", + "[802/1600][4/8] Loss_D: 0.6857 Loss_G: 1.9439 D(x): 0.6141 D(G(z)): 0.0696 / 0.0702\n", + "[802/1600][6/8] Loss_D: 0.7563 Loss_G: 1.8859 D(x): 0.5999 D(G(z)): 0.0768 / 0.0772\n", + "[803/1600][0/8] Loss_D: 0.7607 Loss_G: 1.8907 D(x): 0.5738 D(G(z)): 0.0732 / 0.0738\n", + "[803/1600][2/8] Loss_D: 0.7035 Loss_G: 1.8550 D(x): 0.6036 D(G(z)): 0.0781 / 0.0807\n", + "[803/1600][4/8] Loss_D: 0.7296 Loss_G: 1.8446 D(x): 0.5781 D(G(z)): 0.0778 / 0.0801\n", + "[803/1600][6/8] Loss_D: 0.7441 Loss_G: 1.8072 D(x): 0.6743 D(G(z)): 0.0862 / 0.0838\n", + "[804/1600][0/8] Loss_D: 0.7813 Loss_G: 1.8834 D(x): 0.6458 D(G(z)): 0.0790 / 0.0758\n", + "[804/1600][2/8] Loss_D: 0.6907 Loss_G: 1.9192 D(x): 0.6161 D(G(z)): 0.0722 / 0.0726\n", + "[804/1600][4/8] Loss_D: 0.7069 Loss_G: 1.9070 D(x): 0.6064 D(G(z)): 0.0706 / 0.0724\n", + "[804/1600][6/8] Loss_D: 0.7550 Loss_G: 1.8590 D(x): 0.6394 D(G(z)): 0.0800 / 0.0788\n", + "[805/1600][0/8] Loss_D: 0.7446 Loss_G: 1.9070 D(x): 0.5907 D(G(z)): 0.0732 / 0.0739\n", + "[805/1600][2/8] Loss_D: 0.7275 Loss_G: 1.8514 D(x): 0.6354 D(G(z)): 0.0804 / 0.0799\n", + "[805/1600][4/8] Loss_D: 0.7438 Loss_G: 1.9094 D(x): 0.6169 D(G(z)): 0.0739 / 0.0739\n", + "[805/1600][6/8] Loss_D: 0.7170 Loss_G: 1.8242 D(x): 0.6444 D(G(z)): 0.0823 / 0.0832\n", + "[806/1600][0/8] Loss_D: 0.6886 Loss_G: 1.9048 D(x): 0.6622 D(G(z)): 0.0734 / 0.0733\n", + "[806/1600][2/8] Loss_D: 0.7778 Loss_G: 1.8548 D(x): 0.6152 D(G(z)): 0.0797 / 0.0773\n", + "[806/1600][4/8] Loss_D: 0.7056 Loss_G: 1.9427 D(x): 0.5927 D(G(z)): 0.0683 / 0.0702\n", + "[806/1600][6/8] Loss_D: 0.7778 Loss_G: 1.8439 D(x): 0.6632 D(G(z)): 0.0824 / 0.0797\n", + "[807/1600][0/8] Loss_D: 0.7345 Loss_G: 1.9305 D(x): 0.5961 D(G(z)): 0.0713 / 0.0703\n", + "[807/1600][2/8] Loss_D: 0.7060 Loss_G: 1.8324 D(x): 0.6037 D(G(z)): 0.0779 / 0.0813\n", + "[807/1600][4/8] Loss_D: 0.6931 Loss_G: 1.8274 D(x): 0.6289 D(G(z)): 0.0810 / 0.0816\n", + "[807/1600][6/8] Loss_D: 0.7012 Loss_G: 1.7903 D(x): 0.6433 D(G(z)): 0.0866 / 0.0866\n", + "[808/1600][0/8] Loss_D: 0.6874 Loss_G: 1.8488 D(x): 0.6204 D(G(z)): 0.0781 / 0.0803\n", + "[808/1600][2/8] Loss_D: 0.7503 Loss_G: 1.8328 D(x): 0.6597 D(G(z)): 0.0835 / 0.0824\n", + "[808/1600][4/8] Loss_D: 0.6987 Loss_G: 1.8551 D(x): 0.6289 D(G(z)): 0.0786 / 0.0782\n", + "[808/1600][6/8] Loss_D: 0.7384 Loss_G: 1.8554 D(x): 0.6580 D(G(z)): 0.0826 / 0.0797\n", + "[809/1600][0/8] Loss_D: 0.7053 Loss_G: 1.9413 D(x): 0.6386 D(G(z)): 0.0694 / 0.0693\n", + "[809/1600][2/8] Loss_D: 0.7220 Loss_G: 1.9475 D(x): 0.6184 D(G(z)): 0.0682 / 0.0683\n", + "[809/1600][4/8] Loss_D: 0.7366 Loss_G: 1.8942 D(x): 0.5966 D(G(z)): 0.0740 / 0.0750\n", + "[809/1600][6/8] Loss_D: 0.7550 Loss_G: 1.9280 D(x): 0.6276 D(G(z)): 0.0732 / 0.0708\n", + "[810/1600][0/8] Loss_D: 0.7517 Loss_G: 1.9945 D(x): 0.6027 D(G(z)): 0.0670 / 0.0650\n", + "[810/1600][2/8] Loss_D: 0.7026 Loss_G: 1.9726 D(x): 0.5920 D(G(z)): 0.0647 / 0.0671\n", + "[810/1600][4/8] Loss_D: 0.7094 Loss_G: 1.9722 D(x): 0.6342 D(G(z)): 0.0661 / 0.0670\n", + "[810/1600][6/8] Loss_D: 0.7083 Loss_G: 1.8750 D(x): 0.5940 D(G(z)): 0.0728 / 0.0762\n", + "[811/1600][0/8] Loss_D: 0.7833 Loss_G: 1.9757 D(x): 0.6309 D(G(z)): 0.0692 / 0.0665\n", + "[811/1600][2/8] Loss_D: 0.7692 Loss_G: 1.9503 D(x): 0.5972 D(G(z)): 0.0709 / 0.0695\n", + "[811/1600][4/8] Loss_D: 0.6977 Loss_G: 1.9286 D(x): 0.6493 D(G(z)): 0.0712 / 0.0701\n", + "[811/1600][6/8] Loss_D: 0.6745 Loss_G: 1.9333 D(x): 0.6425 D(G(z)): 0.0696 / 0.0703\n", + "[812/1600][0/8] Loss_D: 0.7753 Loss_G: 1.9491 D(x): 0.6152 D(G(z)): 0.0702 / 0.0696\n", + "[812/1600][2/8] Loss_D: 0.7672 Loss_G: 1.9687 D(x): 0.6286 D(G(z)): 0.0713 / 0.0685\n", + "[812/1600][4/8] Loss_D: 0.7370 Loss_G: 2.0228 D(x): 0.6065 D(G(z)): 0.0632 / 0.0615\n", + "[812/1600][6/8] Loss_D: 0.7389 Loss_G: 1.9461 D(x): 0.6027 D(G(z)): 0.0665 / 0.0677\n", + "[813/1600][0/8] Loss_D: 0.7759 Loss_G: 1.9644 D(x): 0.6500 D(G(z)): 0.0689 / 0.0680\n", + "[813/1600][2/8] Loss_D: 0.7464 Loss_G: 1.8855 D(x): 0.5783 D(G(z)): 0.0764 / 0.0777\n", + "[813/1600][4/8] Loss_D: 0.6923 Loss_G: 1.8809 D(x): 0.6078 D(G(z)): 0.0737 / 0.0766\n", + "[813/1600][6/8] Loss_D: 0.7849 Loss_G: 1.9014 D(x): 0.6296 D(G(z)): 0.0747 / 0.0733\n", + "[814/1600][0/8] Loss_D: 0.7837 Loss_G: 1.8394 D(x): 0.6809 D(G(z)): 0.0832 / 0.0804\n", + "[814/1600][2/8] Loss_D: 0.7040 Loss_G: 1.9117 D(x): 0.6353 D(G(z)): 0.0730 / 0.0727\n", + "[814/1600][4/8] Loss_D: 0.6953 Loss_G: 1.8533 D(x): 0.6314 D(G(z)): 0.0771 / 0.0800\n", + "[814/1600][6/8] Loss_D: 0.7218 Loss_G: 1.8328 D(x): 0.6520 D(G(z)): 0.0838 / 0.0834\n", + "[815/1600][0/8] Loss_D: 0.7327 Loss_G: 1.8164 D(x): 0.6629 D(G(z)): 0.0855 / 0.0845\n", + "[815/1600][2/8] Loss_D: 0.7639 Loss_G: 1.9316 D(x): 0.6318 D(G(z)): 0.0746 / 0.0710\n", + "[815/1600][4/8] Loss_D: 0.7340 Loss_G: 1.9356 D(x): 0.6254 D(G(z)): 0.0722 / 0.0700\n", + "[815/1600][6/8] Loss_D: 0.7112 Loss_G: 1.9559 D(x): 0.6418 D(G(z)): 0.0693 / 0.0680\n", + "[816/1600][0/8] Loss_D: 0.7450 Loss_G: 1.9294 D(x): 0.5865 D(G(z)): 0.0706 / 0.0717\n", + "[816/1600][2/8] Loss_D: 0.7372 Loss_G: 1.8921 D(x): 0.6186 D(G(z)): 0.0734 / 0.0744\n", + "[816/1600][4/8] Loss_D: 0.6580 Loss_G: 1.8872 D(x): 0.6521 D(G(z)): 0.0745 / 0.0753\n", + "[816/1600][6/8] Loss_D: 0.6997 Loss_G: 1.8276 D(x): 0.6044 D(G(z)): 0.0776 / 0.0817\n", + "[817/1600][0/8] Loss_D: 0.7564 Loss_G: 1.8372 D(x): 0.6506 D(G(z)): 0.0819 / 0.0792\n", + "[817/1600][2/8] Loss_D: 0.6827 Loss_G: 1.8788 D(x): 0.6635 D(G(z)): 0.0771 / 0.0773\n", + "[817/1600][4/8] Loss_D: 0.7340 Loss_G: 1.9604 D(x): 0.6765 D(G(z)): 0.0705 / 0.0678\n", + "[817/1600][6/8] Loss_D: 0.7039 Loss_G: 1.8855 D(x): 0.6081 D(G(z)): 0.0753 / 0.0773\n", + "[818/1600][0/8] Loss_D: 0.7659 Loss_G: 1.9321 D(x): 0.6850 D(G(z)): 0.0741 / 0.0710\n", + "[818/1600][2/8] Loss_D: 0.6873 Loss_G: 1.9021 D(x): 0.6275 D(G(z)): 0.0726 / 0.0740\n", + "[818/1600][4/8] Loss_D: 0.7474 Loss_G: 1.9240 D(x): 0.6420 D(G(z)): 0.0715 / 0.0705\n", + "[818/1600][6/8] Loss_D: 0.7664 Loss_G: 1.9021 D(x): 0.6141 D(G(z)): 0.0774 / 0.0751\n", + "[819/1600][0/8] Loss_D: 0.7251 Loss_G: 1.8828 D(x): 0.5947 D(G(z)): 0.0749 / 0.0757\n", + "[819/1600][2/8] Loss_D: 0.7662 Loss_G: 1.9478 D(x): 0.6506 D(G(z)): 0.0718 / 0.0688\n", + "[819/1600][4/8] Loss_D: 0.7700 Loss_G: 1.9280 D(x): 0.6055 D(G(z)): 0.0719 / 0.0708\n", + "[819/1600][6/8] Loss_D: 0.7537 Loss_G: 2.0248 D(x): 0.6232 D(G(z)): 0.0636 / 0.0618\n", + "[820/1600][0/8] Loss_D: 0.7095 Loss_G: 2.0266 D(x): 0.6054 D(G(z)): 0.0611 / 0.0618\n", + "[820/1600][2/8] Loss_D: 0.6849 Loss_G: 1.9834 D(x): 0.6022 D(G(z)): 0.0629 / 0.0660\n", + "[820/1600][4/8] Loss_D: 0.7781 Loss_G: 1.9704 D(x): 0.6506 D(G(z)): 0.0703 / 0.0676\n", + "[820/1600][6/8] Loss_D: 0.7570 Loss_G: 2.0084 D(x): 0.5647 D(G(z)): 0.0628 / 0.0640\n", + "[821/1600][0/8] Loss_D: 0.7152 Loss_G: 1.9050 D(x): 0.6315 D(G(z)): 0.0735 / 0.0753\n", + "[821/1600][2/8] Loss_D: 0.7839 Loss_G: 1.9234 D(x): 0.5690 D(G(z)): 0.0735 / 0.0715\n", + "[821/1600][4/8] Loss_D: 0.7073 Loss_G: 1.9894 D(x): 0.6159 D(G(z)): 0.0650 / 0.0652\n", + "[821/1600][6/8] Loss_D: 0.7159 Loss_G: 1.9399 D(x): 0.6302 D(G(z)): 0.0687 / 0.0696\n", + "[822/1600][0/8] Loss_D: 0.6747 Loss_G: 1.8337 D(x): 0.6429 D(G(z)): 0.0782 / 0.0811\n", + "[822/1600][2/8] Loss_D: 0.6865 Loss_G: 1.7771 D(x): 0.6296 D(G(z)): 0.0844 / 0.0886\n", + "[822/1600][4/8] Loss_D: 0.7814 Loss_G: 1.9087 D(x): 0.6657 D(G(z)): 0.0763 / 0.0727\n", + "[822/1600][6/8] Loss_D: 0.7080 Loss_G: 1.8344 D(x): 0.6380 D(G(z)): 0.0807 / 0.0803\n", + "[823/1600][0/8] Loss_D: 0.7956 Loss_G: 1.9048 D(x): 0.6533 D(G(z)): 0.0788 / 0.0736\n", + "[823/1600][2/8] Loss_D: 0.7563 Loss_G: 1.9601 D(x): 0.6337 D(G(z)): 0.0721 / 0.0682\n", + "[823/1600][4/8] Loss_D: 0.7272 Loss_G: 1.9318 D(x): 0.6119 D(G(z)): 0.0709 / 0.0713\n", + "[823/1600][6/8] Loss_D: 0.7671 Loss_G: 1.9022 D(x): 0.5546 D(G(z)): 0.0716 / 0.0734\n", + "[824/1600][0/8] Loss_D: 0.7301 Loss_G: 1.9254 D(x): 0.6171 D(G(z)): 0.0719 / 0.0723\n", + "[824/1600][2/8] Loss_D: 0.7458 Loss_G: 1.8976 D(x): 0.6269 D(G(z)): 0.0741 / 0.0736\n", + "[824/1600][4/8] Loss_D: 0.7108 Loss_G: 1.9273 D(x): 0.6090 D(G(z)): 0.0708 / 0.0715\n", + "[824/1600][6/8] Loss_D: 0.7761 Loss_G: 1.9318 D(x): 0.6116 D(G(z)): 0.0738 / 0.0713\n", + "[825/1600][0/8] Loss_D: 0.7239 Loss_G: 2.0180 D(x): 0.6121 D(G(z)): 0.0634 / 0.0622\n", + "[825/1600][2/8] Loss_D: 0.7276 Loss_G: 2.0267 D(x): 0.5585 D(G(z)): 0.0591 / 0.0619\n", + "[825/1600][4/8] Loss_D: 0.7762 Loss_G: 1.9649 D(x): 0.5549 D(G(z)): 0.0665 / 0.0679\n", + "[825/1600][6/8] Loss_D: 0.6974 Loss_G: 1.8771 D(x): 0.5974 D(G(z)): 0.0723 / 0.0761\n", + "[826/1600][0/8] Loss_D: 0.7947 Loss_G: 1.8698 D(x): 0.6454 D(G(z)): 0.0802 / 0.0772\n", + "[826/1600][2/8] Loss_D: 0.7209 Loss_G: 1.9201 D(x): 0.6449 D(G(z)): 0.0733 / 0.0717\n", + "[826/1600][4/8] Loss_D: 0.7690 Loss_G: 1.9291 D(x): 0.6303 D(G(z)): 0.0723 / 0.0709\n", + "[826/1600][6/8] Loss_D: 0.7620 Loss_G: 1.9368 D(x): 0.5951 D(G(z)): 0.0713 / 0.0705\n", + "[827/1600][0/8] Loss_D: 0.7610 Loss_G: 1.8917 D(x): 0.5852 D(G(z)): 0.0769 / 0.0756\n", + "[827/1600][2/8] Loss_D: 0.7403 Loss_G: 1.9495 D(x): 0.5890 D(G(z)): 0.0674 / 0.0687\n", + "[827/1600][4/8] Loss_D: 0.7293 Loss_G: 1.8983 D(x): 0.6650 D(G(z)): 0.0743 / 0.0741\n", + "[827/1600][6/8] Loss_D: 0.7715 Loss_G: 1.9163 D(x): 0.6431 D(G(z)): 0.0751 / 0.0719\n", + "[828/1600][0/8] Loss_D: 0.6926 Loss_G: 1.9721 D(x): 0.6173 D(G(z)): 0.0666 / 0.0666\n", + "[828/1600][2/8] Loss_D: 0.7631 Loss_G: 1.9469 D(x): 0.5833 D(G(z)): 0.0691 / 0.0695\n", + "[828/1600][4/8] Loss_D: 0.7640 Loss_G: 1.8911 D(x): 0.6485 D(G(z)): 0.0759 / 0.0749\n", + "[828/1600][6/8] Loss_D: 0.7124 Loss_G: 1.9270 D(x): 0.6006 D(G(z)): 0.0698 / 0.0705\n", + "[829/1600][0/8] Loss_D: 0.7459 Loss_G: 1.8487 D(x): 0.5827 D(G(z)): 0.0799 / 0.0810\n", + "[829/1600][2/8] Loss_D: 0.7627 Loss_G: 1.8428 D(x): 0.6000 D(G(z)): 0.0810 / 0.0812\n", + "[829/1600][4/8] Loss_D: 0.7802 Loss_G: 1.9856 D(x): 0.6703 D(G(z)): 0.0698 / 0.0662\n", + "[829/1600][6/8] Loss_D: 0.6897 Loss_G: 2.0418 D(x): 0.6190 D(G(z)): 0.0604 / 0.0601\n", + "[830/1600][0/8] Loss_D: 0.7136 Loss_G: 1.9545 D(x): 0.6243 D(G(z)): 0.0673 / 0.0682\n", + "[830/1600][2/8] Loss_D: 0.7223 Loss_G: 1.9731 D(x): 0.5653 D(G(z)): 0.0650 / 0.0677\n", + "[830/1600][4/8] Loss_D: 0.7675 Loss_G: 1.9608 D(x): 0.6200 D(G(z)): 0.0691 / 0.0672\n", + "[830/1600][6/8] Loss_D: 0.7254 Loss_G: 1.9196 D(x): 0.6050 D(G(z)): 0.0699 / 0.0711\n", + "[831/1600][0/8] Loss_D: 0.7163 Loss_G: 1.9417 D(x): 0.5930 D(G(z)): 0.0679 / 0.0702\n", + "[831/1600][2/8] Loss_D: 0.7360 Loss_G: 1.9086 D(x): 0.5946 D(G(z)): 0.0717 / 0.0729\n", + "[831/1600][4/8] Loss_D: 0.7589 Loss_G: 1.8770 D(x): 0.6250 D(G(z)): 0.0765 / 0.0762\n", + "[831/1600][6/8] Loss_D: 0.7146 Loss_G: 1.9156 D(x): 0.6687 D(G(z)): 0.0744 / 0.0733\n", + "[832/1600][0/8] Loss_D: 0.7000 Loss_G: 1.8491 D(x): 0.6077 D(G(z)): 0.0811 / 0.0824\n", + "[832/1600][2/8] Loss_D: 0.7327 Loss_G: 1.8908 D(x): 0.6298 D(G(z)): 0.0772 / 0.0758\n", + "[832/1600][4/8] Loss_D: 0.7028 Loss_G: 1.9751 D(x): 0.6451 D(G(z)): 0.0667 / 0.0665\n", + "[832/1600][6/8] Loss_D: 0.7017 Loss_G: 1.8606 D(x): 0.6089 D(G(z)): 0.0762 / 0.0792\n", + "[833/1600][0/8] Loss_D: 0.7709 Loss_G: 1.8786 D(x): 0.6179 D(G(z)): 0.0785 / 0.0770\n", + "[833/1600][2/8] Loss_D: 0.7712 Loss_G: 1.8876 D(x): 0.6370 D(G(z)): 0.0777 / 0.0749\n", + "[833/1600][4/8] Loss_D: 0.7103 Loss_G: 1.8975 D(x): 0.6263 D(G(z)): 0.0735 / 0.0746\n", + "[833/1600][6/8] Loss_D: 0.7055 Loss_G: 1.8709 D(x): 0.6576 D(G(z)): 0.0772 / 0.0768\n", + "[834/1600][0/8] Loss_D: 0.7648 Loss_G: 1.8159 D(x): 0.6482 D(G(z)): 0.0867 / 0.0840\n", + "[834/1600][2/8] Loss_D: 0.7405 Loss_G: 1.9742 D(x): 0.5875 D(G(z)): 0.0667 / 0.0661\n", + "[834/1600][4/8] Loss_D: 0.7043 Loss_G: 1.9344 D(x): 0.6014 D(G(z)): 0.0693 / 0.0709\n", + "[834/1600][6/8] Loss_D: 0.6958 Loss_G: 1.8878 D(x): 0.6250 D(G(z)): 0.0726 / 0.0743\n", + "[835/1600][0/8] Loss_D: 0.7177 Loss_G: 1.9194 D(x): 0.6384 D(G(z)): 0.0727 / 0.0722\n", + "[835/1600][2/8] Loss_D: 0.7384 Loss_G: 1.8992 D(x): 0.6682 D(G(z)): 0.0756 / 0.0739\n", + "[835/1600][4/8] Loss_D: 0.6800 Loss_G: 1.9885 D(x): 0.6202 D(G(z)): 0.0634 / 0.0656\n", + "[835/1600][6/8] Loss_D: 0.7879 Loss_G: 1.9181 D(x): 0.6149 D(G(z)): 0.0747 / 0.0719\n", + "[836/1600][0/8] Loss_D: 0.7528 Loss_G: 1.9439 D(x): 0.6477 D(G(z)): 0.0716 / 0.0698\n", + "[836/1600][2/8] Loss_D: 0.7544 Loss_G: 1.9139 D(x): 0.6325 D(G(z)): 0.0745 / 0.0723\n", + "[836/1600][4/8] Loss_D: 0.7315 Loss_G: 1.9641 D(x): 0.5635 D(G(z)): 0.0663 / 0.0681\n", + "[836/1600][6/8] Loss_D: 0.7941 Loss_G: 1.9061 D(x): 0.6598 D(G(z)): 0.0744 / 0.0729\n", + "[837/1600][0/8] Loss_D: 0.6640 Loss_G: 1.9203 D(x): 0.6548 D(G(z)): 0.0706 / 0.0715\n", + "[837/1600][2/8] Loss_D: 0.7903 Loss_G: 1.9055 D(x): 0.6593 D(G(z)): 0.0759 / 0.0736\n", + "[837/1600][4/8] Loss_D: 0.7392 Loss_G: 1.9320 D(x): 0.6179 D(G(z)): 0.0718 / 0.0717\n", + "[837/1600][6/8] Loss_D: 0.7164 Loss_G: 1.9092 D(x): 0.6075 D(G(z)): 0.0723 / 0.0732\n", + "[838/1600][0/8] Loss_D: 0.7748 Loss_G: 1.8708 D(x): 0.5725 D(G(z)): 0.0782 / 0.0772\n", + "[838/1600][2/8] Loss_D: 0.7341 Loss_G: 1.9432 D(x): 0.5958 D(G(z)): 0.0714 / 0.0714\n", + "[838/1600][4/8] Loss_D: 0.6808 Loss_G: 1.9738 D(x): 0.6092 D(G(z)): 0.0661 / 0.0678\n", + "[838/1600][6/8] Loss_D: 0.7557 Loss_G: 1.9215 D(x): 0.6452 D(G(z)): 0.0732 / 0.0710\n", + "[839/1600][0/8] Loss_D: 0.7584 Loss_G: 1.9511 D(x): 0.6160 D(G(z)): 0.0704 / 0.0688\n", + "[839/1600][2/8] Loss_D: 0.7693 Loss_G: 2.0126 D(x): 0.6246 D(G(z)): 0.0671 / 0.0637\n", + "[839/1600][4/8] Loss_D: 0.7646 Loss_G: 2.0047 D(x): 0.5684 D(G(z)): 0.0635 / 0.0646\n", + "[839/1600][6/8] Loss_D: 0.6907 Loss_G: 1.9153 D(x): 0.6105 D(G(z)): 0.0693 / 0.0734\n", + "[840/1600][0/8] Loss_D: 0.6940 Loss_G: 1.8684 D(x): 0.6401 D(G(z)): 0.0782 / 0.0809\n", + "[840/1600][2/8] Loss_D: 0.7586 Loss_G: 1.8993 D(x): 0.6444 D(G(z)): 0.0785 / 0.0758\n", + "[840/1600][4/8] Loss_D: 0.7696 Loss_G: 1.9177 D(x): 0.5925 D(G(z)): 0.0748 / 0.0726\n", + "[840/1600][6/8] Loss_D: 0.7642 Loss_G: 1.8991 D(x): 0.5705 D(G(z)): 0.0756 / 0.0749\n", + "[841/1600][0/8] Loss_D: 0.7474 Loss_G: 1.9654 D(x): 0.6075 D(G(z)): 0.0678 / 0.0687\n", + "[841/1600][2/8] Loss_D: 0.7667 Loss_G: 1.9011 D(x): 0.6178 D(G(z)): 0.0735 / 0.0732\n", + "[841/1600][4/8] Loss_D: 0.6937 Loss_G: 1.9121 D(x): 0.6225 D(G(z)): 0.0717 / 0.0730\n", + "[841/1600][6/8] Loss_D: 0.6572 Loss_G: 1.8587 D(x): 0.6801 D(G(z)): 0.0778 / 0.0789\n", + "[842/1600][0/8] Loss_D: 0.7933 Loss_G: 1.8725 D(x): 0.6380 D(G(z)): 0.0797 / 0.0765\n", + "[842/1600][2/8] Loss_D: 0.6774 Loss_G: 1.8840 D(x): 0.6246 D(G(z)): 0.0745 / 0.0767\n", + "[842/1600][4/8] Loss_D: 0.7063 Loss_G: 1.8296 D(x): 0.6545 D(G(z)): 0.0817 / 0.0809\n", + "[842/1600][6/8] Loss_D: 0.7671 Loss_G: 1.9261 D(x): 0.6160 D(G(z)): 0.0745 / 0.0727\n", + "[843/1600][0/8] Loss_D: 0.7543 Loss_G: 1.9541 D(x): 0.6198 D(G(z)): 0.0715 / 0.0688\n", + "[843/1600][2/8] Loss_D: 0.7651 Loss_G: 1.9852 D(x): 0.6133 D(G(z)): 0.0686 / 0.0662\n", + "[843/1600][4/8] Loss_D: 0.6968 Loss_G: 1.9867 D(x): 0.6384 D(G(z)): 0.0668 / 0.0666\n", + "[843/1600][6/8] Loss_D: 0.7179 Loss_G: 1.9641 D(x): 0.5897 D(G(z)): 0.0651 / 0.0675\n", + "[844/1600][0/8] Loss_D: 0.7592 Loss_G: 1.8996 D(x): 0.6594 D(G(z)): 0.0752 / 0.0736\n", + "[844/1600][2/8] Loss_D: 0.7659 Loss_G: 1.9005 D(x): 0.5918 D(G(z)): 0.0733 / 0.0736\n", + "[844/1600][4/8] Loss_D: 0.6914 Loss_G: 1.9393 D(x): 0.6004 D(G(z)): 0.0683 / 0.0708\n", + "[844/1600][6/8] Loss_D: 0.7634 Loss_G: 1.8783 D(x): 0.6280 D(G(z)): 0.0790 / 0.0776\n", + "[845/1600][0/8] Loss_D: 0.7872 Loss_G: 1.8632 D(x): 0.6150 D(G(z)): 0.0827 / 0.0788\n", + "[845/1600][2/8] Loss_D: 0.7605 Loss_G: 1.9663 D(x): 0.5705 D(G(z)): 0.0672 / 0.0671\n", + "[845/1600][4/8] Loss_D: 0.6824 Loss_G: 1.9092 D(x): 0.6556 D(G(z)): 0.0719 / 0.0719\n", + "[845/1600][6/8] Loss_D: 0.7325 Loss_G: 1.9902 D(x): 0.6056 D(G(z)): 0.0664 / 0.0659\n", + "[846/1600][0/8] Loss_D: 0.7336 Loss_G: 2.0002 D(x): 0.6281 D(G(z)): 0.0663 / 0.0649\n", + "[846/1600][2/8] Loss_D: 0.7220 Loss_G: 2.0095 D(x): 0.6031 D(G(z)): 0.0642 / 0.0639\n", + "[846/1600][4/8] Loss_D: 0.7229 Loss_G: 1.9988 D(x): 0.5946 D(G(z)): 0.0626 / 0.0650\n", + "[846/1600][6/8] Loss_D: 0.7358 Loss_G: 1.9239 D(x): 0.6335 D(G(z)): 0.0705 / 0.0713\n", + "[847/1600][0/8] Loss_D: 0.7348 Loss_G: 1.8833 D(x): 0.6203 D(G(z)): 0.0757 / 0.0761\n", + "[847/1600][2/8] Loss_D: 0.7409 Loss_G: 1.8511 D(x): 0.6350 D(G(z)): 0.0800 / 0.0797\n", + "[847/1600][4/8] Loss_D: 0.7702 Loss_G: 1.8833 D(x): 0.6233 D(G(z)): 0.0810 / 0.0773\n", + "[847/1600][6/8] Loss_D: 0.6853 Loss_G: 1.9702 D(x): 0.6145 D(G(z)): 0.0658 / 0.0669\n", + "[848/1600][0/8] Loss_D: 0.6880 Loss_G: 1.9363 D(x): 0.6324 D(G(z)): 0.0680 / 0.0696\n", + "[848/1600][2/8] Loss_D: 0.7711 Loss_G: 1.9039 D(x): 0.6153 D(G(z)): 0.0745 / 0.0739\n", + "[848/1600][4/8] Loss_D: 0.7806 Loss_G: 1.9165 D(x): 0.5985 D(G(z)): 0.0749 / 0.0719\n", + "[848/1600][6/8] Loss_D: 0.7454 Loss_G: 1.9529 D(x): 0.6010 D(G(z)): 0.0697 / 0.0695\n", + "[849/1600][0/8] Loss_D: 0.6858 Loss_G: 1.9629 D(x): 0.6078 D(G(z)): 0.0664 / 0.0679\n", + "[849/1600][2/8] Loss_D: 0.6772 Loss_G: 1.8637 D(x): 0.6337 D(G(z)): 0.0766 / 0.0776\n", + "[849/1600][4/8] Loss_D: 0.7543 Loss_G: 1.9428 D(x): 0.6167 D(G(z)): 0.0700 / 0.0692\n", + "[849/1600][6/8] Loss_D: 0.7743 Loss_G: 1.8858 D(x): 0.6264 D(G(z)): 0.0770 / 0.0756\n", + "[850/1600][0/8] Loss_D: 0.7558 Loss_G: 1.9092 D(x): 0.6205 D(G(z)): 0.0744 / 0.0725\n", + "[850/1600][2/8] Loss_D: 0.7929 Loss_G: 1.9175 D(x): 0.6416 D(G(z)): 0.0749 / 0.0725\n", + "[850/1600][4/8] Loss_D: 0.6945 Loss_G: 1.9696 D(x): 0.5963 D(G(z)): 0.0673 / 0.0698\n", + "[850/1600][6/8] Loss_D: 0.7407 Loss_G: 1.8670 D(x): 0.5931 D(G(z)): 0.0769 / 0.0783\n", + "[851/1600][0/8] Loss_D: 0.7590 Loss_G: 1.9804 D(x): 0.6566 D(G(z)): 0.0696 / 0.0666\n", + "[851/1600][2/8] Loss_D: 0.7863 Loss_G: 1.9707 D(x): 0.5464 D(G(z)): 0.0676 / 0.0667\n", + "[851/1600][4/8] Loss_D: 0.7577 Loss_G: 1.9618 D(x): 0.6116 D(G(z)): 0.0686 / 0.0672\n", + "[851/1600][6/8] Loss_D: 0.7526 Loss_G: 1.9118 D(x): 0.5966 D(G(z)): 0.0738 / 0.0733\n", + "[852/1600][0/8] Loss_D: 0.7683 Loss_G: 1.9531 D(x): 0.6064 D(G(z)): 0.0698 / 0.0682\n", + "[852/1600][2/8] Loss_D: 0.7366 Loss_G: 1.9752 D(x): 0.5960 D(G(z)): 0.0673 / 0.0672\n", + "[852/1600][4/8] Loss_D: 0.7445 Loss_G: 1.9007 D(x): 0.6036 D(G(z)): 0.0734 / 0.0746\n", + "[852/1600][6/8] Loss_D: 0.6679 Loss_G: 1.9617 D(x): 0.6375 D(G(z)): 0.0662 / 0.0682\n", + "[853/1600][0/8] Loss_D: 0.7788 Loss_G: 1.8924 D(x): 0.5919 D(G(z)): 0.0769 / 0.0754\n", + "[853/1600][2/8] Loss_D: 0.7125 Loss_G: 1.8743 D(x): 0.6148 D(G(z)): 0.0750 / 0.0768\n", + "[853/1600][4/8] Loss_D: 0.7360 Loss_G: 1.8577 D(x): 0.6081 D(G(z)): 0.0794 / 0.0803\n", + "[853/1600][6/8] Loss_D: 0.7270 Loss_G: 1.8563 D(x): 0.6221 D(G(z)): 0.0778 / 0.0785\n", + "[854/1600][0/8] Loss_D: 0.7255 Loss_G: 1.8807 D(x): 0.6881 D(G(z)): 0.0795 / 0.0776\n", + "[854/1600][2/8] Loss_D: 0.7920 Loss_G: 1.9014 D(x): 0.6250 D(G(z)): 0.0778 / 0.0739\n", + "[854/1600][4/8] Loss_D: 0.7482 Loss_G: 2.0017 D(x): 0.5960 D(G(z)): 0.0646 / 0.0640\n", + "[854/1600][6/8] Loss_D: 0.6940 Loss_G: 1.9465 D(x): 0.6243 D(G(z)): 0.0683 / 0.0693\n", + "[855/1600][0/8] Loss_D: 0.7368 Loss_G: 1.9075 D(x): 0.6200 D(G(z)): 0.0731 / 0.0738\n", + "[855/1600][2/8] Loss_D: 0.7238 Loss_G: 1.9322 D(x): 0.5714 D(G(z)): 0.0679 / 0.0709\n", + "[855/1600][4/8] Loss_D: 0.7788 Loss_G: 1.8832 D(x): 0.6401 D(G(z)): 0.0800 / 0.0766\n", + "[855/1600][6/8] Loss_D: 0.7392 Loss_G: 1.9163 D(x): 0.6459 D(G(z)): 0.0748 / 0.0737\n", + "[856/1600][0/8] Loss_D: 0.6805 Loss_G: 1.8567 D(x): 0.6255 D(G(z)): 0.0778 / 0.0797\n", + "[856/1600][2/8] Loss_D: 0.7050 Loss_G: 1.8599 D(x): 0.6132 D(G(z)): 0.0757 / 0.0784\n", + "[856/1600][4/8] Loss_D: 0.7625 Loss_G: 1.8095 D(x): 0.6747 D(G(z)): 0.0900 / 0.0863\n", + "[856/1600][6/8] Loss_D: 0.7119 Loss_G: 1.9036 D(x): 0.6163 D(G(z)): 0.0736 / 0.0740\n", + "[857/1600][0/8] Loss_D: 0.6849 Loss_G: 1.8858 D(x): 0.6181 D(G(z)): 0.0739 / 0.0764\n", + "[857/1600][2/8] Loss_D: 0.6855 Loss_G: 1.8566 D(x): 0.6288 D(G(z)): 0.0783 / 0.0792\n", + "[857/1600][4/8] Loss_D: 0.7364 Loss_G: 1.8560 D(x): 0.6449 D(G(z)): 0.0812 / 0.0802\n", + "[857/1600][6/8] Loss_D: 0.7298 Loss_G: 1.8306 D(x): 0.5742 D(G(z)): 0.0796 / 0.0827\n", + "[858/1600][0/8] Loss_D: 0.7077 Loss_G: 1.8266 D(x): 0.6381 D(G(z)): 0.0844 / 0.0843\n", + "[858/1600][2/8] Loss_D: 0.7258 Loss_G: 1.8505 D(x): 0.6404 D(G(z)): 0.0822 / 0.0799\n", + "[858/1600][4/8] Loss_D: 0.7394 Loss_G: 1.9099 D(x): 0.6165 D(G(z)): 0.0747 / 0.0733\n", + "[858/1600][6/8] Loss_D: 0.6624 Loss_G: 1.9612 D(x): 0.6472 D(G(z)): 0.0672 / 0.0679\n", + "[859/1600][0/8] Loss_D: 0.7710 Loss_G: 1.9029 D(x): 0.6502 D(G(z)): 0.0747 / 0.0737\n", + "[859/1600][2/8] Loss_D: 0.6699 Loss_G: 1.9624 D(x): 0.6286 D(G(z)): 0.0655 / 0.0670\n", + "[859/1600][4/8] Loss_D: 0.6797 Loss_G: 1.8937 D(x): 0.6383 D(G(z)): 0.0739 / 0.0766\n", + "[859/1600][6/8] Loss_D: 0.7642 Loss_G: 1.8709 D(x): 0.6932 D(G(z)): 0.0811 / 0.0770\n", + "[860/1600][0/8] Loss_D: 0.7724 Loss_G: 1.9982 D(x): 0.6609 D(G(z)): 0.0692 / 0.0655\n", + "[860/1600][2/8] Loss_D: 0.6931 Loss_G: 1.9925 D(x): 0.6321 D(G(z)): 0.0648 / 0.0659\n", + "[860/1600][4/8] Loss_D: 0.7594 Loss_G: 1.9306 D(x): 0.6167 D(G(z)): 0.0739 / 0.0721\n", + "[860/1600][6/8] Loss_D: 0.7518 Loss_G: 1.9999 D(x): 0.6147 D(G(z)): 0.0681 / 0.0666\n", + "[861/1600][0/8] Loss_D: 0.7823 Loss_G: 2.0036 D(x): 0.6535 D(G(z)): 0.0671 / 0.0633\n", + "[861/1600][2/8] Loss_D: 0.7133 Loss_G: 2.0226 D(x): 0.5820 D(G(z)): 0.0610 / 0.0624\n", + "[861/1600][4/8] Loss_D: 0.7180 Loss_G: 2.0605 D(x): 0.5772 D(G(z)): 0.0587 / 0.0607\n", + "[861/1600][6/8] Loss_D: 0.7023 Loss_G: 2.0146 D(x): 0.6037 D(G(z)): 0.0610 / 0.0641\n", + "[862/1600][0/8] Loss_D: 0.7315 Loss_G: 1.9637 D(x): 0.6328 D(G(z)): 0.0674 / 0.0682\n", + "[862/1600][2/8] Loss_D: 0.6650 Loss_G: 1.7960 D(x): 0.6509 D(G(z)): 0.0852 / 0.0872\n", + "[862/1600][4/8] Loss_D: 0.7763 Loss_G: 1.8889 D(x): 0.6272 D(G(z)): 0.0797 / 0.0776\n", + "[862/1600][6/8] Loss_D: 0.7115 Loss_G: 1.8944 D(x): 0.6135 D(G(z)): 0.0733 / 0.0749\n", + "[863/1600][0/8] Loss_D: 0.7243 Loss_G: 1.8712 D(x): 0.6291 D(G(z)): 0.0805 / 0.0797\n", + "[863/1600][2/8] Loss_D: 0.6605 Loss_G: 1.8582 D(x): 0.6583 D(G(z)): 0.0776 / 0.0798\n", + "[863/1600][4/8] Loss_D: 0.6763 Loss_G: 1.7976 D(x): 0.6747 D(G(z)): 0.0855 / 0.0866\n", + "[863/1600][6/8] Loss_D: 0.7004 Loss_G: 1.7630 D(x): 0.6662 D(G(z)): 0.0908 / 0.0910\n", + "[864/1600][0/8] Loss_D: 0.7612 Loss_G: 1.8732 D(x): 0.6753 D(G(z)): 0.0815 / 0.0773\n", + "[864/1600][2/8] Loss_D: 0.7625 Loss_G: 1.8750 D(x): 0.6176 D(G(z)): 0.0793 / 0.0772\n", + "[864/1600][4/8] Loss_D: 0.7536 Loss_G: 1.9269 D(x): 0.6293 D(G(z)): 0.0728 / 0.0706\n", + "[864/1600][6/8] Loss_D: 0.7736 Loss_G: 1.9526 D(x): 0.6431 D(G(z)): 0.0734 / 0.0705\n", + "[865/1600][0/8] Loss_D: 0.7646 Loss_G: 1.8670 D(x): 0.5514 D(G(z)): 0.0758 / 0.0790\n", + "[865/1600][2/8] Loss_D: 0.7866 Loss_G: 1.8839 D(x): 0.6424 D(G(z)): 0.0792 / 0.0764\n", + "[865/1600][4/8] Loss_D: 0.6934 Loss_G: 1.9642 D(x): 0.6598 D(G(z)): 0.0687 / 0.0675\n", + "[865/1600][6/8] Loss_D: 0.6851 Loss_G: 1.9428 D(x): 0.6142 D(G(z)): 0.0690 / 0.0704\n", + "[866/1600][0/8] Loss_D: 0.7677 Loss_G: 1.9606 D(x): 0.6500 D(G(z)): 0.0704 / 0.0680\n", + "[866/1600][2/8] Loss_D: 0.7251 Loss_G: 2.0014 D(x): 0.6398 D(G(z)): 0.0652 / 0.0636\n", + "[866/1600][4/8] Loss_D: 0.7374 Loss_G: 1.9395 D(x): 0.5893 D(G(z)): 0.0692 / 0.0706\n", + "[866/1600][6/8] Loss_D: 0.6886 Loss_G: 1.9901 D(x): 0.5995 D(G(z)): 0.0633 / 0.0657\n", + "[867/1600][0/8] Loss_D: 0.7786 Loss_G: 1.9276 D(x): 0.6384 D(G(z)): 0.0734 / 0.0714\n", + "[867/1600][2/8] Loss_D: 0.7513 Loss_G: 1.9213 D(x): 0.6039 D(G(z)): 0.0731 / 0.0724\n", + "[867/1600][4/8] Loss_D: 0.6967 Loss_G: 1.9697 D(x): 0.6260 D(G(z)): 0.0682 / 0.0684\n", + "[867/1600][6/8] Loss_D: 0.7171 Loss_G: 1.9336 D(x): 0.6265 D(G(z)): 0.0703 / 0.0715\n", + "[868/1600][0/8] Loss_D: 0.7257 Loss_G: 1.8313 D(x): 0.6364 D(G(z)): 0.0822 / 0.0836\n", + "[868/1600][2/8] Loss_D: 0.7232 Loss_G: 1.9428 D(x): 0.6259 D(G(z)): 0.0700 / 0.0696\n", + "[868/1600][4/8] Loss_D: 0.7243 Loss_G: 1.9004 D(x): 0.6491 D(G(z)): 0.0744 / 0.0739\n", + "[868/1600][6/8] Loss_D: 0.7636 Loss_G: 1.8842 D(x): 0.6110 D(G(z)): 0.0801 / 0.0789\n", + "[869/1600][0/8] Loss_D: 0.7596 Loss_G: 1.9557 D(x): 0.6248 D(G(z)): 0.0715 / 0.0695\n", + "[869/1600][2/8] Loss_D: 0.7558 Loss_G: 1.9195 D(x): 0.6326 D(G(z)): 0.0733 / 0.0721\n", + "[869/1600][4/8] Loss_D: 0.7215 Loss_G: 1.9286 D(x): 0.6280 D(G(z)): 0.0727 / 0.0725\n", + "[869/1600][6/8] Loss_D: 0.7582 Loss_G: 1.9016 D(x): 0.6487 D(G(z)): 0.0760 / 0.0750\n", + "[870/1600][0/8] Loss_D: 0.7130 Loss_G: 1.9561 D(x): 0.6536 D(G(z)): 0.0703 / 0.0695\n", + "[870/1600][2/8] Loss_D: 0.6950 Loss_G: 1.8490 D(x): 0.6296 D(G(z)): 0.0817 / 0.0836\n", + "[870/1600][4/8] Loss_D: 0.7099 Loss_G: 1.9365 D(x): 0.6671 D(G(z)): 0.0709 / 0.0708\n", + "[870/1600][6/8] Loss_D: 0.6975 Loss_G: 1.8422 D(x): 0.6335 D(G(z)): 0.0820 / 0.0823\n", + "[871/1600][0/8] Loss_D: 0.7659 Loss_G: 1.8840 D(x): 0.6264 D(G(z)): 0.0773 / 0.0759\n", + "[871/1600][2/8] Loss_D: 0.7211 Loss_G: 1.8946 D(x): 0.6482 D(G(z)): 0.0784 / 0.0763\n", + "[871/1600][4/8] Loss_D: 0.6748 Loss_G: 1.9747 D(x): 0.6472 D(G(z)): 0.0693 / 0.0682\n", + "[871/1600][6/8] Loss_D: 0.7738 Loss_G: 2.0053 D(x): 0.6296 D(G(z)): 0.0676 / 0.0646\n", + "[872/1600][0/8] Loss_D: 0.7283 Loss_G: 1.9593 D(x): 0.5860 D(G(z)): 0.0665 / 0.0682\n", + "[872/1600][2/8] Loss_D: 0.7618 Loss_G: 1.9409 D(x): 0.6402 D(G(z)): 0.0736 / 0.0720\n", + "[872/1600][4/8] Loss_D: 0.7616 Loss_G: 1.9594 D(x): 0.5933 D(G(z)): 0.0703 / 0.0680\n", + "[872/1600][6/8] Loss_D: 0.7217 Loss_G: 2.0363 D(x): 0.6145 D(G(z)): 0.0637 / 0.0633\n", + "[873/1600][0/8] Loss_D: 0.6703 Loss_G: 1.9829 D(x): 0.6243 D(G(z)): 0.0634 / 0.0674\n", + "[873/1600][2/8] Loss_D: 0.7820 Loss_G: 1.9092 D(x): 0.6074 D(G(z)): 0.0739 / 0.0746\n", + "[873/1600][4/8] Loss_D: 0.7566 Loss_G: 1.9088 D(x): 0.6561 D(G(z)): 0.0759 / 0.0741\n", + "[873/1600][6/8] Loss_D: 0.6791 Loss_G: 1.8932 D(x): 0.6758 D(G(z)): 0.0745 / 0.0745\n", + "[874/1600][0/8] Loss_D: 0.6798 Loss_G: 1.8739 D(x): 0.6913 D(G(z)): 0.0785 / 0.0779\n", + "[874/1600][2/8] Loss_D: 0.6819 Loss_G: 1.9283 D(x): 0.6697 D(G(z)): 0.0735 / 0.0726\n", + "[874/1600][4/8] Loss_D: 0.7282 Loss_G: 1.8753 D(x): 0.6213 D(G(z)): 0.0784 / 0.0787\n", + "[874/1600][6/8] Loss_D: 0.7715 Loss_G: 1.9546 D(x): 0.6549 D(G(z)): 0.0742 / 0.0701\n", + "[875/1600][0/8] Loss_D: 0.6775 Loss_G: 1.9912 D(x): 0.6147 D(G(z)): 0.0631 / 0.0644\n", + "[875/1600][2/8] Loss_D: 0.7629 Loss_G: 1.9699 D(x): 0.5995 D(G(z)): 0.0690 / 0.0673\n", + "[875/1600][4/8] Loss_D: 0.7584 Loss_G: 2.0697 D(x): 0.5929 D(G(z)): 0.0611 / 0.0592\n", + "[875/1600][6/8] Loss_D: 0.6770 Loss_G: 1.9263 D(x): 0.6323 D(G(z)): 0.0698 / 0.0724\n", + "[876/1600][0/8] Loss_D: 0.7672 Loss_G: 2.0399 D(x): 0.5998 D(G(z)): 0.0617 / 0.0606\n", + "[876/1600][2/8] Loss_D: 0.7140 Loss_G: 1.9470 D(x): 0.5812 D(G(z)): 0.0665 / 0.0694\n", + "[876/1600][4/8] Loss_D: 0.7264 Loss_G: 1.9755 D(x): 0.6256 D(G(z)): 0.0671 / 0.0674\n", + "[876/1600][6/8] Loss_D: 0.7549 Loss_G: 1.9303 D(x): 0.6455 D(G(z)): 0.0739 / 0.0725\n", + "[877/1600][0/8] Loss_D: 0.7066 Loss_G: 1.9785 D(x): 0.5817 D(G(z)): 0.0657 / 0.0677\n", + "[877/1600][2/8] Loss_D: 0.6853 Loss_G: 1.9044 D(x): 0.6495 D(G(z)): 0.0716 / 0.0745\n", + "[877/1600][4/8] Loss_D: 0.7126 Loss_G: 1.9057 D(x): 0.6466 D(G(z)): 0.0748 / 0.0742\n", + "[877/1600][6/8] Loss_D: 0.7976 Loss_G: 1.8731 D(x): 0.6566 D(G(z)): 0.0824 / 0.0785\n", + "[878/1600][0/8] Loss_D: 0.6557 Loss_G: 2.0107 D(x): 0.6707 D(G(z)): 0.0656 / 0.0648\n", + "[878/1600][2/8] Loss_D: 0.7448 Loss_G: 1.9675 D(x): 0.6386 D(G(z)): 0.0685 / 0.0674\n", + "[878/1600][4/8] Loss_D: 0.7847 Loss_G: 1.9292 D(x): 0.6102 D(G(z)): 0.0741 / 0.0711\n", + "[878/1600][6/8] Loss_D: 0.7704 Loss_G: 1.9679 D(x): 0.5671 D(G(z)): 0.0687 / 0.0686\n", + "[879/1600][0/8] Loss_D: 0.6936 Loss_G: 1.9271 D(x): 0.6085 D(G(z)): 0.0701 / 0.0719\n", + "[879/1600][2/8] Loss_D: 0.7049 Loss_G: 1.8819 D(x): 0.6550 D(G(z)): 0.0758 / 0.0765\n", + "[879/1600][4/8] Loss_D: 0.7346 Loss_G: 1.9480 D(x): 0.6153 D(G(z)): 0.0691 / 0.0695\n", + "[879/1600][6/8] Loss_D: 0.6896 Loss_G: 1.9366 D(x): 0.6363 D(G(z)): 0.0712 / 0.0723\n", + "[880/1600][0/8] Loss_D: 0.7005 Loss_G: 1.8448 D(x): 0.6435 D(G(z)): 0.0803 / 0.0813\n", + "[880/1600][2/8] Loss_D: 0.6893 Loss_G: 1.8216 D(x): 0.6680 D(G(z)): 0.0808 / 0.0822\n", + "[880/1600][4/8] Loss_D: 0.7479 Loss_G: 1.8586 D(x): 0.6665 D(G(z)): 0.0828 / 0.0797\n", + "[880/1600][6/8] Loss_D: 0.7205 Loss_G: 1.9782 D(x): 0.6108 D(G(z)): 0.0668 / 0.0657\n", + "[881/1600][0/8] Loss_D: 0.7655 Loss_G: 1.9462 D(x): 0.6316 D(G(z)): 0.0727 / 0.0702\n", + "[881/1600][2/8] Loss_D: 0.7595 Loss_G: 2.0062 D(x): 0.5943 D(G(z)): 0.0653 / 0.0650\n", + "[881/1600][4/8] Loss_D: 0.7932 Loss_G: 1.9191 D(x): 0.6569 D(G(z)): 0.0766 / 0.0735\n", + "[881/1600][6/8] Loss_D: 0.7234 Loss_G: 1.9952 D(x): 0.6576 D(G(z)): 0.0645 / 0.0646\n", + "[882/1600][0/8] Loss_D: 0.7482 Loss_G: 1.8732 D(x): 0.5874 D(G(z)): 0.0778 / 0.0791\n", + "[882/1600][2/8] Loss_D: 0.7048 Loss_G: 1.8402 D(x): 0.6464 D(G(z)): 0.0817 / 0.0823\n", + "[882/1600][4/8] Loss_D: 0.6870 Loss_G: 1.8755 D(x): 0.6737 D(G(z)): 0.0783 / 0.0776\n", + "[882/1600][6/8] Loss_D: 0.6963 Loss_G: 1.8790 D(x): 0.6331 D(G(z)): 0.0767 / 0.0782\n", + "[883/1600][0/8] Loss_D: 0.7740 Loss_G: 1.8670 D(x): 0.6384 D(G(z)): 0.0802 / 0.0784\n", + "[883/1600][2/8] Loss_D: 0.7536 Loss_G: 1.9311 D(x): 0.6178 D(G(z)): 0.0739 / 0.0716\n", + "[883/1600][4/8] Loss_D: 0.6978 Loss_G: 1.8782 D(x): 0.6608 D(G(z)): 0.0758 / 0.0763\n", + "[883/1600][6/8] Loss_D: 0.7036 Loss_G: 1.9366 D(x): 0.6514 D(G(z)): 0.0711 / 0.0715\n", + "[884/1600][0/8] Loss_D: 0.7244 Loss_G: 1.8907 D(x): 0.6134 D(G(z)): 0.0749 / 0.0756\n", + "[884/1600][2/8] Loss_D: 0.7514 Loss_G: 1.8468 D(x): 0.6084 D(G(z)): 0.0827 / 0.0814\n", + "[884/1600][4/8] Loss_D: 0.7468 Loss_G: 1.9374 D(x): 0.6728 D(G(z)): 0.0731 / 0.0702\n", + "[884/1600][6/8] Loss_D: 0.7609 Loss_G: 1.9383 D(x): 0.5824 D(G(z)): 0.0703 / 0.0701\n", + "[885/1600][0/8] Loss_D: 0.7160 Loss_G: 1.9515 D(x): 0.6505 D(G(z)): 0.0701 / 0.0698\n", + "[885/1600][2/8] Loss_D: 0.7678 Loss_G: 1.9370 D(x): 0.6043 D(G(z)): 0.0731 / 0.0722\n", + "[885/1600][4/8] Loss_D: 0.7333 Loss_G: 1.9207 D(x): 0.6452 D(G(z)): 0.0740 / 0.0733\n", + "[885/1600][6/8] Loss_D: 0.6789 Loss_G: 1.9806 D(x): 0.6372 D(G(z)): 0.0655 / 0.0671\n", + "[886/1600][0/8] Loss_D: 0.7739 Loss_G: 1.9616 D(x): 0.6833 D(G(z)): 0.0716 / 0.0696\n", + "[886/1600][2/8] Loss_D: 0.7509 Loss_G: 1.9137 D(x): 0.5779 D(G(z)): 0.0734 / 0.0742\n", + "[886/1600][4/8] Loss_D: 0.7773 Loss_G: 1.9343 D(x): 0.6577 D(G(z)): 0.0749 / 0.0709\n", + "[886/1600][6/8] Loss_D: 0.6981 Loss_G: 1.9602 D(x): 0.6405 D(G(z)): 0.0692 / 0.0692\n", + "[887/1600][0/8] Loss_D: 0.7473 Loss_G: 1.9047 D(x): 0.6603 D(G(z)): 0.0741 / 0.0732\n", + "[887/1600][2/8] Loss_D: 0.7750 Loss_G: 2.0645 D(x): 0.6080 D(G(z)): 0.0644 / 0.0617\n", + "[887/1600][4/8] Loss_D: 0.7196 Loss_G: 1.9935 D(x): 0.6171 D(G(z)): 0.0638 / 0.0643\n", + "[887/1600][6/8] Loss_D: 0.7827 Loss_G: 1.9701 D(x): 0.5257 D(G(z)): 0.0657 / 0.0671\n", + "[888/1600][0/8] Loss_D: 0.7498 Loss_G: 1.9605 D(x): 0.5763 D(G(z)): 0.0674 / 0.0683\n", + "[888/1600][2/8] Loss_D: 0.7652 Loss_G: 1.9668 D(x): 0.6109 D(G(z)): 0.0699 / 0.0674\n", + "[888/1600][4/8] Loss_D: 0.7370 Loss_G: 2.0545 D(x): 0.5951 D(G(z)): 0.0587 / 0.0594\n", + "[888/1600][6/8] Loss_D: 0.6960 Loss_G: 2.0150 D(x): 0.6106 D(G(z)): 0.0612 / 0.0637\n", + "[889/1600][0/8] Loss_D: 0.6515 Loss_G: 1.9680 D(x): 0.6601 D(G(z)): 0.0657 / 0.0674\n", + "[889/1600][2/8] Loss_D: 0.7455 Loss_G: 1.9779 D(x): 0.6452 D(G(z)): 0.0703 / 0.0676\n", + "[889/1600][4/8] Loss_D: 0.7686 Loss_G: 2.0062 D(x): 0.6002 D(G(z)): 0.0663 / 0.0639\n", + "[889/1600][6/8] Loss_D: 0.7029 Loss_G: 2.0063 D(x): 0.6071 D(G(z)): 0.0626 / 0.0635\n", + "[890/1600][0/8] Loss_D: 0.7112 Loss_G: 2.0540 D(x): 0.6379 D(G(z)): 0.0596 / 0.0602\n", + "[890/1600][2/8] Loss_D: 0.7876 Loss_G: 1.9520 D(x): 0.6440 D(G(z)): 0.0730 / 0.0699\n", + "[890/1600][4/8] Loss_D: 0.7601 Loss_G: 2.0059 D(x): 0.5786 D(G(z)): 0.0668 / 0.0652\n", + "[890/1600][6/8] Loss_D: 0.7196 Loss_G: 1.9868 D(x): 0.5590 D(G(z)): 0.0614 / 0.0662\n", + "[891/1600][0/8] Loss_D: 0.6835 Loss_G: 1.9009 D(x): 0.6430 D(G(z)): 0.0717 / 0.0741\n", + "[891/1600][2/8] Loss_D: 0.7055 Loss_G: 1.8586 D(x): 0.6390 D(G(z)): 0.0790 / 0.0809\n", + "[891/1600][4/8] Loss_D: 0.7489 Loss_G: 1.8628 D(x): 0.6104 D(G(z)): 0.0814 / 0.0798\n", + "[891/1600][6/8] Loss_D: 0.7149 Loss_G: 2.0598 D(x): 0.6564 D(G(z)): 0.0614 / 0.0594\n", + "[892/1600][0/8] Loss_D: 0.7526 Loss_G: 1.9228 D(x): 0.6470 D(G(z)): 0.0743 / 0.0722\n", + "[892/1600][2/8] Loss_D: 0.7314 Loss_G: 1.9948 D(x): 0.6267 D(G(z)): 0.0683 / 0.0663\n", + "[892/1600][4/8] Loss_D: 0.7070 Loss_G: 2.0055 D(x): 0.6122 D(G(z)): 0.0629 / 0.0643\n", + "[892/1600][6/8] Loss_D: 0.6768 Loss_G: 1.9387 D(x): 0.6226 D(G(z)): 0.0665 / 0.0704\n", + "[893/1600][0/8] Loss_D: 0.7622 Loss_G: 1.9628 D(x): 0.6194 D(G(z)): 0.0688 / 0.0681\n", + "[893/1600][2/8] Loss_D: 0.7657 Loss_G: 1.9861 D(x): 0.6090 D(G(z)): 0.0675 / 0.0655\n", + "[893/1600][4/8] Loss_D: 0.6821 Loss_G: 1.9140 D(x): 0.6359 D(G(z)): 0.0718 / 0.0739\n", + "[893/1600][6/8] Loss_D: 0.7231 Loss_G: 1.9276 D(x): 0.6502 D(G(z)): 0.0723 / 0.0718\n", + "[894/1600][0/8] Loss_D: 0.7087 Loss_G: 1.9622 D(x): 0.6083 D(G(z)): 0.0678 / 0.0679\n", + "[894/1600][2/8] Loss_D: 0.7546 Loss_G: 2.0049 D(x): 0.6633 D(G(z)): 0.0646 / 0.0633\n", + "[894/1600][4/8] Loss_D: 0.6701 Loss_G: 1.9033 D(x): 0.6730 D(G(z)): 0.0729 / 0.0734\n", + "[894/1600][6/8] Loss_D: 0.7726 Loss_G: 1.9091 D(x): 0.6671 D(G(z)): 0.0774 / 0.0748\n", + "[895/1600][0/8] Loss_D: 0.7340 Loss_G: 2.0074 D(x): 0.6054 D(G(z)): 0.0651 / 0.0638\n", + "[895/1600][2/8] Loss_D: 0.7678 Loss_G: 2.0029 D(x): 0.5910 D(G(z)): 0.0671 / 0.0651\n", + "[895/1600][4/8] Loss_D: 0.6956 Loss_G: 2.0391 D(x): 0.6365 D(G(z)): 0.0614 / 0.0618\n", + "[895/1600][6/8] Loss_D: 0.7621 Loss_G: 2.0284 D(x): 0.5979 D(G(z)): 0.0626 / 0.0624\n", + "[896/1600][0/8] Loss_D: 0.7572 Loss_G: 2.0199 D(x): 0.5841 D(G(z)): 0.0625 / 0.0632\n", + "[896/1600][2/8] Loss_D: 0.6775 Loss_G: 1.8970 D(x): 0.6267 D(G(z)): 0.0723 / 0.0752\n", + "[896/1600][4/8] Loss_D: 0.7527 Loss_G: 1.9482 D(x): 0.6637 D(G(z)): 0.0714 / 0.0692\n", + "[896/1600][6/8] Loss_D: 0.7719 Loss_G: 2.0269 D(x): 0.6166 D(G(z)): 0.0654 / 0.0622\n", + "[897/1600][0/8] Loss_D: 0.7132 Loss_G: 2.0374 D(x): 0.6273 D(G(z)): 0.0619 / 0.0613\n", + "[897/1600][2/8] Loss_D: 0.7339 Loss_G: 2.0728 D(x): 0.6229 D(G(z)): 0.0591 / 0.0583\n", + "[897/1600][4/8] Loss_D: 0.7070 Loss_G: 2.0163 D(x): 0.5704 D(G(z)): 0.0588 / 0.0624\n", + "[897/1600][6/8] Loss_D: 0.7532 Loss_G: 2.0588 D(x): 0.6009 D(G(z)): 0.0610 / 0.0601\n", + "[898/1600][0/8] Loss_D: 0.7672 Loss_G: 2.0074 D(x): 0.5696 D(G(z)): 0.0633 / 0.0628\n", + "[898/1600][2/8] Loss_D: 0.6794 Loss_G: 1.9533 D(x): 0.6209 D(G(z)): 0.0670 / 0.0688\n", + "[898/1600][4/8] Loss_D: 0.6819 Loss_G: 1.9477 D(x): 0.6163 D(G(z)): 0.0656 / 0.0698\n", + "[898/1600][6/8] Loss_D: 0.7772 Loss_G: 1.9471 D(x): 0.6730 D(G(z)): 0.0712 / 0.0694\n", + "[899/1600][0/8] Loss_D: 0.6495 Loss_G: 1.9226 D(x): 0.6630 D(G(z)): 0.0719 / 0.0733\n", + "[899/1600][2/8] Loss_D: 0.6719 Loss_G: 1.9123 D(x): 0.6741 D(G(z)): 0.0722 / 0.0733\n", + "[899/1600][4/8] Loss_D: 0.6812 Loss_G: 1.8397 D(x): 0.6687 D(G(z)): 0.0798 / 0.0811\n", + "[899/1600][6/8] Loss_D: 0.6475 Loss_G: 1.9086 D(x): 0.6913 D(G(z)): 0.0752 / 0.0748\n", + "[900/1600][0/8] Loss_D: 0.6685 Loss_G: 1.9050 D(x): 0.6798 D(G(z)): 0.0759 / 0.0748\n", + "[900/1600][2/8] Loss_D: 0.7469 Loss_G: 1.8474 D(x): 0.6493 D(G(z)): 0.0814 / 0.0794\n", + "[900/1600][4/8] Loss_D: 0.7768 Loss_G: 1.9441 D(x): 0.6130 D(G(z)): 0.0734 / 0.0693\n", + "[900/1600][6/8] Loss_D: 0.6616 Loss_G: 2.0112 D(x): 0.6399 D(G(z)): 0.0612 / 0.0620\n", + "[901/1600][0/8] Loss_D: 0.7803 Loss_G: 1.9352 D(x): 0.6107 D(G(z)): 0.0742 / 0.0730\n", + "[901/1600][2/8] Loss_D: 0.7209 Loss_G: 1.9964 D(x): 0.6136 D(G(z)): 0.0651 / 0.0641\n", + "[901/1600][4/8] Loss_D: 0.7217 Loss_G: 2.0140 D(x): 0.5744 D(G(z)): 0.0622 / 0.0637\n", + "[901/1600][6/8] Loss_D: 0.7060 Loss_G: 1.9965 D(x): 0.6206 D(G(z)): 0.0642 / 0.0653\n", + "[902/1600][0/8] Loss_D: 0.7038 Loss_G: 1.8955 D(x): 0.6302 D(G(z)): 0.0739 / 0.0762\n", + "[902/1600][2/8] Loss_D: 0.6786 Loss_G: 1.8583 D(x): 0.6359 D(G(z)): 0.0750 / 0.0786\n", + "[902/1600][4/8] Loss_D: 0.7598 Loss_G: 1.7848 D(x): 0.6699 D(G(z)): 0.0897 / 0.0880\n", + "[902/1600][6/8] Loss_D: 0.7263 Loss_G: 1.8869 D(x): 0.6483 D(G(z)): 0.0791 / 0.0770\n", + "[903/1600][0/8] Loss_D: 0.7867 Loss_G: 1.9078 D(x): 0.6375 D(G(z)): 0.0768 / 0.0741\n", + "[903/1600][2/8] Loss_D: 0.6744 Loss_G: 1.9580 D(x): 0.6820 D(G(z)): 0.0689 / 0.0684\n", + "[903/1600][4/8] Loss_D: 0.7576 Loss_G: 1.9465 D(x): 0.6895 D(G(z)): 0.0726 / 0.0700\n", + "[903/1600][6/8] Loss_D: 0.7443 Loss_G: 1.9717 D(x): 0.6036 D(G(z)): 0.0694 / 0.0669\n", + "[904/1600][0/8] Loss_D: 0.7101 Loss_G: 2.0039 D(x): 0.6666 D(G(z)): 0.0656 / 0.0651\n", + "[904/1600][2/8] Loss_D: 0.7485 Loss_G: 1.8968 D(x): 0.5952 D(G(z)): 0.0724 / 0.0742\n", + "[904/1600][4/8] Loss_D: 0.7610 Loss_G: 1.9547 D(x): 0.6345 D(G(z)): 0.0711 / 0.0690\n", + "[904/1600][6/8] Loss_D: 0.6653 Loss_G: 1.9302 D(x): 0.6537 D(G(z)): 0.0701 / 0.0721\n", + "[905/1600][0/8] Loss_D: 0.6650 Loss_G: 1.9189 D(x): 0.6340 D(G(z)): 0.0711 / 0.0724\n", + "[905/1600][2/8] Loss_D: 0.7122 Loss_G: 1.9018 D(x): 0.6580 D(G(z)): 0.0758 / 0.0754\n", + "[905/1600][4/8] Loss_D: 0.7771 Loss_G: 1.9187 D(x): 0.6555 D(G(z)): 0.0753 / 0.0728\n", + "[905/1600][6/8] Loss_D: 0.6830 Loss_G: 1.8332 D(x): 0.6427 D(G(z)): 0.0823 / 0.0821\n", + "[906/1600][0/8] Loss_D: 0.7684 Loss_G: 2.0079 D(x): 0.6223 D(G(z)): 0.0667 / 0.0640\n", + "[906/1600][2/8] Loss_D: 0.7702 Loss_G: 2.0301 D(x): 0.6119 D(G(z)): 0.0646 / 0.0610\n", + "[906/1600][4/8] Loss_D: 0.7531 Loss_G: 2.1138 D(x): 0.5613 D(G(z)): 0.0558 / 0.0549\n", + "[906/1600][6/8] Loss_D: 0.7499 Loss_G: 2.1192 D(x): 0.5744 D(G(z)): 0.0519 / 0.0531\n", + "[907/1600][0/8] Loss_D: 0.7059 Loss_G: 2.0057 D(x): 0.6022 D(G(z)): 0.0610 / 0.0639\n", + "[907/1600][2/8] Loss_D: 0.6818 Loss_G: 1.9705 D(x): 0.6530 D(G(z)): 0.0655 / 0.0666\n", + "[907/1600][4/8] Loss_D: 0.7735 Loss_G: 2.0226 D(x): 0.5903 D(G(z)): 0.0645 / 0.0624\n", + "[907/1600][6/8] Loss_D: 0.6832 Loss_G: 2.0114 D(x): 0.6134 D(G(z)): 0.0597 / 0.0624\n", + "[908/1600][0/8] Loss_D: 0.7786 Loss_G: 1.9132 D(x): 0.6436 D(G(z)): 0.0749 / 0.0741\n", + "[908/1600][2/8] Loss_D: 0.7297 Loss_G: 1.9358 D(x): 0.6230 D(G(z)): 0.0704 / 0.0700\n", + "[908/1600][4/8] Loss_D: 0.6881 Loss_G: 1.8832 D(x): 0.6420 D(G(z)): 0.0748 / 0.0766\n", + "[908/1600][6/8] Loss_D: 0.7408 Loss_G: 1.8885 D(x): 0.6700 D(G(z)): 0.0765 / 0.0752\n", + "[909/1600][0/8] Loss_D: 0.7161 Loss_G: 1.8532 D(x): 0.6114 D(G(z)): 0.0791 / 0.0798\n", + "[909/1600][2/8] Loss_D: 0.7929 Loss_G: 1.9351 D(x): 0.6566 D(G(z)): 0.0746 / 0.0709\n", + "[909/1600][4/8] Loss_D: 0.7201 Loss_G: 1.9304 D(x): 0.6259 D(G(z)): 0.0702 / 0.0702\n", + "[909/1600][6/8] Loss_D: 0.7540 Loss_G: 1.8602 D(x): 0.6563 D(G(z)): 0.0812 / 0.0802\n", + "[910/1600][0/8] Loss_D: 0.7391 Loss_G: 1.9392 D(x): 0.6606 D(G(z)): 0.0740 / 0.0712\n", + "[910/1600][2/8] Loss_D: 0.7113 Loss_G: 1.9899 D(x): 0.6342 D(G(z)): 0.0662 / 0.0664\n", + "[910/1600][4/8] Loss_D: 0.6882 Loss_G: 2.0091 D(x): 0.6378 D(G(z)): 0.0638 / 0.0639\n", + "[910/1600][6/8] Loss_D: 0.7410 Loss_G: 1.9226 D(x): 0.6008 D(G(z)): 0.0735 / 0.0736\n", + "[911/1600][0/8] Loss_D: 0.7218 Loss_G: 1.9125 D(x): 0.5982 D(G(z)): 0.0720 / 0.0737\n", + "[911/1600][2/8] Loss_D: 0.7289 Loss_G: 1.9406 D(x): 0.6286 D(G(z)): 0.0699 / 0.0701\n", + "[911/1600][4/8] Loss_D: 0.6960 Loss_G: 1.9414 D(x): 0.6272 D(G(z)): 0.0693 / 0.0710\n", + "[911/1600][6/8] Loss_D: 0.7241 Loss_G: 1.9163 D(x): 0.6680 D(G(z)): 0.0746 / 0.0728\n", + "[912/1600][0/8] Loss_D: 0.6798 Loss_G: 1.9701 D(x): 0.6173 D(G(z)): 0.0652 / 0.0669\n", + "[912/1600][2/8] Loss_D: 0.7103 Loss_G: 2.0049 D(x): 0.6463 D(G(z)): 0.0658 / 0.0641\n", + "[912/1600][4/8] Loss_D: 0.6970 Loss_G: 1.9832 D(x): 0.6124 D(G(z)): 0.0635 / 0.0646\n", + "[912/1600][6/8] Loss_D: 0.7191 Loss_G: 1.8724 D(x): 0.6398 D(G(z)): 0.0754 / 0.0769\n", + "[913/1600][0/8] Loss_D: 0.7025 Loss_G: 1.8267 D(x): 0.6300 D(G(z)): 0.0811 / 0.0836\n", + "[913/1600][2/8] Loss_D: 0.6789 Loss_G: 1.8646 D(x): 0.6345 D(G(z)): 0.0786 / 0.0803\n", + "[913/1600][4/8] Loss_D: 0.7778 Loss_G: 1.8806 D(x): 0.6217 D(G(z)): 0.0798 / 0.0768\n", + "[913/1600][6/8] Loss_D: 0.6711 Loss_G: 1.9640 D(x): 0.6665 D(G(z)): 0.0692 / 0.0671\n", + "[914/1600][0/8] Loss_D: 0.7167 Loss_G: 1.9286 D(x): 0.6510 D(G(z)): 0.0701 / 0.0699\n", + "[914/1600][2/8] Loss_D: 0.7177 Loss_G: 1.9244 D(x): 0.6194 D(G(z)): 0.0713 / 0.0718\n", + "[914/1600][4/8] Loss_D: 0.7400 Loss_G: 1.9903 D(x): 0.6416 D(G(z)): 0.0686 / 0.0664\n", + "[914/1600][6/8] Loss_D: 0.6811 Loss_G: 1.9627 D(x): 0.6258 D(G(z)): 0.0658 / 0.0684\n", + "[915/1600][0/8] Loss_D: 0.7471 Loss_G: 1.9075 D(x): 0.5972 D(G(z)): 0.0732 / 0.0740\n", + "[915/1600][2/8] Loss_D: 0.6882 Loss_G: 1.9384 D(x): 0.6756 D(G(z)): 0.0718 / 0.0705\n", + "[915/1600][4/8] Loss_D: 0.7692 Loss_G: 1.8917 D(x): 0.6556 D(G(z)): 0.0773 / 0.0747\n", + "[915/1600][6/8] Loss_D: 0.7568 Loss_G: 1.9594 D(x): 0.6208 D(G(z)): 0.0698 / 0.0680\n", + "[916/1600][0/8] Loss_D: 0.7282 Loss_G: 1.8901 D(x): 0.6355 D(G(z)): 0.0763 / 0.0769\n", + "[916/1600][2/8] Loss_D: 0.6835 Loss_G: 1.9096 D(x): 0.6592 D(G(z)): 0.0722 / 0.0736\n", + "[916/1600][4/8] Loss_D: 0.7091 Loss_G: 1.8316 D(x): 0.6090 D(G(z)): 0.0793 / 0.0826\n", + "[916/1600][6/8] Loss_D: 0.7584 Loss_G: 1.8620 D(x): 0.6227 D(G(z)): 0.0797 / 0.0776\n", + "[917/1600][0/8] Loss_D: 0.6896 Loss_G: 1.9753 D(x): 0.6885 D(G(z)): 0.0691 / 0.0670\n", + "[917/1600][2/8] Loss_D: 0.6775 Loss_G: 1.9995 D(x): 0.6501 D(G(z)): 0.0648 / 0.0641\n", + "[917/1600][4/8] Loss_D: 0.7641 Loss_G: 2.0161 D(x): 0.6088 D(G(z)): 0.0644 / 0.0626\n", + "[917/1600][6/8] Loss_D: 0.6949 Loss_G: 2.0141 D(x): 0.6385 D(G(z)): 0.0635 / 0.0630\n", + "[918/1600][0/8] Loss_D: 0.7620 Loss_G: 2.0550 D(x): 0.5801 D(G(z)): 0.0598 / 0.0594\n", + "[918/1600][2/8] Loss_D: 0.7573 Loss_G: 2.0383 D(x): 0.5954 D(G(z)): 0.0627 / 0.0607\n", + "[918/1600][4/8] Loss_D: 0.7272 Loss_G: 2.0464 D(x): 0.5854 D(G(z)): 0.0581 / 0.0593\n", + "[918/1600][6/8] Loss_D: 0.6897 Loss_G: 2.0677 D(x): 0.6155 D(G(z)): 0.0568 / 0.0593\n", + "[919/1600][0/8] Loss_D: 0.6801 Loss_G: 1.9868 D(x): 0.6155 D(G(z)): 0.0630 / 0.0661\n", + "[919/1600][2/8] Loss_D: 0.6894 Loss_G: 1.9205 D(x): 0.6228 D(G(z)): 0.0698 / 0.0728\n", + "[919/1600][4/8] Loss_D: 0.7717 Loss_G: 1.9412 D(x): 0.6392 D(G(z)): 0.0731 / 0.0703\n", + "[919/1600][6/8] Loss_D: 0.6285 Loss_G: 1.9897 D(x): 0.6882 D(G(z)): 0.0650 / 0.0647\n", + "[920/1600][0/8] Loss_D: 0.6912 Loss_G: 1.9403 D(x): 0.6231 D(G(z)): 0.0696 / 0.0715\n", + "[920/1600][2/8] Loss_D: 0.7470 Loss_G: 1.9987 D(x): 0.6482 D(G(z)): 0.0657 / 0.0645\n", + "[920/1600][4/8] Loss_D: 0.7126 Loss_G: 1.9178 D(x): 0.6666 D(G(z)): 0.0751 / 0.0732\n", + "[920/1600][6/8] Loss_D: 0.7016 Loss_G: 1.8494 D(x): 0.6136 D(G(z)): 0.0787 / 0.0806\n", + "[921/1600][0/8] Loss_D: 0.7313 Loss_G: 1.9136 D(x): 0.6109 D(G(z)): 0.0722 / 0.0723\n", + "[921/1600][2/8] Loss_D: 0.6876 Loss_G: 1.9244 D(x): 0.6142 D(G(z)): 0.0700 / 0.0720\n", + "[921/1600][4/8] Loss_D: 0.6795 Loss_G: 1.8892 D(x): 0.6334 D(G(z)): 0.0725 / 0.0750\n", + "[921/1600][6/8] Loss_D: 0.7782 Loss_G: 1.9014 D(x): 0.6676 D(G(z)): 0.0758 / 0.0741\n", + "[922/1600][0/8] Loss_D: 0.7769 Loss_G: 1.9553 D(x): 0.6393 D(G(z)): 0.0718 / 0.0685\n", + "[922/1600][2/8] Loss_D: 0.6670 Loss_G: 1.9002 D(x): 0.6536 D(G(z)): 0.0739 / 0.0746\n", + "[922/1600][4/8] Loss_D: 0.7912 Loss_G: 1.8342 D(x): 0.6419 D(G(z)): 0.0837 / 0.0811\n", + "[922/1600][6/8] Loss_D: 0.7742 Loss_G: 1.9723 D(x): 0.6058 D(G(z)): 0.0697 / 0.0667\n", + "[923/1600][0/8] Loss_D: 0.7344 Loss_G: 2.0444 D(x): 0.5889 D(G(z)): 0.0602 / 0.0600\n", + "[923/1600][2/8] Loss_D: 0.7361 Loss_G: 2.0186 D(x): 0.5848 D(G(z)): 0.0609 / 0.0617\n", + "[923/1600][4/8] Loss_D: 0.7161 Loss_G: 2.0262 D(x): 0.5996 D(G(z)): 0.0611 / 0.0630\n", + "[923/1600][6/8] Loss_D: 0.6739 Loss_G: 1.9790 D(x): 0.6334 D(G(z)): 0.0660 / 0.0670\n", + "[924/1600][0/8] Loss_D: 0.7582 Loss_G: 1.9260 D(x): 0.6377 D(G(z)): 0.0738 / 0.0718\n", + "[924/1600][2/8] Loss_D: 0.7231 Loss_G: 1.9135 D(x): 0.6743 D(G(z)): 0.0743 / 0.0731\n", + "[924/1600][4/8] Loss_D: 0.7511 Loss_G: 1.9712 D(x): 0.6354 D(G(z)): 0.0672 / 0.0664\n", + "[924/1600][6/8] Loss_D: 0.7116 Loss_G: 1.8984 D(x): 0.6024 D(G(z)): 0.0708 / 0.0734\n", + "[925/1600][0/8] Loss_D: 0.7292 Loss_G: 1.8976 D(x): 0.6979 D(G(z)): 0.0759 / 0.0745\n", + "[925/1600][2/8] Loss_D: 0.7937 Loss_G: 1.9033 D(x): 0.6286 D(G(z)): 0.0786 / 0.0764\n", + "[925/1600][4/8] Loss_D: 0.7164 Loss_G: 1.9232 D(x): 0.6325 D(G(z)): 0.0736 / 0.0725\n", + "[925/1600][6/8] Loss_D: 0.7550 Loss_G: 1.9738 D(x): 0.6221 D(G(z)): 0.0686 / 0.0664\n", + "[926/1600][0/8] Loss_D: 0.6513 Loss_G: 1.9921 D(x): 0.6451 D(G(z)): 0.0634 / 0.0647\n", + "[926/1600][2/8] Loss_D: 0.7760 Loss_G: 1.9308 D(x): 0.6474 D(G(z)): 0.0705 / 0.0698\n", + "[926/1600][4/8] Loss_D: 0.6942 Loss_G: 1.9867 D(x): 0.6368 D(G(z)): 0.0646 / 0.0644\n", + "[926/1600][6/8] Loss_D: 0.7271 Loss_G: 2.0301 D(x): 0.6395 D(G(z)): 0.0618 / 0.0606\n", + "[927/1600][0/8] Loss_D: 0.7187 Loss_G: 2.0348 D(x): 0.6456 D(G(z)): 0.0616 / 0.0609\n", + "[927/1600][2/8] Loss_D: 0.7540 Loss_G: 1.9443 D(x): 0.5993 D(G(z)): 0.0717 / 0.0701\n", + "[927/1600][4/8] Loss_D: 0.7340 Loss_G: 2.0331 D(x): 0.5959 D(G(z)): 0.0611 / 0.0609\n", + "[927/1600][6/8] Loss_D: 0.7362 Loss_G: 2.0093 D(x): 0.5585 D(G(z)): 0.0606 / 0.0631\n", + "[928/1600][0/8] Loss_D: 0.7162 Loss_G: 1.9056 D(x): 0.5948 D(G(z)): 0.0696 / 0.0731\n", + "[928/1600][2/8] Loss_D: 0.7336 Loss_G: 1.9316 D(x): 0.6287 D(G(z)): 0.0697 / 0.0708\n", + "[928/1600][4/8] Loss_D: 0.7719 Loss_G: 1.9243 D(x): 0.6871 D(G(z)): 0.0764 / 0.0720\n", + "[928/1600][6/8] Loss_D: 0.7075 Loss_G: 2.0243 D(x): 0.5942 D(G(z)): 0.0613 / 0.0617\n", + "[929/1600][0/8] Loss_D: 0.7145 Loss_G: 2.0325 D(x): 0.6037 D(G(z)): 0.0595 / 0.0608\n", + "[929/1600][2/8] Loss_D: 0.7254 Loss_G: 1.8642 D(x): 0.6054 D(G(z)): 0.0765 / 0.0790\n", + "[929/1600][4/8] Loss_D: 0.7261 Loss_G: 1.9002 D(x): 0.6454 D(G(z)): 0.0732 / 0.0729\n", + "[929/1600][6/8] Loss_D: 0.7507 Loss_G: 1.8994 D(x): 0.6530 D(G(z)): 0.0753 / 0.0733\n", + "[930/1600][0/8] Loss_D: 0.6847 Loss_G: 1.9735 D(x): 0.6552 D(G(z)): 0.0677 / 0.0669\n", + "[930/1600][2/8] Loss_D: 0.7657 Loss_G: 1.9641 D(x): 0.6237 D(G(z)): 0.0688 / 0.0674\n", + "[930/1600][4/8] Loss_D: 0.7284 Loss_G: 2.0154 D(x): 0.6521 D(G(z)): 0.0646 / 0.0625\n", + "[930/1600][6/8] Loss_D: 0.7661 Loss_G: 2.0793 D(x): 0.5861 D(G(z)): 0.0577 / 0.0566\n", + "[931/1600][0/8] Loss_D: 0.6804 Loss_G: 1.9522 D(x): 0.6371 D(G(z)): 0.0665 / 0.0683\n", + "[931/1600][2/8] Loss_D: 0.7341 Loss_G: 1.9644 D(x): 0.6372 D(G(z)): 0.0685 / 0.0684\n", + "[931/1600][4/8] Loss_D: 0.6794 Loss_G: 1.8839 D(x): 0.6341 D(G(z)): 0.0723 / 0.0744\n", + "[931/1600][6/8] Loss_D: 0.7756 Loss_G: 1.9546 D(x): 0.6377 D(G(z)): 0.0706 / 0.0693\n", + "[932/1600][0/8] Loss_D: 0.7506 Loss_G: 1.9320 D(x): 0.5862 D(G(z)): 0.0714 / 0.0707\n", + "[932/1600][2/8] Loss_D: 0.7698 Loss_G: 2.0439 D(x): 0.5914 D(G(z)): 0.0634 / 0.0613\n", + "[932/1600][4/8] Loss_D: 0.7049 Loss_G: 2.0214 D(x): 0.6026 D(G(z)): 0.0611 / 0.0616\n", + "[932/1600][6/8] Loss_D: 0.7658 Loss_G: 2.0857 D(x): 0.6141 D(G(z)): 0.0582 / 0.0568\n", + "[933/1600][0/8] Loss_D: 0.7004 Loss_G: 2.0845 D(x): 0.6058 D(G(z)): 0.0545 / 0.0558\n", + "[933/1600][2/8] Loss_D: 0.6921 Loss_G: 2.0340 D(x): 0.6163 D(G(z)): 0.0585 / 0.0608\n", + "[933/1600][4/8] Loss_D: 0.7042 Loss_G: 1.9450 D(x): 0.6315 D(G(z)): 0.0678 / 0.0704\n", + "[933/1600][6/8] Loss_D: 0.7426 Loss_G: 1.9139 D(x): 0.5923 D(G(z)): 0.0702 / 0.0727\n", + "[934/1600][0/8] Loss_D: 0.7785 Loss_G: 1.8608 D(x): 0.6560 D(G(z)): 0.0813 / 0.0788\n", + "[934/1600][2/8] Loss_D: 0.7175 Loss_G: 1.9095 D(x): 0.6495 D(G(z)): 0.0761 / 0.0731\n", + "[934/1600][4/8] Loss_D: 0.6928 Loss_G: 1.9790 D(x): 0.6262 D(G(z)): 0.0661 / 0.0657\n", + "[934/1600][6/8] Loss_D: 0.7509 Loss_G: 1.9860 D(x): 0.5737 D(G(z)): 0.0638 / 0.0650\n", + "[935/1600][0/8] Loss_D: 0.7590 Loss_G: 2.0324 D(x): 0.6506 D(G(z)): 0.0635 / 0.0610\n", + "[935/1600][2/8] Loss_D: 0.7311 Loss_G: 2.0764 D(x): 0.6019 D(G(z)): 0.0589 / 0.0582\n", + "[935/1600][4/8] Loss_D: 0.6998 Loss_G: 2.0505 D(x): 0.6044 D(G(z)): 0.0591 / 0.0609\n", + "[935/1600][6/8] Loss_D: 0.6950 Loss_G: 1.9783 D(x): 0.6030 D(G(z)): 0.0635 / 0.0665\n", + "[936/1600][0/8] Loss_D: 0.7122 Loss_G: 1.9717 D(x): 0.6628 D(G(z)): 0.0678 / 0.0679\n", + "[936/1600][2/8] Loss_D: 0.7452 Loss_G: 1.9073 D(x): 0.6302 D(G(z)): 0.0747 / 0.0752\n", + "[936/1600][4/8] Loss_D: 0.7389 Loss_G: 1.9870 D(x): 0.6372 D(G(z)): 0.0676 / 0.0660\n", + "[936/1600][6/8] Loss_D: 0.7717 Loss_G: 1.9235 D(x): 0.6573 D(G(z)): 0.0732 / 0.0712\n", + "[937/1600][0/8] Loss_D: 0.7191 Loss_G: 1.9736 D(x): 0.6290 D(G(z)): 0.0693 / 0.0671\n", + "[937/1600][2/8] Loss_D: 0.7456 Loss_G: 2.0163 D(x): 0.6200 D(G(z)): 0.0630 / 0.0623\n", + "[937/1600][4/8] Loss_D: 0.6883 Loss_G: 1.9315 D(x): 0.6469 D(G(z)): 0.0685 / 0.0702\n", + "[937/1600][6/8] Loss_D: 0.6988 Loss_G: 1.9111 D(x): 0.6526 D(G(z)): 0.0745 / 0.0755\n", + "[938/1600][0/8] Loss_D: 0.7616 Loss_G: 1.8863 D(x): 0.6554 D(G(z)): 0.0765 / 0.0754\n", + "[938/1600][2/8] Loss_D: 0.7760 Loss_G: 1.9357 D(x): 0.6675 D(G(z)): 0.0729 / 0.0698\n", + "[938/1600][4/8] Loss_D: 0.7621 Loss_G: 1.8697 D(x): 0.5784 D(G(z)): 0.0767 / 0.0764\n", + "[938/1600][6/8] Loss_D: 0.7653 Loss_G: 1.9737 D(x): 0.6442 D(G(z)): 0.0696 / 0.0673\n", + "[939/1600][0/8] Loss_D: 0.7372 Loss_G: 2.0281 D(x): 0.5893 D(G(z)): 0.0617 / 0.0617\n", + "[939/1600][2/8] Loss_D: 0.6597 Loss_G: 1.9853 D(x): 0.6323 D(G(z)): 0.0646 / 0.0655\n", + "[939/1600][4/8] Loss_D: 0.7153 Loss_G: 1.9750 D(x): 0.6414 D(G(z)): 0.0663 / 0.0673\n", + "[939/1600][6/8] Loss_D: 0.7672 Loss_G: 1.9206 D(x): 0.6035 D(G(z)): 0.0734 / 0.0721\n", + "[940/1600][0/8] Loss_D: 0.7657 Loss_G: 2.0456 D(x): 0.6429 D(G(z)): 0.0633 / 0.0598\n", + "[940/1600][2/8] Loss_D: 0.7273 Loss_G: 2.1139 D(x): 0.5899 D(G(z)): 0.0544 / 0.0544\n", + "[940/1600][4/8] Loss_D: 0.7749 Loss_G: 1.9873 D(x): 0.6054 D(G(z)): 0.0659 / 0.0656\n", + "[940/1600][6/8] Loss_D: 0.7460 Loss_G: 1.9616 D(x): 0.6225 D(G(z)): 0.0675 / 0.0684\n", + "[941/1600][0/8] Loss_D: 0.7288 Loss_G: 1.9554 D(x): 0.5987 D(G(z)): 0.0664 / 0.0684\n", + "[941/1600][2/8] Loss_D: 0.7154 Loss_G: 1.9019 D(x): 0.6162 D(G(z)): 0.0722 / 0.0729\n", + "[941/1600][4/8] Loss_D: 0.7435 Loss_G: 1.9860 D(x): 0.5927 D(G(z)): 0.0657 / 0.0650\n", + "[941/1600][6/8] Loss_D: 0.7934 Loss_G: 1.9691 D(x): 0.6915 D(G(z)): 0.0701 / 0.0667\n", + "[942/1600][0/8] Loss_D: 0.7698 Loss_G: 1.9983 D(x): 0.6234 D(G(z)): 0.0673 / 0.0645\n", + "[942/1600][2/8] Loss_D: 0.7713 Loss_G: 2.0169 D(x): 0.5965 D(G(z)): 0.0651 / 0.0631\n", + "[942/1600][4/8] Loss_D: 0.7628 Loss_G: 2.0773 D(x): 0.6026 D(G(z)): 0.0598 / 0.0576\n", + "[942/1600][6/8] Loss_D: 0.7812 Loss_G: 2.1396 D(x): 0.5213 D(G(z)): 0.0511 / 0.0515\n", + "[943/1600][0/8] Loss_D: 0.7059 Loss_G: 2.0179 D(x): 0.6165 D(G(z)): 0.0589 / 0.0615\n", + "[943/1600][2/8] Loss_D: 0.7723 Loss_G: 1.9674 D(x): 0.6233 D(G(z)): 0.0655 / 0.0656\n", + "[943/1600][4/8] Loss_D: 0.7231 Loss_G: 1.9911 D(x): 0.5850 D(G(z)): 0.0650 / 0.0657\n", + "[943/1600][6/8] Loss_D: 0.7510 Loss_G: 2.0432 D(x): 0.6068 D(G(z)): 0.0617 / 0.0612\n", + "[944/1600][0/8] Loss_D: 0.7567 Loss_G: 1.9258 D(x): 0.5547 D(G(z)): 0.0698 / 0.0734\n", + "[944/1600][2/8] Loss_D: 0.7245 Loss_G: 1.9844 D(x): 0.6408 D(G(z)): 0.0663 / 0.0657\n", + "[944/1600][4/8] Loss_D: 0.7522 Loss_G: 1.9420 D(x): 0.6197 D(G(z)): 0.0710 / 0.0705\n", + "[944/1600][6/8] Loss_D: 0.7263 Loss_G: 1.9921 D(x): 0.6480 D(G(z)): 0.0651 / 0.0646\n", + "[945/1600][0/8] Loss_D: 0.6984 Loss_G: 1.9642 D(x): 0.5951 D(G(z)): 0.0649 / 0.0665\n", + "[945/1600][2/8] Loss_D: 0.7210 Loss_G: 1.9038 D(x): 0.6603 D(G(z)): 0.0742 / 0.0733\n", + "[945/1600][4/8] Loss_D: 0.6758 Loss_G: 1.9385 D(x): 0.6375 D(G(z)): 0.0679 / 0.0703\n", + "[945/1600][6/8] Loss_D: 0.7069 Loss_G: 1.9600 D(x): 0.6337 D(G(z)): 0.0673 / 0.0672\n", + "[946/1600][0/8] Loss_D: 0.7030 Loss_G: 1.9936 D(x): 0.6632 D(G(z)): 0.0668 / 0.0651\n", + "[946/1600][2/8] Loss_D: 0.7545 Loss_G: 2.0237 D(x): 0.6489 D(G(z)): 0.0628 / 0.0608\n", + "[946/1600][4/8] Loss_D: 0.6676 Loss_G: 1.9705 D(x): 0.6550 D(G(z)): 0.0673 / 0.0684\n", + "[946/1600][6/8] Loss_D: 0.6871 Loss_G: 1.9560 D(x): 0.6129 D(G(z)): 0.0653 / 0.0682\n", + "[947/1600][0/8] Loss_D: 0.7811 Loss_G: 1.9101 D(x): 0.6264 D(G(z)): 0.0748 / 0.0736\n", + "[947/1600][2/8] Loss_D: 0.7087 Loss_G: 1.9142 D(x): 0.6616 D(G(z)): 0.0750 / 0.0735\n", + "[947/1600][4/8] Loss_D: 0.7030 Loss_G: 1.9347 D(x): 0.6312 D(G(z)): 0.0681 / 0.0690\n", + "[947/1600][6/8] Loss_D: 0.6803 Loss_G: 1.8496 D(x): 0.6311 D(G(z)): 0.0769 / 0.0801\n", + "[948/1600][0/8] Loss_D: 0.6768 Loss_G: 1.8110 D(x): 0.6472 D(G(z)): 0.0826 / 0.0847\n", + "[948/1600][2/8] Loss_D: 0.7721 Loss_G: 1.8379 D(x): 0.6714 D(G(z)): 0.0846 / 0.0810\n", + "[948/1600][4/8] Loss_D: 0.7335 Loss_G: 1.9354 D(x): 0.6808 D(G(z)): 0.0754 / 0.0715\n", + "[948/1600][6/8] Loss_D: 0.7538 Loss_G: 1.9863 D(x): 0.6372 D(G(z)): 0.0695 / 0.0669\n", + "[949/1600][0/8] Loss_D: 0.7181 Loss_G: 1.9476 D(x): 0.6226 D(G(z)): 0.0698 / 0.0692\n", + "[949/1600][2/8] Loss_D: 0.7478 Loss_G: 1.9663 D(x): 0.5503 D(G(z)): 0.0652 / 0.0677\n", + "[949/1600][4/8] Loss_D: 0.7411 Loss_G: 1.9246 D(x): 0.6495 D(G(z)): 0.0716 / 0.0717\n", + "[949/1600][6/8] Loss_D: 0.7715 Loss_G: 2.0006 D(x): 0.6404 D(G(z)): 0.0661 / 0.0636\n", + "[950/1600][0/8] Loss_D: 0.7838 Loss_G: 2.0002 D(x): 0.6408 D(G(z)): 0.0675 / 0.0649\n", + "[950/1600][2/8] Loss_D: 0.7535 Loss_G: 2.0328 D(x): 0.5943 D(G(z)): 0.0631 / 0.0624\n", + "[950/1600][4/8] Loss_D: 0.7543 Loss_G: 1.9253 D(x): 0.6306 D(G(z)): 0.0716 / 0.0717\n", + "[950/1600][6/8] Loss_D: 0.6957 Loss_G: 1.9388 D(x): 0.6135 D(G(z)): 0.0691 / 0.0701\n", + "[951/1600][0/8] Loss_D: 0.7233 Loss_G: 2.0066 D(x): 0.5845 D(G(z)): 0.0618 / 0.0635\n", + "[951/1600][2/8] Loss_D: 0.7201 Loss_G: 1.9669 D(x): 0.6314 D(G(z)): 0.0678 / 0.0677\n", + "[951/1600][4/8] Loss_D: 0.7786 Loss_G: 1.9403 D(x): 0.5868 D(G(z)): 0.0713 / 0.0703\n", + "[951/1600][6/8] Loss_D: 0.6740 Loss_G: 1.9566 D(x): 0.6448 D(G(z)): 0.0689 / 0.0709\n", + "[952/1600][0/8] Loss_D: 0.6986 Loss_G: 1.8529 D(x): 0.6184 D(G(z)): 0.0776 / 0.0797\n", + "[952/1600][2/8] Loss_D: 0.6787 Loss_G: 1.9010 D(x): 0.6595 D(G(z)): 0.0736 / 0.0741\n", + "[952/1600][4/8] Loss_D: 0.7959 Loss_G: 1.9573 D(x): 0.6744 D(G(z)): 0.0714 / 0.0682\n", + "[952/1600][6/8] Loss_D: 0.6939 Loss_G: 1.9600 D(x): 0.6181 D(G(z)): 0.0687 / 0.0685\n", + "[953/1600][0/8] Loss_D: 0.6567 Loss_G: 1.9399 D(x): 0.6545 D(G(z)): 0.0690 / 0.0696\n", + "[953/1600][2/8] Loss_D: 0.7392 Loss_G: 1.9963 D(x): 0.6124 D(G(z)): 0.0658 / 0.0648\n", + "[953/1600][4/8] Loss_D: 0.6638 Loss_G: 1.9948 D(x): 0.6294 D(G(z)): 0.0629 / 0.0644\n", + "[953/1600][6/8] Loss_D: 0.7923 Loss_G: 1.9136 D(x): 0.5496 D(G(z)): 0.0730 / 0.0739\n", + "[954/1600][0/8] Loss_D: 0.6913 Loss_G: 1.9181 D(x): 0.6359 D(G(z)): 0.0709 / 0.0729\n", + "[954/1600][2/8] Loss_D: 0.7748 Loss_G: 1.9525 D(x): 0.6142 D(G(z)): 0.0725 / 0.0696\n", + "[954/1600][4/8] Loss_D: 0.6885 Loss_G: 1.9772 D(x): 0.6355 D(G(z)): 0.0653 / 0.0660\n", + "[954/1600][6/8] Loss_D: 0.7122 Loss_G: 1.9044 D(x): 0.6254 D(G(z)): 0.0713 / 0.0730\n", + "[955/1600][0/8] Loss_D: 0.7253 Loss_G: 1.8823 D(x): 0.6228 D(G(z)): 0.0748 / 0.0763\n", + "[955/1600][2/8] Loss_D: 0.7590 Loss_G: 1.9626 D(x): 0.6711 D(G(z)): 0.0720 / 0.0684\n", + "[955/1600][4/8] Loss_D: 0.7383 Loss_G: 2.0169 D(x): 0.6286 D(G(z)): 0.0647 / 0.0626\n", + "[955/1600][6/8] Loss_D: 0.7388 Loss_G: 1.9772 D(x): 0.6014 D(G(z)): 0.0668 / 0.0667\n", + "[956/1600][0/8] Loss_D: 0.7134 Loss_G: 1.9875 D(x): 0.5822 D(G(z)): 0.0642 / 0.0671\n", + "[956/1600][2/8] Loss_D: 0.7294 Loss_G: 1.9186 D(x): 0.6081 D(G(z)): 0.0686 / 0.0715\n", + "[956/1600][4/8] Loss_D: 0.7169 Loss_G: 1.8398 D(x): 0.6455 D(G(z)): 0.0794 / 0.0806\n", + "[956/1600][6/8] Loss_D: 0.6938 Loss_G: 1.9069 D(x): 0.6584 D(G(z)): 0.0736 / 0.0726\n", + "[957/1600][0/8] Loss_D: 0.6981 Loss_G: 1.8778 D(x): 0.6182 D(G(z)): 0.0763 / 0.0775\n", + "[957/1600][2/8] Loss_D: 0.6547 Loss_G: 1.8779 D(x): 0.6615 D(G(z)): 0.0743 / 0.0760\n", + "[957/1600][4/8] Loss_D: 0.6971 Loss_G: 1.9094 D(x): 0.6658 D(G(z)): 0.0744 / 0.0731\n", + "[957/1600][6/8] Loss_D: 0.6926 Loss_G: 1.8432 D(x): 0.6439 D(G(z)): 0.0800 / 0.0806\n", + "[958/1600][0/8] Loss_D: 0.7701 Loss_G: 1.9164 D(x): 0.6746 D(G(z)): 0.0742 / 0.0716\n", + "[958/1600][2/8] Loss_D: 0.7753 Loss_G: 2.0170 D(x): 0.6430 D(G(z)): 0.0671 / 0.0628\n", + "[958/1600][4/8] Loss_D: 0.7011 Loss_G: 1.9824 D(x): 0.6178 D(G(z)): 0.0640 / 0.0652\n", + "[958/1600][6/8] Loss_D: 0.7620 Loss_G: 1.9744 D(x): 0.6024 D(G(z)): 0.0685 / 0.0669\n", + "[959/1600][0/8] Loss_D: 0.6864 Loss_G: 1.9514 D(x): 0.6097 D(G(z)): 0.0653 / 0.0685\n", + "[959/1600][2/8] Loss_D: 0.7063 Loss_G: 1.9836 D(x): 0.6464 D(G(z)): 0.0668 / 0.0661\n", + "[959/1600][4/8] Loss_D: 0.7737 Loss_G: 2.0432 D(x): 0.5866 D(G(z)): 0.0623 / 0.0609\n", + "[959/1600][6/8] Loss_D: 0.7333 Loss_G: 2.0159 D(x): 0.5662 D(G(z)): 0.0611 / 0.0631\n", + "[960/1600][0/8] Loss_D: 0.7053 Loss_G: 1.8969 D(x): 0.6316 D(G(z)): 0.0725 / 0.0734\n", + "[960/1600][2/8] Loss_D: 0.7515 Loss_G: 2.0368 D(x): 0.6262 D(G(z)): 0.0621 / 0.0611\n", + "[960/1600][4/8] Loss_D: 0.7358 Loss_G: 1.9647 D(x): 0.5745 D(G(z)): 0.0671 / 0.0675\n", + "[960/1600][6/8] Loss_D: 0.7149 Loss_G: 1.9924 D(x): 0.6070 D(G(z)): 0.0636 / 0.0644\n", + "[961/1600][0/8] Loss_D: 0.6750 Loss_G: 1.9886 D(x): 0.6219 D(G(z)): 0.0620 / 0.0642\n", + "[961/1600][2/8] Loss_D: 0.6795 Loss_G: 2.0181 D(x): 0.6265 D(G(z)): 0.0609 / 0.0620\n", + "[961/1600][4/8] Loss_D: 0.7965 Loss_G: 1.9279 D(x): 0.6598 D(G(z)): 0.0744 / 0.0720\n", + "[961/1600][6/8] Loss_D: 0.7147 Loss_G: 1.9429 D(x): 0.6090 D(G(z)): 0.0694 / 0.0695\n", + "[962/1600][0/8] Loss_D: 0.6911 Loss_G: 1.9870 D(x): 0.6273 D(G(z)): 0.0650 / 0.0658\n", + "[962/1600][2/8] Loss_D: 0.6914 Loss_G: 1.8615 D(x): 0.6328 D(G(z)): 0.0747 / 0.0784\n", + "[962/1600][4/8] Loss_D: 0.6693 Loss_G: 1.9618 D(x): 0.6230 D(G(z)): 0.0671 / 0.0683\n", + "[962/1600][6/8] Loss_D: 0.7438 Loss_G: 1.8709 D(x): 0.6557 D(G(z)): 0.0803 / 0.0779\n", + "[963/1600][0/8] Loss_D: 0.7530 Loss_G: 2.0194 D(x): 0.6129 D(G(z)): 0.0669 / 0.0641\n", + "[963/1600][2/8] Loss_D: 0.7430 Loss_G: 1.9768 D(x): 0.6413 D(G(z)): 0.0659 / 0.0653\n", + "[963/1600][4/8] Loss_D: 0.7096 Loss_G: 2.0055 D(x): 0.6268 D(G(z)): 0.0628 / 0.0637\n", + "[963/1600][6/8] Loss_D: 0.6726 Loss_G: 1.9588 D(x): 0.6275 D(G(z)): 0.0640 / 0.0675\n", + "[964/1600][0/8] Loss_D: 0.7685 Loss_G: 1.8027 D(x): 0.6222 D(G(z)): 0.0888 / 0.0892\n", + "[964/1600][2/8] Loss_D: 0.7022 Loss_G: 1.7997 D(x): 0.6274 D(G(z)): 0.0830 / 0.0851\n", + "[964/1600][4/8] Loss_D: 0.7870 Loss_G: 1.8972 D(x): 0.6878 D(G(z)): 0.0767 / 0.0733\n", + "[964/1600][6/8] Loss_D: 0.7503 Loss_G: 1.9211 D(x): 0.6605 D(G(z)): 0.0779 / 0.0738\n", + "[965/1600][0/8] Loss_D: 0.6874 Loss_G: 1.9794 D(x): 0.6138 D(G(z)): 0.0666 / 0.0668\n", + "[965/1600][2/8] Loss_D: 0.6892 Loss_G: 1.9686 D(x): 0.6092 D(G(z)): 0.0639 / 0.0664\n", + "[965/1600][4/8] Loss_D: 0.7194 Loss_G: 1.9154 D(x): 0.6305 D(G(z)): 0.0740 / 0.0747\n", + "[965/1600][6/8] Loss_D: 0.6723 Loss_G: 1.9447 D(x): 0.6704 D(G(z)): 0.0695 / 0.0700\n", + "[966/1600][0/8] Loss_D: 0.6797 Loss_G: 1.8620 D(x): 0.6485 D(G(z)): 0.0767 / 0.0787\n", + "[966/1600][2/8] Loss_D: 0.7328 Loss_G: 1.8846 D(x): 0.6770 D(G(z)): 0.0774 / 0.0755\n", + "[966/1600][4/8] Loss_D: 0.7377 Loss_G: 1.8649 D(x): 0.6694 D(G(z)): 0.0800 / 0.0772\n", + "[966/1600][6/8] Loss_D: 0.6802 Loss_G: 1.9250 D(x): 0.6199 D(G(z)): 0.0728 / 0.0745\n", + "[967/1600][0/8] Loss_D: 0.6853 Loss_G: 1.8824 D(x): 0.6440 D(G(z)): 0.0757 / 0.0762\n", + "[967/1600][2/8] Loss_D: 0.7670 Loss_G: 1.9446 D(x): 0.6560 D(G(z)): 0.0735 / 0.0704\n", + "[967/1600][4/8] Loss_D: 0.6839 Loss_G: 1.9534 D(x): 0.6422 D(G(z)): 0.0686 / 0.0687\n", + "[967/1600][6/8] Loss_D: 0.6865 Loss_G: 1.8919 D(x): 0.6764 D(G(z)): 0.0731 / 0.0745\n", + "[968/1600][0/8] Loss_D: 0.7051 Loss_G: 1.9327 D(x): 0.6628 D(G(z)): 0.0709 / 0.0700\n", + "[968/1600][2/8] Loss_D: 0.7087 Loss_G: 1.8982 D(x): 0.6312 D(G(z)): 0.0742 / 0.0743\n", + "[968/1600][4/8] Loss_D: 0.7306 Loss_G: 1.8992 D(x): 0.6080 D(G(z)): 0.0723 / 0.0740\n", + "[968/1600][6/8] Loss_D: 0.6984 Loss_G: 1.9212 D(x): 0.6544 D(G(z)): 0.0725 / 0.0730\n", + "[969/1600][0/8] Loss_D: 0.7475 Loss_G: 1.8494 D(x): 0.6456 D(G(z)): 0.0821 / 0.0810\n", + "[969/1600][2/8] Loss_D: 0.7189 Loss_G: 1.8983 D(x): 0.6558 D(G(z)): 0.0765 / 0.0745\n", + "[969/1600][4/8] Loss_D: 0.7408 Loss_G: 1.9153 D(x): 0.6201 D(G(z)): 0.0741 / 0.0726\n", + "[969/1600][6/8] Loss_D: 0.6540 Loss_G: 1.9367 D(x): 0.6646 D(G(z)): 0.0709 / 0.0707\n", + "[970/1600][0/8] Loss_D: 0.7235 Loss_G: 1.9234 D(x): 0.6113 D(G(z)): 0.0713 / 0.0724\n", + "[970/1600][2/8] Loss_D: 0.7319 Loss_G: 1.9135 D(x): 0.6335 D(G(z)): 0.0718 / 0.0717\n", + "[970/1600][4/8] Loss_D: 0.6462 Loss_G: 1.9302 D(x): 0.6801 D(G(z)): 0.0698 / 0.0701\n", + "[970/1600][6/8] Loss_D: 0.7837 Loss_G: 1.9396 D(x): 0.6078 D(G(z)): 0.0718 / 0.0699\n", + "[971/1600][0/8] Loss_D: 0.7808 Loss_G: 2.0075 D(x): 0.6222 D(G(z)): 0.0667 / 0.0638\n", + "[971/1600][2/8] Loss_D: 0.7649 Loss_G: 1.9635 D(x): 0.5487 D(G(z)): 0.0681 / 0.0692\n", + "[971/1600][4/8] Loss_D: 0.7028 Loss_G: 2.0010 D(x): 0.6785 D(G(z)): 0.0642 / 0.0641\n", + "[971/1600][6/8] Loss_D: 0.7194 Loss_G: 1.9474 D(x): 0.5783 D(G(z)): 0.0685 / 0.0708\n", + "[972/1600][0/8] Loss_D: 0.7592 Loss_G: 1.9362 D(x): 0.6476 D(G(z)): 0.0733 / 0.0716\n", + "[972/1600][2/8] Loss_D: 0.7323 Loss_G: 1.9842 D(x): 0.6229 D(G(z)): 0.0664 / 0.0661\n", + "[972/1600][4/8] Loss_D: 0.7394 Loss_G: 1.8987 D(x): 0.5834 D(G(z)): 0.0746 / 0.0758\n", + "[972/1600][6/8] Loss_D: 0.7866 Loss_G: 1.9363 D(x): 0.6435 D(G(z)): 0.0715 / 0.0694\n", + "[973/1600][0/8] Loss_D: 0.7637 Loss_G: 2.0393 D(x): 0.5989 D(G(z)): 0.0616 / 0.0601\n", + "[973/1600][2/8] Loss_D: 0.6910 Loss_G: 2.0091 D(x): 0.6018 D(G(z)): 0.0628 / 0.0643\n", + "[973/1600][4/8] Loss_D: 0.7330 Loss_G: 1.9524 D(x): 0.6031 D(G(z)): 0.0671 / 0.0690\n", + "[973/1600][6/8] Loss_D: 0.7533 Loss_G: 1.9173 D(x): 0.6255 D(G(z)): 0.0733 / 0.0734\n", + "[974/1600][0/8] Loss_D: 0.7773 Loss_G: 1.9382 D(x): 0.6319 D(G(z)): 0.0724 / 0.0701\n", + "[974/1600][2/8] Loss_D: 0.6798 Loss_G: 1.9173 D(x): 0.6325 D(G(z)): 0.0711 / 0.0733\n", + "[974/1600][4/8] Loss_D: 0.7242 Loss_G: 1.9200 D(x): 0.6544 D(G(z)): 0.0739 / 0.0719\n", + "[974/1600][6/8] Loss_D: 0.6576 Loss_G: 1.9121 D(x): 0.6590 D(G(z)): 0.0714 / 0.0734\n", + "[975/1600][0/8] Loss_D: 0.7681 Loss_G: 2.0222 D(x): 0.6115 D(G(z)): 0.0645 / 0.0624\n", + "[975/1600][2/8] Loss_D: 0.7686 Loss_G: 1.9482 D(x): 0.6205 D(G(z)): 0.0743 / 0.0705\n", + "[975/1600][4/8] Loss_D: 0.7543 Loss_G: 1.9710 D(x): 0.5410 D(G(z)): 0.0644 / 0.0674\n", + "[975/1600][6/8] Loss_D: 0.7743 Loss_G: 1.9774 D(x): 0.6078 D(G(z)): 0.0682 / 0.0664\n", + "[976/1600][0/8] Loss_D: 0.7423 Loss_G: 1.9541 D(x): 0.6379 D(G(z)): 0.0683 / 0.0675\n", + "[976/1600][2/8] Loss_D: 0.7530 Loss_G: 1.9695 D(x): 0.6410 D(G(z)): 0.0672 / 0.0661\n", + "[976/1600][4/8] Loss_D: 0.7199 Loss_G: 1.9279 D(x): 0.6077 D(G(z)): 0.0690 / 0.0708\n", + "[976/1600][6/8] Loss_D: 0.7490 Loss_G: 1.9393 D(x): 0.6472 D(G(z)): 0.0720 / 0.0707\n", + "[977/1600][0/8] Loss_D: 0.7336 Loss_G: 1.8804 D(x): 0.6103 D(G(z)): 0.0755 / 0.0771\n", + "[977/1600][2/8] Loss_D: 0.7372 Loss_G: 1.8692 D(x): 0.6488 D(G(z)): 0.0765 / 0.0764\n", + "[977/1600][4/8] Loss_D: 0.7402 Loss_G: 1.8998 D(x): 0.6311 D(G(z)): 0.0767 / 0.0761\n", + "[977/1600][6/8] Loss_D: 0.6797 Loss_G: 1.8431 D(x): 0.6534 D(G(z)): 0.0827 / 0.0832\n", + "[978/1600][0/8] Loss_D: 0.6893 Loss_G: 1.8159 D(x): 0.6274 D(G(z)): 0.0823 / 0.0845\n", + "[978/1600][2/8] Loss_D: 0.7265 Loss_G: 1.9047 D(x): 0.6256 D(G(z)): 0.0738 / 0.0737\n", + "[978/1600][4/8] Loss_D: 0.7367 Loss_G: 1.8764 D(x): 0.6585 D(G(z)): 0.0793 / 0.0779\n", + "[978/1600][6/8] Loss_D: 0.7782 Loss_G: 1.8276 D(x): 0.6813 D(G(z)): 0.0865 / 0.0827\n", + "[979/1600][0/8] Loss_D: 0.7591 Loss_G: 1.9715 D(x): 0.6669 D(G(z)): 0.0694 / 0.0660\n", + "[979/1600][2/8] Loss_D: 0.6865 Loss_G: 1.9871 D(x): 0.6365 D(G(z)): 0.0665 / 0.0655\n", + "[979/1600][4/8] Loss_D: 0.7578 Loss_G: 1.9648 D(x): 0.5879 D(G(z)): 0.0674 / 0.0685\n", + "[979/1600][6/8] Loss_D: 0.7498 Loss_G: 1.9282 D(x): 0.6149 D(G(z)): 0.0719 / 0.0705\n", + "[980/1600][0/8] Loss_D: 0.7148 Loss_G: 1.9447 D(x): 0.5973 D(G(z)): 0.0675 / 0.0692\n", + "[980/1600][2/8] Loss_D: 0.7362 Loss_G: 1.9045 D(x): 0.5841 D(G(z)): 0.0718 / 0.0741\n", + "[980/1600][4/8] Loss_D: 0.7798 Loss_G: 1.9235 D(x): 0.7080 D(G(z)): 0.0735 / 0.0709\n", + "[980/1600][6/8] Loss_D: 0.7563 Loss_G: 1.8907 D(x): 0.6680 D(G(z)): 0.0776 / 0.0757\n", + "[981/1600][0/8] Loss_D: 0.7016 Loss_G: 1.8239 D(x): 0.6326 D(G(z)): 0.0825 / 0.0844\n", + "[981/1600][2/8] Loss_D: 0.6762 Loss_G: 1.8904 D(x): 0.6906 D(G(z)): 0.0768 / 0.0749\n", + "[981/1600][4/8] Loss_D: 0.7661 Loss_G: 1.8651 D(x): 0.6263 D(G(z)): 0.0808 / 0.0791\n", + "[981/1600][6/8] Loss_D: 0.7315 Loss_G: 1.9616 D(x): 0.6109 D(G(z)): 0.0695 / 0.0685\n", + "[982/1600][0/8] Loss_D: 0.6884 Loss_G: 1.9586 D(x): 0.6298 D(G(z)): 0.0674 / 0.0691\n", + "[982/1600][2/8] Loss_D: 0.7505 Loss_G: 1.9453 D(x): 0.6247 D(G(z)): 0.0709 / 0.0710\n", + "[982/1600][4/8] Loss_D: 0.7639 Loss_G: 1.9491 D(x): 0.6422 D(G(z)): 0.0729 / 0.0701\n", + "[982/1600][6/8] Loss_D: 0.7238 Loss_G: 1.9922 D(x): 0.6239 D(G(z)): 0.0667 / 0.0661\n", + "[983/1600][0/8] Loss_D: 0.7763 Loss_G: 2.0011 D(x): 0.5989 D(G(z)): 0.0666 / 0.0645\n", + "[983/1600][2/8] Loss_D: 0.7709 Loss_G: 2.0811 D(x): 0.6032 D(G(z)): 0.0610 / 0.0580\n", + "[983/1600][4/8] Loss_D: 0.7465 Loss_G: 2.0640 D(x): 0.5673 D(G(z)): 0.0587 / 0.0591\n", + "[983/1600][6/8] Loss_D: 0.6924 Loss_G: 1.9577 D(x): 0.6062 D(G(z)): 0.0634 / 0.0678\n", + "[984/1600][0/8] Loss_D: 0.7313 Loss_G: 1.9769 D(x): 0.6721 D(G(z)): 0.0672 / 0.0674\n", + "[984/1600][2/8] Loss_D: 0.6901 Loss_G: 1.8658 D(x): 0.6107 D(G(z)): 0.0739 / 0.0774\n", + "[984/1600][4/8] Loss_D: 0.7735 Loss_G: 1.8970 D(x): 0.7127 D(G(z)): 0.0787 / 0.0752\n", + "[984/1600][6/8] Loss_D: 0.7427 Loss_G: 1.9152 D(x): 0.6219 D(G(z)): 0.0740 / 0.0724\n", + "[985/1600][0/8] Loss_D: 0.7540 Loss_G: 1.8778 D(x): 0.5896 D(G(z)): 0.0747 / 0.0767\n", + "[985/1600][2/8] Loss_D: 0.7758 Loss_G: 1.9377 D(x): 0.6372 D(G(z)): 0.0739 / 0.0715\n", + "[985/1600][4/8] Loss_D: 0.7565 Loss_G: 1.8990 D(x): 0.6331 D(G(z)): 0.0752 / 0.0732\n", + "[985/1600][6/8] Loss_D: 0.6902 Loss_G: 1.9152 D(x): 0.7011 D(G(z)): 0.0751 / 0.0744\n", + "[986/1600][0/8] Loss_D: 0.7722 Loss_G: 1.9530 D(x): 0.6480 D(G(z)): 0.0717 / 0.0680\n", + "[986/1600][2/8] Loss_D: 0.7696 Loss_G: 1.9842 D(x): 0.5847 D(G(z)): 0.0665 / 0.0654\n", + "[986/1600][4/8] Loss_D: 0.7332 Loss_G: 1.9532 D(x): 0.5979 D(G(z)): 0.0678 / 0.0690\n", + "[986/1600][6/8] Loss_D: 0.7679 Loss_G: 1.9497 D(x): 0.5793 D(G(z)): 0.0700 / 0.0701\n", + "[987/1600][0/8] Loss_D: 0.7027 Loss_G: 1.9249 D(x): 0.6003 D(G(z)): 0.0667 / 0.0711\n", + "[987/1600][2/8] Loss_D: 0.7837 Loss_G: 1.9054 D(x): 0.6486 D(G(z)): 0.0789 / 0.0752\n", + "[987/1600][4/8] Loss_D: 0.7224 Loss_G: 1.9142 D(x): 0.6139 D(G(z)): 0.0732 / 0.0725\n", + "[987/1600][6/8] Loss_D: 0.7528 Loss_G: 1.9643 D(x): 0.6086 D(G(z)): 0.0697 / 0.0686\n", + "[988/1600][0/8] Loss_D: 0.7570 Loss_G: 1.9743 D(x): 0.5963 D(G(z)): 0.0670 / 0.0669\n", + "[988/1600][2/8] Loss_D: 0.7563 Loss_G: 1.9030 D(x): 0.6115 D(G(z)): 0.0757 / 0.0745\n", + "[988/1600][4/8] Loss_D: 0.7286 Loss_G: 1.9680 D(x): 0.6091 D(G(z)): 0.0693 / 0.0686\n", + "[988/1600][6/8] Loss_D: 0.7378 Loss_G: 1.9483 D(x): 0.6053 D(G(z)): 0.0700 / 0.0705\n", + "[989/1600][0/8] Loss_D: 0.7165 Loss_G: 1.9370 D(x): 0.5874 D(G(z)): 0.0680 / 0.0706\n", + "[989/1600][2/8] Loss_D: 0.7286 Loss_G: 1.8805 D(x): 0.6195 D(G(z)): 0.0751 / 0.0757\n", + "[989/1600][4/8] Loss_D: 0.7113 Loss_G: 1.9223 D(x): 0.6159 D(G(z)): 0.0721 / 0.0729\n", + "[989/1600][6/8] Loss_D: 0.7783 Loss_G: 1.9283 D(x): 0.6429 D(G(z)): 0.0748 / 0.0711\n", + "[990/1600][0/8] Loss_D: 0.7732 Loss_G: 1.9683 D(x): 0.5595 D(G(z)): 0.0686 / 0.0680\n", + "[990/1600][2/8] Loss_D: 0.7505 Loss_G: 1.9726 D(x): 0.6398 D(G(z)): 0.0680 / 0.0666\n", + "[990/1600][4/8] Loss_D: 0.7334 Loss_G: 1.9261 D(x): 0.6285 D(G(z)): 0.0727 / 0.0730\n", + "[990/1600][6/8] Loss_D: 0.6968 Loss_G: 1.8777 D(x): 0.6228 D(G(z)): 0.0739 / 0.0778\n", + "[991/1600][0/8] Loss_D: 0.7285 Loss_G: 1.8085 D(x): 0.6272 D(G(z)): 0.0819 / 0.0842\n", + "[991/1600][2/8] Loss_D: 0.7905 Loss_G: 1.9480 D(x): 0.6754 D(G(z)): 0.0736 / 0.0694\n", + "[991/1600][4/8] Loss_D: 0.7794 Loss_G: 1.9233 D(x): 0.6387 D(G(z)): 0.0770 / 0.0737\n", + "[991/1600][6/8] Loss_D: 0.7710 Loss_G: 1.9711 D(x): 0.6176 D(G(z)): 0.0716 / 0.0684\n", + "[992/1600][0/8] Loss_D: 0.6875 Loss_G: 2.0220 D(x): 0.6118 D(G(z)): 0.0609 / 0.0623\n", + "[992/1600][2/8] Loss_D: 0.7708 Loss_G: 1.9927 D(x): 0.5998 D(G(z)): 0.0670 / 0.0649\n", + "[992/1600][4/8] Loss_D: 0.7280 Loss_G: 1.9605 D(x): 0.5958 D(G(z)): 0.0663 / 0.0679\n", + "[992/1600][6/8] Loss_D: 0.6975 Loss_G: 1.8926 D(x): 0.6103 D(G(z)): 0.0717 / 0.0755\n", + "[993/1600][0/8] Loss_D: 0.7539 Loss_G: 1.8385 D(x): 0.5749 D(G(z)): 0.0783 / 0.0814\n", + "[993/1600][2/8] Loss_D: 0.7669 Loss_G: 1.8746 D(x): 0.6538 D(G(z)): 0.0821 / 0.0786\n", + "[993/1600][4/8] Loss_D: 0.7837 Loss_G: 2.0165 D(x): 0.6535 D(G(z)): 0.0669 / 0.0623\n", + "[993/1600][6/8] Loss_D: 0.7342 Loss_G: 1.9392 D(x): 0.5777 D(G(z)): 0.0694 / 0.0708\n", + "[994/1600][0/8] Loss_D: 0.7508 Loss_G: 1.8848 D(x): 0.5762 D(G(z)): 0.0734 / 0.0758\n", + "[994/1600][2/8] Loss_D: 0.6811 Loss_G: 1.8796 D(x): 0.6429 D(G(z)): 0.0752 / 0.0759\n", + "[994/1600][4/8] Loss_D: 0.7020 Loss_G: 1.9170 D(x): 0.6477 D(G(z)): 0.0731 / 0.0728\n", + "[994/1600][6/8] Loss_D: 0.7592 Loss_G: 1.9656 D(x): 0.5956 D(G(z)): 0.0682 / 0.0672\n", + "[995/1600][0/8] Loss_D: 0.7629 Loss_G: 1.9726 D(x): 0.6080 D(G(z)): 0.0686 / 0.0669\n", + "[995/1600][2/8] Loss_D: 0.7667 Loss_G: 1.9330 D(x): 0.6080 D(G(z)): 0.0691 / 0.0691\n", + "[995/1600][4/8] Loss_D: 0.6935 Loss_G: 1.8897 D(x): 0.6307 D(G(z)): 0.0737 / 0.0766\n", + "[995/1600][6/8] Loss_D: 0.7642 Loss_G: 1.8336 D(x): 0.6468 D(G(z)): 0.0822 / 0.0810\n", + "[996/1600][0/8] Loss_D: 0.6614 Loss_G: 1.8870 D(x): 0.6699 D(G(z)): 0.0749 / 0.0764\n", + "[996/1600][2/8] Loss_D: 0.7986 Loss_G: 1.8491 D(x): 0.6261 D(G(z)): 0.0853 / 0.0808\n", + "[996/1600][4/8] Loss_D: 0.7552 Loss_G: 1.9241 D(x): 0.6316 D(G(z)): 0.0757 / 0.0722\n", + "[996/1600][6/8] Loss_D: 0.7221 Loss_G: 1.9720 D(x): 0.5717 D(G(z)): 0.0647 / 0.0663\n", + "[997/1600][0/8] Loss_D: 0.7403 Loss_G: 1.9419 D(x): 0.6102 D(G(z)): 0.0711 / 0.0715\n", + "[997/1600][2/8] Loss_D: 0.7377 Loss_G: 1.9058 D(x): 0.6309 D(G(z)): 0.0742 / 0.0736\n", + "[997/1600][4/8] Loss_D: 0.7549 Loss_G: 1.9482 D(x): 0.6101 D(G(z)): 0.0708 / 0.0708\n", + "[997/1600][6/8] Loss_D: 0.7560 Loss_G: 1.8758 D(x): 0.5733 D(G(z)): 0.0755 / 0.0756\n", + "[998/1600][0/8] Loss_D: 0.7439 Loss_G: 1.8957 D(x): 0.6454 D(G(z)): 0.0769 / 0.0760\n", + "[998/1600][2/8] Loss_D: 0.7128 Loss_G: 1.8601 D(x): 0.6247 D(G(z)): 0.0786 / 0.0788\n", + "[998/1600][4/8] Loss_D: 0.7428 Loss_G: 1.8438 D(x): 0.5943 D(G(z)): 0.0805 / 0.0815\n", + "[998/1600][6/8] Loss_D: 0.7396 Loss_G: 1.8072 D(x): 0.5990 D(G(z)): 0.0851 / 0.0863\n", + "[999/1600][0/8] Loss_D: 0.7555 Loss_G: 1.8861 D(x): 0.5916 D(G(z)): 0.0750 / 0.0757\n", + "[999/1600][2/8] Loss_D: 0.6673 Loss_G: 1.8491 D(x): 0.6548 D(G(z)): 0.0790 / 0.0804\n", + "[999/1600][4/8] Loss_D: 0.7473 Loss_G: 1.8291 D(x): 0.6332 D(G(z)): 0.0849 / 0.0846\n", + "[999/1600][6/8] Loss_D: 0.7282 Loss_G: 1.8097 D(x): 0.6085 D(G(z)): 0.0838 / 0.0838\n", + "[1000/1600][0/8] Loss_D: 0.7306 Loss_G: 1.8828 D(x): 0.6461 D(G(z)): 0.0755 / 0.0752\n", + "[1000/1600][2/8] Loss_D: 0.7204 Loss_G: 1.7134 D(x): 0.6413 D(G(z)): 0.0959 / 0.0972\n", + "[1000/1600][4/8] Loss_D: 0.6718 Loss_G: 1.7831 D(x): 0.6604 D(G(z)): 0.0885 / 0.0896\n", + "[1000/1600][6/8] Loss_D: 0.7365 Loss_G: 1.7680 D(x): 0.6480 D(G(z)): 0.0913 / 0.0907\n", + "[1001/1600][0/8] Loss_D: 0.8240 Loss_G: 1.7260 D(x): 0.6505 D(G(z)): 0.1021 / 0.0956\n", + "[1001/1600][2/8] Loss_D: 0.7126 Loss_G: 1.8123 D(x): 0.6032 D(G(z)): 0.0833 / 0.0840\n", + "[1001/1600][4/8] Loss_D: 0.7291 Loss_G: 1.8556 D(x): 0.6372 D(G(z)): 0.0782 / 0.0778\n", + "[1001/1600][6/8] Loss_D: 0.7456 Loss_G: 1.8930 D(x): 0.6381 D(G(z)): 0.0786 / 0.0760\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1002/1600][0/8] Loss_D: 0.7090 Loss_G: 1.8794 D(x): 0.5956 D(G(z)): 0.0751 / 0.0765\n", + "[1002/1600][2/8] Loss_D: 0.7528 Loss_G: 1.7845 D(x): 0.6273 D(G(z)): 0.0884 / 0.0897\n", + "[1002/1600][4/8] Loss_D: 0.7033 Loss_G: 1.8700 D(x): 0.6340 D(G(z)): 0.0790 / 0.0785\n", + "[1002/1600][6/8] Loss_D: 0.7722 Loss_G: 1.8202 D(x): 0.6475 D(G(z)): 0.0857 / 0.0841\n", + "[1003/1600][0/8] Loss_D: 0.7100 Loss_G: 1.8732 D(x): 0.5989 D(G(z)): 0.0749 / 0.0771\n", + "[1003/1600][2/8] Loss_D: 0.7495 Loss_G: 1.8269 D(x): 0.6490 D(G(z)): 0.0845 / 0.0834\n", + "[1003/1600][4/8] Loss_D: 0.7827 Loss_G: 1.8774 D(x): 0.6626 D(G(z)): 0.0811 / 0.0767\n", + "[1003/1600][6/8] Loss_D: 0.7630 Loss_G: 1.9033 D(x): 0.5860 D(G(z)): 0.0748 / 0.0730\n", + "[1004/1600][0/8] Loss_D: 0.7183 Loss_G: 1.9761 D(x): 0.6552 D(G(z)): 0.0665 / 0.0668\n", + "[1004/1600][2/8] Loss_D: 0.7321 Loss_G: 1.8667 D(x): 0.5895 D(G(z)): 0.0748 / 0.0770\n", + "[1004/1600][4/8] Loss_D: 0.7627 Loss_G: 1.7918 D(x): 0.6413 D(G(z)): 0.0890 / 0.0888\n", + "[1004/1600][6/8] Loss_D: 0.7641 Loss_G: 1.8317 D(x): 0.6026 D(G(z)): 0.0851 / 0.0835\n", + "[1005/1600][0/8] Loss_D: 0.7828 Loss_G: 1.9391 D(x): 0.5871 D(G(z)): 0.0728 / 0.0700\n", + "[1005/1600][2/8] Loss_D: 0.7791 Loss_G: 1.8851 D(x): 0.6061 D(G(z)): 0.0795 / 0.0757\n", + "[1005/1600][4/8] Loss_D: 0.7365 Loss_G: 2.0091 D(x): 0.5499 D(G(z)): 0.0617 / 0.0647\n", + "[1005/1600][6/8] Loss_D: 0.7605 Loss_G: 1.8660 D(x): 0.6043 D(G(z)): 0.0769 / 0.0784\n", + "[1006/1600][0/8] Loss_D: 0.7272 Loss_G: 1.8666 D(x): 0.6131 D(G(z)): 0.0774 / 0.0779\n", + "[1006/1600][2/8] Loss_D: 0.7186 Loss_G: 1.7960 D(x): 0.6252 D(G(z)): 0.0878 / 0.0879\n", + "[1006/1600][4/8] Loss_D: 0.7118 Loss_G: 1.8430 D(x): 0.6540 D(G(z)): 0.0815 / 0.0812\n", + "[1006/1600][6/8] Loss_D: 0.7343 Loss_G: 1.8448 D(x): 0.6058 D(G(z)): 0.0792 / 0.0797\n", + "[1007/1600][0/8] Loss_D: 0.7383 Loss_G: 1.7879 D(x): 0.6737 D(G(z)): 0.0900 / 0.0885\n", + "[1007/1600][2/8] Loss_D: 0.7296 Loss_G: 1.8158 D(x): 0.6350 D(G(z)): 0.0857 / 0.0858\n", + "[1007/1600][4/8] Loss_D: 0.7132 Loss_G: 1.7592 D(x): 0.6333 D(G(z)): 0.0901 / 0.0913\n", + "[1007/1600][6/8] Loss_D: 0.8095 Loss_G: 1.8671 D(x): 0.6701 D(G(z)): 0.0827 / 0.0782\n", + "[1008/1600][0/8] Loss_D: 0.7471 Loss_G: 1.9060 D(x): 0.6254 D(G(z)): 0.0775 / 0.0744\n", + "[1008/1600][2/8] Loss_D: 0.6977 Loss_G: 1.7952 D(x): 0.6189 D(G(z)): 0.0841 / 0.0866\n", + "[1008/1600][4/8] Loss_D: 0.6945 Loss_G: 1.8145 D(x): 0.6343 D(G(z)): 0.0827 / 0.0839\n", + "[1008/1600][6/8] Loss_D: 0.7740 Loss_G: 1.8408 D(x): 0.5536 D(G(z)): 0.0808 / 0.0819\n", + "[1009/1600][0/8] Loss_D: 0.6881 Loss_G: 1.8226 D(x): 0.6334 D(G(z)): 0.0819 / 0.0851\n", + "[1009/1600][2/8] Loss_D: 0.6864 Loss_G: 1.6738 D(x): 0.6731 D(G(z)): 0.1038 / 0.1050\n", + "[1009/1600][4/8] Loss_D: 0.7663 Loss_G: 1.7695 D(x): 0.5980 D(G(z)): 0.0923 / 0.0903\n", + "[1009/1600][6/8] Loss_D: 0.7570 Loss_G: 1.7132 D(x): 0.6520 D(G(z)): 0.0987 / 0.0966\n", + "[1010/1600][0/8] Loss_D: 0.7437 Loss_G: 1.8043 D(x): 0.6336 D(G(z)): 0.0873 / 0.0866\n", + "[1010/1600][2/8] Loss_D: 0.7552 Loss_G: 1.8459 D(x): 0.6482 D(G(z)): 0.0841 / 0.0819\n", + "[1010/1600][4/8] Loss_D: 0.7411 Loss_G: 1.7834 D(x): 0.6351 D(G(z)): 0.0907 / 0.0901\n", + "[1010/1600][6/8] Loss_D: 0.7704 Loss_G: 1.7772 D(x): 0.6347 D(G(z)): 0.0901 / 0.0879\n", + "[1011/1600][0/8] Loss_D: 0.7494 Loss_G: 1.8347 D(x): 0.6056 D(G(z)): 0.0857 / 0.0841\n", + "[1011/1600][2/8] Loss_D: 0.7617 Loss_G: 1.8052 D(x): 0.5987 D(G(z)): 0.0859 / 0.0850\n", + "[1011/1600][4/8] Loss_D: 0.7437 Loss_G: 1.8661 D(x): 0.6155 D(G(z)): 0.0792 / 0.0779\n", + "[1011/1600][6/8] Loss_D: 0.7521 Loss_G: 1.8385 D(x): 0.5680 D(G(z)): 0.0800 / 0.0821\n", + "[1012/1600][0/8] Loss_D: 0.7513 Loss_G: 1.8250 D(x): 0.5975 D(G(z)): 0.0801 / 0.0835\n", + "[1012/1600][2/8] Loss_D: 0.7230 Loss_G: 1.7378 D(x): 0.6057 D(G(z)): 0.0890 / 0.0924\n", + "[1012/1600][4/8] Loss_D: 0.8068 Loss_G: 1.7318 D(x): 0.6712 D(G(z)): 0.1002 / 0.0946\n", + "[1012/1600][6/8] Loss_D: 0.7358 Loss_G: 1.7453 D(x): 0.6132 D(G(z)): 0.0937 / 0.0922\n", + "[1013/1600][0/8] Loss_D: 0.6870 Loss_G: 1.7653 D(x): 0.6475 D(G(z)): 0.0896 / 0.0900\n", + "[1013/1600][2/8] Loss_D: 0.7184 Loss_G: 1.7554 D(x): 0.6347 D(G(z)): 0.0904 / 0.0918\n", + "[1013/1600][4/8] Loss_D: 0.7277 Loss_G: 1.7152 D(x): 0.6247 D(G(z)): 0.0963 / 0.0976\n", + "[1013/1600][6/8] Loss_D: 0.7063 Loss_G: 1.7463 D(x): 0.6131 D(G(z)): 0.0951 / 0.0957\n", + "[1014/1600][0/8] Loss_D: 0.7164 Loss_G: 1.7598 D(x): 0.6436 D(G(z)): 0.0887 / 0.0894\n", + "[1014/1600][2/8] Loss_D: 0.7613 Loss_G: 1.7041 D(x): 0.6555 D(G(z)): 0.1016 / 0.1000\n", + "[1014/1600][4/8] Loss_D: 0.7926 Loss_G: 1.8242 D(x): 0.6165 D(G(z)): 0.0866 / 0.0819\n", + "[1014/1600][6/8] Loss_D: 0.6923 Loss_G: 1.8608 D(x): 0.6302 D(G(z)): 0.0782 / 0.0792\n", + "[1015/1600][0/8] Loss_D: 0.8032 Loss_G: 1.7913 D(x): 0.6382 D(G(z)): 0.0913 / 0.0869\n", + "[1015/1600][2/8] Loss_D: 0.7980 Loss_G: 1.8216 D(x): 0.6074 D(G(z)): 0.0891 / 0.0844\n", + "[1015/1600][4/8] Loss_D: 0.7417 Loss_G: 1.9086 D(x): 0.5695 D(G(z)): 0.0734 / 0.0740\n", + "[1015/1600][6/8] Loss_D: 0.7830 Loss_G: 1.8821 D(x): 0.5900 D(G(z)): 0.0783 / 0.0774\n", + "[1016/1600][0/8] Loss_D: 0.7402 Loss_G: 1.8696 D(x): 0.6075 D(G(z)): 0.0780 / 0.0784\n", + "[1016/1600][2/8] Loss_D: 0.7340 Loss_G: 1.8738 D(x): 0.6202 D(G(z)): 0.0775 / 0.0771\n", + "[1016/1600][4/8] Loss_D: 0.7334 Loss_G: 1.8838 D(x): 0.5984 D(G(z)): 0.0748 / 0.0767\n", + "[1016/1600][6/8] Loss_D: 0.7862 Loss_G: 1.8560 D(x): 0.6582 D(G(z)): 0.0838 / 0.0803\n", + "[1017/1600][0/8] Loss_D: 0.7311 Loss_G: 1.9224 D(x): 0.5779 D(G(z)): 0.0719 / 0.0727\n", + "[1017/1600][2/8] Loss_D: 0.7492 Loss_G: 1.8963 D(x): 0.6049 D(G(z)): 0.0750 / 0.0742\n", + "[1017/1600][4/8] Loss_D: 0.7744 Loss_G: 1.8695 D(x): 0.5688 D(G(z)): 0.0776 / 0.0788\n", + "[1017/1600][6/8] Loss_D: 0.7385 Loss_G: 1.7955 D(x): 0.5982 D(G(z)): 0.0832 / 0.0862\n", + "[1018/1600][0/8] Loss_D: 0.6834 Loss_G: 1.7749 D(x): 0.6512 D(G(z)): 0.0885 / 0.0901\n", + "[1018/1600][2/8] Loss_D: 0.7909 Loss_G: 1.7222 D(x): 0.6207 D(G(z)): 0.0997 / 0.0980\n", + "[1018/1600][4/8] Loss_D: 0.7765 Loss_G: 1.7787 D(x): 0.6299 D(G(z)): 0.0939 / 0.0893\n", + "[1018/1600][6/8] Loss_D: 0.7758 Loss_G: 1.8309 D(x): 0.6596 D(G(z)): 0.0846 / 0.0812\n", + "[1019/1600][0/8] Loss_D: 0.8107 Loss_G: 1.7458 D(x): 0.6198 D(G(z)): 0.0971 / 0.0939\n", + "[1019/1600][2/8] Loss_D: 0.7373 Loss_G: 1.8336 D(x): 0.5731 D(G(z)): 0.0803 / 0.0822\n", + "[1019/1600][4/8] Loss_D: 0.7835 Loss_G: 1.7922 D(x): 0.6014 D(G(z)): 0.0914 / 0.0890\n", + "[1019/1600][6/8] Loss_D: 0.7676 Loss_G: 1.9058 D(x): 0.5702 D(G(z)): 0.0749 / 0.0752\n", + "[1020/1600][0/8] Loss_D: 0.7516 Loss_G: 1.8176 D(x): 0.5773 D(G(z)): 0.0832 / 0.0845\n", + "[1020/1600][2/8] Loss_D: 0.7760 Loss_G: 1.8724 D(x): 0.5609 D(G(z)): 0.0770 / 0.0774\n", + "[1020/1600][4/8] Loss_D: 0.7919 Loss_G: 1.7961 D(x): 0.6122 D(G(z)): 0.0893 / 0.0862\n", + "[1020/1600][6/8] Loss_D: 0.7505 Loss_G: 1.8352 D(x): 0.6079 D(G(z)): 0.0825 / 0.0810\n", + "[1021/1600][0/8] Loss_D: 0.7161 Loss_G: 1.8590 D(x): 0.5975 D(G(z)): 0.0783 / 0.0799\n", + "[1021/1600][2/8] Loss_D: 0.7359 Loss_G: 1.7938 D(x): 0.5735 D(G(z)): 0.0805 / 0.0858\n", + "[1021/1600][4/8] Loss_D: 0.7038 Loss_G: 1.7756 D(x): 0.6653 D(G(z)): 0.0900 / 0.0898\n", + "[1021/1600][6/8] Loss_D: 0.7351 Loss_G: 1.8022 D(x): 0.5924 D(G(z)): 0.0846 / 0.0851\n", + "[1022/1600][0/8] Loss_D: 0.7634 Loss_G: 1.7910 D(x): 0.6385 D(G(z)): 0.0900 / 0.0879\n", + "[1022/1600][2/8] Loss_D: 0.8087 Loss_G: 1.8014 D(x): 0.6341 D(G(z)): 0.0913 / 0.0856\n", + "[1022/1600][4/8] Loss_D: 0.7841 Loss_G: 1.8014 D(x): 0.5814 D(G(z)): 0.0878 / 0.0873\n", + "[1022/1600][6/8] Loss_D: 0.7786 Loss_G: 1.7695 D(x): 0.5578 D(G(z)): 0.0878 / 0.0900\n", + "[1023/1600][0/8] Loss_D: 0.7872 Loss_G: 1.8804 D(x): 0.5860 D(G(z)): 0.0789 / 0.0762\n", + "[1023/1600][2/8] Loss_D: 0.7841 Loss_G: 1.9010 D(x): 0.5990 D(G(z)): 0.0773 / 0.0746\n", + "[1023/1600][4/8] Loss_D: 0.7670 Loss_G: 1.8338 D(x): 0.6050 D(G(z)): 0.0826 / 0.0813\n", + "[1023/1600][6/8] Loss_D: 0.7915 Loss_G: 1.8697 D(x): 0.6177 D(G(z)): 0.0801 / 0.0781\n", + "[1024/1600][0/8] Loss_D: 0.7642 Loss_G: 1.8268 D(x): 0.5715 D(G(z)): 0.0803 / 0.0820\n", + "[1024/1600][2/8] Loss_D: 0.7635 Loss_G: 1.8364 D(x): 0.5892 D(G(z)): 0.0799 / 0.0804\n", + "[1024/1600][4/8] Loss_D: 0.7876 Loss_G: 1.8715 D(x): 0.5539 D(G(z)): 0.0790 / 0.0777\n", + "[1024/1600][6/8] Loss_D: 0.7997 Loss_G: 1.8327 D(x): 0.6145 D(G(z)): 0.0839 / 0.0814\n", + "[1025/1600][0/8] Loss_D: 0.7377 Loss_G: 1.8483 D(x): 0.5694 D(G(z)): 0.0777 / 0.0806\n", + "[1025/1600][2/8] Loss_D: 0.7450 Loss_G: 1.8807 D(x): 0.6428 D(G(z)): 0.0775 / 0.0760\n", + "[1025/1600][4/8] Loss_D: 0.7476 Loss_G: 1.8727 D(x): 0.5622 D(G(z)): 0.0759 / 0.0770\n", + "[1025/1600][6/8] Loss_D: 0.7530 Loss_G: 1.8487 D(x): 0.5926 D(G(z)): 0.0800 / 0.0806\n", + "[1026/1600][0/8] Loss_D: 0.7295 Loss_G: 1.8859 D(x): 0.6156 D(G(z)): 0.0781 / 0.0770\n", + "[1026/1600][2/8] Loss_D: 0.7354 Loss_G: 1.8224 D(x): 0.5733 D(G(z)): 0.0797 / 0.0835\n", + "[1026/1600][4/8] Loss_D: 0.7502 Loss_G: 1.7929 D(x): 0.6126 D(G(z)): 0.0880 / 0.0871\n", + "[1026/1600][6/8] Loss_D: 0.7400 Loss_G: 1.7978 D(x): 0.6008 D(G(z)): 0.0850 / 0.0856\n", + "[1027/1600][0/8] Loss_D: 0.7280 Loss_G: 1.8051 D(x): 0.5831 D(G(z)): 0.0830 / 0.0865\n", + "[1027/1600][2/8] Loss_D: 0.7358 Loss_G: 1.7423 D(x): 0.6166 D(G(z)): 0.0927 / 0.0947\n", + "[1027/1600][4/8] Loss_D: 0.7760 Loss_G: 1.8039 D(x): 0.5724 D(G(z)): 0.0852 / 0.0850\n", + "[1027/1600][6/8] Loss_D: 0.7636 Loss_G: 1.7168 D(x): 0.6586 D(G(z)): 0.1030 / 0.0988\n", + "[1028/1600][0/8] Loss_D: 0.7331 Loss_G: 1.7731 D(x): 0.5887 D(G(z)): 0.0879 / 0.0884\n", + "[1028/1600][2/8] Loss_D: 0.7650 Loss_G: 1.7260 D(x): 0.5740 D(G(z)): 0.0939 / 0.0959\n", + "[1028/1600][4/8] Loss_D: 0.7829 Loss_G: 1.7173 D(x): 0.6045 D(G(z)): 0.0977 / 0.0972\n", + "[1028/1600][6/8] Loss_D: 0.7965 Loss_G: 1.7834 D(x): 0.6298 D(G(z)): 0.0938 / 0.0885\n", + "[1029/1600][0/8] Loss_D: 0.6965 Loss_G: 1.8207 D(x): 0.6185 D(G(z)): 0.0812 / 0.0831\n", + "[1029/1600][2/8] Loss_D: 0.8062 Loss_G: 1.7097 D(x): 0.6147 D(G(z)): 0.0989 / 0.0973\n", + "[1029/1600][4/8] Loss_D: 0.7281 Loss_G: 1.7951 D(x): 0.6212 D(G(z)): 0.0886 / 0.0873\n", + "[1029/1600][6/8] Loss_D: 0.7777 Loss_G: 1.9027 D(x): 0.6320 D(G(z)): 0.0767 / 0.0733\n", + "[1030/1600][0/8] Loss_D: 0.7828 Loss_G: 1.8537 D(x): 0.5797 D(G(z)): 0.0826 / 0.0800\n", + "[1030/1600][2/8] Loss_D: 0.7476 Loss_G: 1.8890 D(x): 0.5624 D(G(z)): 0.0729 / 0.0754\n", + "[1030/1600][4/8] Loss_D: 0.7662 Loss_G: 1.8015 D(x): 0.6140 D(G(z)): 0.0850 / 0.0846\n", + "[1030/1600][6/8] Loss_D: 0.7954 Loss_G: 1.8321 D(x): 0.5618 D(G(z)): 0.0816 / 0.0815\n", + "[1031/1600][0/8] Loss_D: 0.7502 Loss_G: 1.8547 D(x): 0.6074 D(G(z)): 0.0789 / 0.0786\n", + "[1031/1600][2/8] Loss_D: 0.7575 Loss_G: 1.8088 D(x): 0.6179 D(G(z)): 0.0883 / 0.0857\n", + "[1031/1600][4/8] Loss_D: 0.7696 Loss_G: 1.7578 D(x): 0.5416 D(G(z)): 0.0863 / 0.0925\n", + "[1031/1600][6/8] Loss_D: 0.7584 Loss_G: 1.7701 D(x): 0.6332 D(G(z)): 0.0907 / 0.0894\n", + "[1032/1600][0/8] Loss_D: 0.7618 Loss_G: 1.8351 D(x): 0.6048 D(G(z)): 0.0835 / 0.0806\n", + "[1032/1600][2/8] Loss_D: 0.7589 Loss_G: 1.9089 D(x): 0.6206 D(G(z)): 0.0775 / 0.0752\n", + "[1032/1600][4/8] Loss_D: 0.7702 Loss_G: 1.8474 D(x): 0.6122 D(G(z)): 0.0815 / 0.0802\n", + "[1032/1600][6/8] Loss_D: 0.7765 Loss_G: 1.8511 D(x): 0.5688 D(G(z)): 0.0796 / 0.0794\n", + "[1033/1600][0/8] Loss_D: 0.7320 Loss_G: 1.7715 D(x): 0.6214 D(G(z)): 0.0871 / 0.0889\n", + "[1033/1600][2/8] Loss_D: 0.7795 Loss_G: 1.7463 D(x): 0.5554 D(G(z)): 0.0885 / 0.0914\n", + "[1033/1600][4/8] Loss_D: 0.7774 Loss_G: 1.8099 D(x): 0.6488 D(G(z)): 0.0881 / 0.0856\n", + "[1033/1600][6/8] Loss_D: 0.7465 Loss_G: 1.7075 D(x): 0.6569 D(G(z)): 0.0985 / 0.0973\n", + "[1034/1600][0/8] Loss_D: 0.7808 Loss_G: 1.8651 D(x): 0.6407 D(G(z)): 0.0823 / 0.0783\n", + "[1034/1600][2/8] Loss_D: 0.7773 Loss_G: 1.7795 D(x): 0.5782 D(G(z)): 0.0887 / 0.0879\n", + "[1034/1600][4/8] Loss_D: 0.7214 Loss_G: 1.8186 D(x): 0.6400 D(G(z)): 0.0855 / 0.0848\n", + "[1034/1600][6/8] Loss_D: 0.7906 Loss_G: 1.8358 D(x): 0.6391 D(G(z)): 0.0852 / 0.0811\n", + "[1035/1600][0/8] Loss_D: 0.7298 Loss_G: 1.8316 D(x): 0.6023 D(G(z)): 0.0796 / 0.0814\n", + "[1035/1600][2/8] Loss_D: 0.7640 Loss_G: 1.7025 D(x): 0.6145 D(G(z)): 0.0967 / 0.0983\n", + "[1035/1600][4/8] Loss_D: 0.7908 Loss_G: 1.8045 D(x): 0.6613 D(G(z)): 0.0908 / 0.0857\n", + "[1035/1600][6/8] Loss_D: 0.7516 Loss_G: 1.7315 D(x): 0.5977 D(G(z)): 0.0938 / 0.0949\n", + "[1036/1600][0/8] Loss_D: 0.8044 Loss_G: 1.7719 D(x): 0.6327 D(G(z)): 0.0939 / 0.0900\n", + "[1036/1600][2/8] Loss_D: 0.7242 Loss_G: 1.7922 D(x): 0.5910 D(G(z)): 0.0864 / 0.0881\n", + "[1036/1600][4/8] Loss_D: 0.6687 Loss_G: 1.7492 D(x): 0.6574 D(G(z)): 0.0916 / 0.0933\n", + "[1036/1600][6/8] Loss_D: 0.6967 Loss_G: 1.7740 D(x): 0.6352 D(G(z)): 0.0883 / 0.0894\n", + "[1037/1600][0/8] Loss_D: 0.7247 Loss_G: 1.7571 D(x): 0.6310 D(G(z)): 0.0930 / 0.0930\n", + "[1037/1600][2/8] Loss_D: 0.7350 Loss_G: 1.7686 D(x): 0.6160 D(G(z)): 0.0914 / 0.0901\n", + "[1037/1600][4/8] Loss_D: 0.7601 Loss_G: 1.8305 D(x): 0.6060 D(G(z)): 0.0832 / 0.0819\n", + "[1037/1600][6/8] Loss_D: 0.8013 Loss_G: 1.8356 D(x): 0.5342 D(G(z)): 0.0836 / 0.0819\n", + "[1038/1600][0/8] Loss_D: 0.7319 Loss_G: 1.8354 D(x): 0.5962 D(G(z)): 0.0796 / 0.0800\n", + "[1038/1600][2/8] Loss_D: 0.7822 Loss_G: 1.8146 D(x): 0.5928 D(G(z)): 0.0857 / 0.0852\n", + "[1038/1600][4/8] Loss_D: 0.7696 Loss_G: 1.7835 D(x): 0.5773 D(G(z)): 0.0868 / 0.0869\n", + "[1038/1600][6/8] Loss_D: 0.7774 Loss_G: 1.7976 D(x): 0.6469 D(G(z)): 0.0891 / 0.0865\n", + "[1039/1600][0/8] Loss_D: 0.7608 Loss_G: 1.9070 D(x): 0.6003 D(G(z)): 0.0738 / 0.0724\n", + "[1039/1600][2/8] Loss_D: 0.7563 Loss_G: 1.8888 D(x): 0.5994 D(G(z)): 0.0766 / 0.0753\n", + "[1039/1600][4/8] Loss_D: 0.7724 Loss_G: 1.8461 D(x): 0.5856 D(G(z)): 0.0811 / 0.0801\n", + "[1039/1600][6/8] Loss_D: 0.7422 Loss_G: 1.8749 D(x): 0.5891 D(G(z)): 0.0758 / 0.0759\n", + "[1040/1600][0/8] Loss_D: 0.7742 Loss_G: 1.7958 D(x): 0.5532 D(G(z)): 0.0834 / 0.0847\n", + "[1040/1600][2/8] Loss_D: 0.7513 Loss_G: 1.8586 D(x): 0.5786 D(G(z)): 0.0766 / 0.0782\n", + "[1040/1600][4/8] Loss_D: 0.7329 Loss_G: 1.8892 D(x): 0.6165 D(G(z)): 0.0774 / 0.0768\n", + "[1040/1600][6/8] Loss_D: 0.6943 Loss_G: 1.8168 D(x): 0.6420 D(G(z)): 0.0831 / 0.0838\n", + "[1041/1600][0/8] Loss_D: 0.7593 Loss_G: 1.7632 D(x): 0.5692 D(G(z)): 0.0872 / 0.0890\n", + "[1041/1600][2/8] Loss_D: 0.7567 Loss_G: 1.7365 D(x): 0.6013 D(G(z)): 0.0933 / 0.0947\n", + "[1041/1600][4/8] Loss_D: 0.7940 Loss_G: 1.7231 D(x): 0.6322 D(G(z)): 0.0987 / 0.0959\n", + "[1041/1600][6/8] Loss_D: 0.7408 Loss_G: 1.9116 D(x): 0.6310 D(G(z)): 0.0764 / 0.0731\n", + "[1042/1600][0/8] Loss_D: 0.7293 Loss_G: 1.8474 D(x): 0.6224 D(G(z)): 0.0816 / 0.0814\n", + "[1042/1600][2/8] Loss_D: 0.7311 Loss_G: 1.8693 D(x): 0.5897 D(G(z)): 0.0764 / 0.0776\n", + "[1042/1600][4/8] Loss_D: 0.7736 Loss_G: 1.8112 D(x): 0.6023 D(G(z)): 0.0855 / 0.0855\n", + "[1042/1600][6/8] Loss_D: 0.7019 Loss_G: 1.8437 D(x): 0.6521 D(G(z)): 0.0823 / 0.0818\n", + "[1043/1600][0/8] Loss_D: 0.6808 Loss_G: 1.7041 D(x): 0.6516 D(G(z)): 0.0964 / 0.0978\n", + "[1043/1600][2/8] Loss_D: 0.7219 Loss_G: 1.7947 D(x): 0.6473 D(G(z)): 0.0868 / 0.0867\n", + "[1043/1600][4/8] Loss_D: 0.7135 Loss_G: 1.8253 D(x): 0.5962 D(G(z)): 0.0796 / 0.0812\n", + "[1043/1600][6/8] Loss_D: 0.7713 Loss_G: 1.8108 D(x): 0.6467 D(G(z)): 0.0885 / 0.0845\n", + "[1044/1600][0/8] Loss_D: 0.7331 Loss_G: 1.8512 D(x): 0.6195 D(G(z)): 0.0816 / 0.0799\n", + "[1044/1600][2/8] Loss_D: 0.7117 Loss_G: 1.8423 D(x): 0.6138 D(G(z)): 0.0786 / 0.0806\n", + "[1044/1600][4/8] Loss_D: 0.7483 Loss_G: 1.8335 D(x): 0.5821 D(G(z)): 0.0795 / 0.0826\n", + "[1044/1600][6/8] Loss_D: 0.6902 Loss_G: 1.8027 D(x): 0.6554 D(G(z)): 0.0845 / 0.0858\n", + "[1045/1600][0/8] Loss_D: 0.7121 Loss_G: 1.7701 D(x): 0.6600 D(G(z)): 0.0933 / 0.0915\n", + "[1045/1600][2/8] Loss_D: 0.7249 Loss_G: 1.8359 D(x): 0.6303 D(G(z)): 0.0825 / 0.0832\n", + "[1045/1600][4/8] Loss_D: 0.7434 Loss_G: 1.7808 D(x): 0.6316 D(G(z)): 0.0896 / 0.0884\n", + "[1045/1600][6/8] Loss_D: 0.7746 Loss_G: 1.7780 D(x): 0.6407 D(G(z)): 0.0901 / 0.0879\n", + "[1046/1600][0/8] Loss_D: 0.7740 Loss_G: 1.7910 D(x): 0.5835 D(G(z)): 0.0897 / 0.0869\n", + "[1046/1600][2/8] Loss_D: 0.7092 Loss_G: 1.8534 D(x): 0.6351 D(G(z)): 0.0803 / 0.0807\n", + "[1046/1600][4/8] Loss_D: 0.6978 Loss_G: 1.8745 D(x): 0.6313 D(G(z)): 0.0770 / 0.0765\n", + "[1046/1600][6/8] Loss_D: 0.7769 Loss_G: 1.8226 D(x): 0.5794 D(G(z)): 0.0838 / 0.0832\n", + "[1047/1600][0/8] Loss_D: 0.7436 Loss_G: 1.9230 D(x): 0.5921 D(G(z)): 0.0709 / 0.0720\n", + "[1047/1600][2/8] Loss_D: 0.7216 Loss_G: 1.8487 D(x): 0.6743 D(G(z)): 0.0811 / 0.0806\n", + "[1047/1600][4/8] Loss_D: 0.7773 Loss_G: 1.7754 D(x): 0.6343 D(G(z)): 0.0927 / 0.0899\n", + "[1047/1600][6/8] Loss_D: 0.7046 Loss_G: 1.7624 D(x): 0.6520 D(G(z)): 0.0889 / 0.0893\n", + "[1048/1600][0/8] Loss_D: 0.7852 Loss_G: 1.7539 D(x): 0.6381 D(G(z)): 0.0937 / 0.0916\n", + "[1048/1600][2/8] Loss_D: 0.7552 Loss_G: 1.8006 D(x): 0.6230 D(G(z)): 0.0883 / 0.0867\n", + "[1048/1600][4/8] Loss_D: 0.7797 Loss_G: 1.8629 D(x): 0.6359 D(G(z)): 0.0820 / 0.0779\n", + "[1048/1600][6/8] Loss_D: 0.6871 Loss_G: 1.8899 D(x): 0.6428 D(G(z)): 0.0759 / 0.0752\n", + "[1049/1600][0/8] Loss_D: 0.7294 Loss_G: 1.8459 D(x): 0.5904 D(G(z)): 0.0778 / 0.0800\n", + "[1049/1600][2/8] Loss_D: 0.7273 Loss_G: 1.7296 D(x): 0.6387 D(G(z)): 0.0928 / 0.0941\n", + "[1049/1600][4/8] Loss_D: 0.7138 Loss_G: 1.8088 D(x): 0.6440 D(G(z)): 0.0837 / 0.0832\n", + "[1049/1600][6/8] Loss_D: 0.7530 Loss_G: 1.8394 D(x): 0.6191 D(G(z)): 0.0823 / 0.0808\n", + "[1050/1600][0/8] Loss_D: 0.7392 Loss_G: 1.8818 D(x): 0.6186 D(G(z)): 0.0789 / 0.0777\n", + "[1050/1600][2/8] Loss_D: 0.7164 Loss_G: 1.8053 D(x): 0.6025 D(G(z)): 0.0856 / 0.0871\n", + "[1050/1600][4/8] Loss_D: 0.6842 Loss_G: 1.8377 D(x): 0.6263 D(G(z)): 0.0797 / 0.0819\n", + "[1050/1600][6/8] Loss_D: 0.7906 Loss_G: 1.8322 D(x): 0.6575 D(G(z)): 0.0858 / 0.0824\n", + "[1051/1600][0/8] Loss_D: 0.7313 Loss_G: 1.9117 D(x): 0.6257 D(G(z)): 0.0755 / 0.0728\n", + "[1051/1600][2/8] Loss_D: 0.7080 Loss_G: 1.8767 D(x): 0.6007 D(G(z)): 0.0737 / 0.0762\n", + "[1051/1600][4/8] Loss_D: 0.7320 Loss_G: 1.7773 D(x): 0.6020 D(G(z)): 0.0859 / 0.0885\n", + "[1051/1600][6/8] Loss_D: 0.6851 Loss_G: 1.8111 D(x): 0.6410 D(G(z)): 0.0832 / 0.0854\n", + "[1052/1600][0/8] Loss_D: 0.7522 Loss_G: 1.8815 D(x): 0.6394 D(G(z)): 0.0767 / 0.0751\n", + "[1052/1600][2/8] Loss_D: 0.7774 Loss_G: 1.8597 D(x): 0.6538 D(G(z)): 0.0828 / 0.0791\n", + "[1052/1600][4/8] Loss_D: 0.7520 Loss_G: 1.8835 D(x): 0.5868 D(G(z)): 0.0777 / 0.0765\n", + "[1052/1600][6/8] Loss_D: 0.7767 Loss_G: 1.8959 D(x): 0.6148 D(G(z)): 0.0779 / 0.0757\n", + "[1053/1600][0/8] Loss_D: 0.7203 Loss_G: 1.8981 D(x): 0.6272 D(G(z)): 0.0743 / 0.0736\n", + "[1053/1600][2/8] Loss_D: 0.7160 Loss_G: 1.9512 D(x): 0.6249 D(G(z)): 0.0675 / 0.0691\n", + "[1053/1600][4/8] Loss_D: 0.7019 Loss_G: 1.8673 D(x): 0.6081 D(G(z)): 0.0761 / 0.0778\n", + "[1053/1600][6/8] Loss_D: 0.7115 Loss_G: 1.8750 D(x): 0.6421 D(G(z)): 0.0769 / 0.0782\n", + "[1054/1600][0/8] Loss_D: 0.7630 Loss_G: 1.7666 D(x): 0.6211 D(G(z)): 0.0906 / 0.0896\n", + "[1054/1600][2/8] Loss_D: 0.7124 Loss_G: 1.7452 D(x): 0.6561 D(G(z)): 0.0929 / 0.0925\n", + "[1054/1600][4/8] Loss_D: 0.7069 Loss_G: 1.8233 D(x): 0.6703 D(G(z)): 0.0868 / 0.0861\n", + "[1054/1600][6/8] Loss_D: 0.7438 Loss_G: 1.7947 D(x): 0.6464 D(G(z)): 0.0884 / 0.0869\n", + "[1055/1600][0/8] Loss_D: 0.7591 Loss_G: 1.8372 D(x): 0.6391 D(G(z)): 0.0842 / 0.0808\n", + "[1055/1600][2/8] Loss_D: 0.7717 Loss_G: 1.8407 D(x): 0.6622 D(G(z)): 0.0851 / 0.0814\n", + "[1055/1600][4/8] Loss_D: 0.7830 Loss_G: 1.9062 D(x): 0.6226 D(G(z)): 0.0773 / 0.0741\n", + "[1055/1600][6/8] Loss_D: 0.7688 Loss_G: 1.9742 D(x): 0.6070 D(G(z)): 0.0697 / 0.0671\n", + "[1056/1600][0/8] Loss_D: 0.7435 Loss_G: 1.9606 D(x): 0.5629 D(G(z)): 0.0675 / 0.0684\n", + "[1056/1600][2/8] Loss_D: 0.7418 Loss_G: 1.8397 D(x): 0.6220 D(G(z)): 0.0802 / 0.0816\n", + "[1056/1600][4/8] Loss_D: 0.7654 Loss_G: 1.8630 D(x): 0.6258 D(G(z)): 0.0800 / 0.0797\n", + "[1056/1600][6/8] Loss_D: 0.7932 Loss_G: 1.8932 D(x): 0.6498 D(G(z)): 0.0793 / 0.0756\n", + "[1057/1600][0/8] Loss_D: 0.7772 Loss_G: 1.9076 D(x): 0.6321 D(G(z)): 0.0793 / 0.0760\n", + "[1057/1600][2/8] Loss_D: 0.7577 Loss_G: 1.9048 D(x): 0.6490 D(G(z)): 0.0751 / 0.0739\n", + "[1057/1600][4/8] Loss_D: 0.6876 Loss_G: 1.8974 D(x): 0.6595 D(G(z)): 0.0744 / 0.0747\n", + "[1057/1600][6/8] Loss_D: 0.6912 Loss_G: 1.9041 D(x): 0.6170 D(G(z)): 0.0706 / 0.0727\n", + "[1058/1600][0/8] Loss_D: 0.6837 Loss_G: 1.8091 D(x): 0.6326 D(G(z)): 0.0806 / 0.0845\n", + "[1058/1600][2/8] Loss_D: 0.7467 Loss_G: 1.8174 D(x): 0.6487 D(G(z)): 0.0879 / 0.0855\n", + "[1058/1600][4/8] Loss_D: 0.7267 Loss_G: 1.8188 D(x): 0.6444 D(G(z)): 0.0862 / 0.0856\n", + "[1058/1600][6/8] Loss_D: 0.7535 Loss_G: 1.7968 D(x): 0.6708 D(G(z)): 0.0903 / 0.0877\n", + "[1059/1600][0/8] Loss_D: 0.7879 Loss_G: 1.9024 D(x): 0.6311 D(G(z)): 0.0794 / 0.0748\n", + "[1059/1600][2/8] Loss_D: 0.7669 Loss_G: 1.9276 D(x): 0.6075 D(G(z)): 0.0741 / 0.0715\n", + "[1059/1600][4/8] Loss_D: 0.7608 Loss_G: 2.0012 D(x): 0.5804 D(G(z)): 0.0645 / 0.0633\n", + "[1059/1600][6/8] Loss_D: 0.6916 Loss_G: 1.9174 D(x): 0.6484 D(G(z)): 0.0709 / 0.0723\n", + "[1060/1600][0/8] Loss_D: 0.7656 Loss_G: 1.9044 D(x): 0.6209 D(G(z)): 0.0754 / 0.0739\n", + "[1060/1600][2/8] Loss_D: 0.7804 Loss_G: 1.9197 D(x): 0.6180 D(G(z)): 0.0734 / 0.0719\n", + "[1060/1600][4/8] Loss_D: 0.7144 Loss_G: 1.8688 D(x): 0.6315 D(G(z)): 0.0746 / 0.0766\n", + "[1060/1600][6/8] Loss_D: 0.7660 Loss_G: 1.8854 D(x): 0.6085 D(G(z)): 0.0751 / 0.0753\n", + "[1061/1600][0/8] Loss_D: 0.7127 Loss_G: 1.8681 D(x): 0.6515 D(G(z)): 0.0784 / 0.0782\n", + "[1061/1600][2/8] Loss_D: 0.6973 Loss_G: 1.8174 D(x): 0.6353 D(G(z)): 0.0841 / 0.0852\n", + "[1061/1600][4/8] Loss_D: 0.6920 Loss_G: 1.8704 D(x): 0.6686 D(G(z)): 0.0786 / 0.0781\n", + "[1061/1600][6/8] Loss_D: 0.7276 Loss_G: 1.8085 D(x): 0.6205 D(G(z)): 0.0859 / 0.0855\n", + "[1062/1600][0/8] Loss_D: 0.7588 Loss_G: 1.9359 D(x): 0.6329 D(G(z)): 0.0753 / 0.0715\n", + "[1062/1600][2/8] Loss_D: 0.7350 Loss_G: 1.9591 D(x): 0.5724 D(G(z)): 0.0689 / 0.0694\n", + "[1062/1600][4/8] Loss_D: 0.7574 Loss_G: 1.8352 D(x): 0.6284 D(G(z)): 0.0814 / 0.0813\n", + "[1062/1600][6/8] Loss_D: 0.6852 Loss_G: 1.8927 D(x): 0.6353 D(G(z)): 0.0759 / 0.0771\n", + "[1063/1600][0/8] Loss_D: 0.7178 Loss_G: 1.9715 D(x): 0.6218 D(G(z)): 0.0670 / 0.0663\n", + "[1063/1600][2/8] Loss_D: 0.7338 Loss_G: 1.8815 D(x): 0.6088 D(G(z)): 0.0777 / 0.0772\n", + "[1063/1600][4/8] Loss_D: 0.6808 Loss_G: 1.8546 D(x): 0.6592 D(G(z)): 0.0787 / 0.0798\n", + "[1063/1600][6/8] Loss_D: 0.6888 Loss_G: 1.7934 D(x): 0.6366 D(G(z)): 0.0830 / 0.0863\n", + "[1064/1600][0/8] Loss_D: 0.6863 Loss_G: 1.7640 D(x): 0.6571 D(G(z)): 0.0913 / 0.0918\n", + "[1064/1600][2/8] Loss_D: 0.7056 Loss_G: 1.8493 D(x): 0.6795 D(G(z)): 0.0803 / 0.0796\n", + "[1064/1600][4/8] Loss_D: 0.7593 Loss_G: 1.8479 D(x): 0.6190 D(G(z)): 0.0846 / 0.0819\n", + "[1064/1600][6/8] Loss_D: 0.7723 Loss_G: 1.8589 D(x): 0.6482 D(G(z)): 0.0821 / 0.0789\n", + "[1065/1600][0/8] Loss_D: 0.6984 Loss_G: 1.8709 D(x): 0.6296 D(G(z)): 0.0783 / 0.0784\n", + "[1065/1600][2/8] Loss_D: 0.7114 Loss_G: 1.8879 D(x): 0.6354 D(G(z)): 0.0748 / 0.0760\n", + "[1065/1600][4/8] Loss_D: 0.7564 Loss_G: 1.8531 D(x): 0.6545 D(G(z)): 0.0823 / 0.0803\n", + "[1065/1600][6/8] Loss_D: 0.7726 Loss_G: 1.9388 D(x): 0.6378 D(G(z)): 0.0724 / 0.0693\n", + "[1066/1600][0/8] Loss_D: 0.7665 Loss_G: 1.8097 D(x): 0.5791 D(G(z)): 0.0839 / 0.0857\n", + "[1066/1600][2/8] Loss_D: 0.7829 Loss_G: 1.8608 D(x): 0.6445 D(G(z)): 0.0834 / 0.0794\n", + "[1066/1600][4/8] Loss_D: 0.7305 Loss_G: 1.9103 D(x): 0.6261 D(G(z)): 0.0738 / 0.0730\n", + "[1066/1600][6/8] Loss_D: 0.7539 Loss_G: 1.9944 D(x): 0.6320 D(G(z)): 0.0654 / 0.0640\n", + "[1067/1600][0/8] Loss_D: 0.7318 Loss_G: 1.9927 D(x): 0.5973 D(G(z)): 0.0667 / 0.0670\n", + "[1067/1600][2/8] Loss_D: 0.7582 Loss_G: 1.9430 D(x): 0.6235 D(G(z)): 0.0708 / 0.0702\n", + "[1067/1600][4/8] Loss_D: 0.6821 Loss_G: 1.8648 D(x): 0.6572 D(G(z)): 0.0766 / 0.0778\n", + "[1067/1600][6/8] Loss_D: 0.7022 Loss_G: 1.9380 D(x): 0.6211 D(G(z)): 0.0701 / 0.0716\n", + "[1068/1600][0/8] Loss_D: 0.7222 Loss_G: 1.9019 D(x): 0.6349 D(G(z)): 0.0762 / 0.0759\n", + "[1068/1600][2/8] Loss_D: 0.7809 Loss_G: 1.9131 D(x): 0.6236 D(G(z)): 0.0761 / 0.0736\n", + "[1068/1600][4/8] Loss_D: 0.7317 Loss_G: 1.9325 D(x): 0.6396 D(G(z)): 0.0735 / 0.0726\n", + "[1068/1600][6/8] Loss_D: 0.7244 Loss_G: 1.9443 D(x): 0.6353 D(G(z)): 0.0714 / 0.0703\n", + "[1069/1600][0/8] Loss_D: 0.7451 Loss_G: 1.9100 D(x): 0.5815 D(G(z)): 0.0726 / 0.0744\n", + "[1069/1600][2/8] Loss_D: 0.7031 Loss_G: 1.8664 D(x): 0.6018 D(G(z)): 0.0761 / 0.0787\n", + "[1069/1600][4/8] Loss_D: 0.6884 Loss_G: 1.8349 D(x): 0.6753 D(G(z)): 0.0808 / 0.0813\n", + "[1069/1600][6/8] Loss_D: 0.6661 Loss_G: 1.8326 D(x): 0.7055 D(G(z)): 0.0861 / 0.0840\n", + "[1070/1600][0/8] Loss_D: 0.7848 Loss_G: 1.8656 D(x): 0.6305 D(G(z)): 0.0822 / 0.0783\n", + "[1070/1600][2/8] Loss_D: 0.7137 Loss_G: 1.8549 D(x): 0.6320 D(G(z)): 0.0807 / 0.0812\n", + "[1070/1600][4/8] Loss_D: 0.7354 Loss_G: 1.9326 D(x): 0.6192 D(G(z)): 0.0708 / 0.0706\n", + "[1070/1600][6/8] Loss_D: 0.8015 Loss_G: 1.9927 D(x): 0.7111 D(G(z)): 0.0678 / 0.0639\n", + "[1071/1600][0/8] Loss_D: 0.7592 Loss_G: 1.9499 D(x): 0.6167 D(G(z)): 0.0714 / 0.0694\n", + "[1071/1600][2/8] Loss_D: 0.7281 Loss_G: 2.0398 D(x): 0.6305 D(G(z)): 0.0620 / 0.0609\n", + "[1071/1600][4/8] Loss_D: 0.7385 Loss_G: 1.9922 D(x): 0.6217 D(G(z)): 0.0664 / 0.0654\n", + "[1071/1600][6/8] Loss_D: 0.7017 Loss_G: 1.9721 D(x): 0.6375 D(G(z)): 0.0660 / 0.0672\n", + "[1072/1600][0/8] Loss_D: 0.7259 Loss_G: 1.8462 D(x): 0.6403 D(G(z)): 0.0795 / 0.0808\n", + "[1072/1600][2/8] Loss_D: 0.6947 Loss_G: 1.8907 D(x): 0.6574 D(G(z)): 0.0751 / 0.0762\n", + "[1072/1600][4/8] Loss_D: 0.7057 Loss_G: 1.8062 D(x): 0.6391 D(G(z)): 0.0857 / 0.0872\n", + "[1072/1600][6/8] Loss_D: 0.7524 Loss_G: 1.8489 D(x): 0.6525 D(G(z)): 0.0826 / 0.0810\n", + "[1073/1600][0/8] Loss_D: 0.6738 Loss_G: 1.8155 D(x): 0.6440 D(G(z)): 0.0832 / 0.0843\n", + "[1073/1600][2/8] Loss_D: 0.7264 Loss_G: 1.7830 D(x): 0.6052 D(G(z)): 0.0887 / 0.0906\n", + "[1073/1600][4/8] Loss_D: 0.7400 Loss_G: 1.8225 D(x): 0.6445 D(G(z)): 0.0880 / 0.0864\n", + "[1073/1600][6/8] Loss_D: 0.6836 Loss_G: 1.7439 D(x): 0.6744 D(G(z)): 0.0958 / 0.0948\n", + "[1074/1600][0/8] Loss_D: 0.6962 Loss_G: 1.7568 D(x): 0.6570 D(G(z)): 0.0920 / 0.0921\n", + "[1074/1600][2/8] Loss_D: 0.7747 Loss_G: 1.9616 D(x): 0.6051 D(G(z)): 0.0709 / 0.0680\n", + "[1074/1600][4/8] Loss_D: 0.7473 Loss_G: 1.8897 D(x): 0.6293 D(G(z)): 0.0779 / 0.0743\n", + "[1074/1600][6/8] Loss_D: 0.7005 Loss_G: 1.9167 D(x): 0.5974 D(G(z)): 0.0696 / 0.0728\n", + "[1075/1600][0/8] Loss_D: 0.7640 Loss_G: 1.8778 D(x): 0.6487 D(G(z)): 0.0794 / 0.0779\n", + "[1075/1600][2/8] Loss_D: 0.7880 Loss_G: 2.0020 D(x): 0.6199 D(G(z)): 0.0687 / 0.0656\n", + "[1075/1600][4/8] Loss_D: 0.7461 Loss_G: 1.9622 D(x): 0.6026 D(G(z)): 0.0705 / 0.0697\n", + "[1075/1600][6/8] Loss_D: 0.7610 Loss_G: 2.0350 D(x): 0.5980 D(G(z)): 0.0636 / 0.0615\n", + "[1076/1600][0/8] Loss_D: 0.7402 Loss_G: 2.0580 D(x): 0.5731 D(G(z)): 0.0595 / 0.0598\n", + "[1076/1600][2/8] Loss_D: 0.7774 Loss_G: 2.0671 D(x): 0.5392 D(G(z)): 0.0580 / 0.0583\n", + "[1076/1600][4/8] Loss_D: 0.7123 Loss_G: 1.9907 D(x): 0.6044 D(G(z)): 0.0618 / 0.0643\n", + "[1076/1600][6/8] Loss_D: 0.6983 Loss_G: 1.9906 D(x): 0.6408 D(G(z)): 0.0635 / 0.0653\n", + "[1077/1600][0/8] Loss_D: 0.7739 Loss_G: 1.9346 D(x): 0.6414 D(G(z)): 0.0732 / 0.0715\n", + "[1077/1600][2/8] Loss_D: 0.7814 Loss_G: 1.9419 D(x): 0.6225 D(G(z)): 0.0721 / 0.0696\n", + "[1077/1600][4/8] Loss_D: 0.7158 Loss_G: 1.8900 D(x): 0.5831 D(G(z)): 0.0735 / 0.0781\n", + "[1077/1600][6/8] Loss_D: 0.7189 Loss_G: 1.9190 D(x): 0.6527 D(G(z)): 0.0744 / 0.0742\n", + "[1078/1600][0/8] Loss_D: 0.6892 Loss_G: 1.9022 D(x): 0.6591 D(G(z)): 0.0753 / 0.0752\n", + "[1078/1600][2/8] Loss_D: 0.7713 Loss_G: 1.8364 D(x): 0.6377 D(G(z)): 0.0848 / 0.0819\n", + "[1078/1600][4/8] Loss_D: 0.7939 Loss_G: 1.8528 D(x): 0.6573 D(G(z)): 0.0842 / 0.0797\n", + "[1078/1600][6/8] Loss_D: 0.7456 Loss_G: 1.9354 D(x): 0.5882 D(G(z)): 0.0712 / 0.0711\n", + "[1079/1600][0/8] Loss_D: 0.6789 Loss_G: 1.9510 D(x): 0.6431 D(G(z)): 0.0697 / 0.0697\n", + "[1079/1600][2/8] Loss_D: 0.7450 Loss_G: 2.0258 D(x): 0.5911 D(G(z)): 0.0640 / 0.0635\n", + "[1079/1600][4/8] Loss_D: 0.7734 Loss_G: 1.9825 D(x): 0.6042 D(G(z)): 0.0662 / 0.0651\n", + "[1079/1600][6/8] Loss_D: 0.6969 Loss_G: 2.0064 D(x): 0.6184 D(G(z)): 0.0620 / 0.0642\n", + "[1080/1600][0/8] Loss_D: 0.6732 Loss_G: 1.8999 D(x): 0.6494 D(G(z)): 0.0736 / 0.0758\n", + "[1080/1600][2/8] Loss_D: 0.7431 Loss_G: 1.9557 D(x): 0.6379 D(G(z)): 0.0710 / 0.0687\n", + "[1080/1600][4/8] Loss_D: 0.7358 Loss_G: 1.9025 D(x): 0.6225 D(G(z)): 0.0764 / 0.0756\n", + "[1080/1600][6/8] Loss_D: 0.7350 Loss_G: 1.8816 D(x): 0.6395 D(G(z)): 0.0781 / 0.0767\n", + "[1081/1600][0/8] Loss_D: 0.7505 Loss_G: 1.9906 D(x): 0.6048 D(G(z)): 0.0679 / 0.0669\n", + "[1081/1600][2/8] Loss_D: 0.7424 Loss_G: 1.9991 D(x): 0.6357 D(G(z)): 0.0648 / 0.0636\n", + "[1081/1600][4/8] Loss_D: 0.7159 Loss_G: 1.9661 D(x): 0.6077 D(G(z)): 0.0666 / 0.0683\n", + "[1081/1600][6/8] Loss_D: 0.7664 Loss_G: 1.8878 D(x): 0.6336 D(G(z)): 0.0784 / 0.0775\n", + "[1082/1600][0/8] Loss_D: 0.6714 Loss_G: 1.9109 D(x): 0.6593 D(G(z)): 0.0730 / 0.0740\n", + "[1082/1600][2/8] Loss_D: 0.7614 Loss_G: 1.8782 D(x): 0.5893 D(G(z)): 0.0752 / 0.0761\n", + "[1082/1600][4/8] Loss_D: 0.6762 Loss_G: 2.0051 D(x): 0.6382 D(G(z)): 0.0636 / 0.0636\n", + "[1082/1600][6/8] Loss_D: 0.7889 Loss_G: 1.8807 D(x): 0.6270 D(G(z)): 0.0809 / 0.0768\n", + "[1083/1600][0/8] Loss_D: 0.7261 Loss_G: 1.9244 D(x): 0.5861 D(G(z)): 0.0716 / 0.0733\n", + "[1083/1600][2/8] Loss_D: 0.7113 Loss_G: 1.9756 D(x): 0.6737 D(G(z)): 0.0672 / 0.0668\n", + "[1083/1600][4/8] Loss_D: 0.7503 Loss_G: 1.8816 D(x): 0.6414 D(G(z)): 0.0779 / 0.0780\n", + "[1083/1600][6/8] Loss_D: 0.7265 Loss_G: 1.9116 D(x): 0.6200 D(G(z)): 0.0736 / 0.0744\n", + "[1084/1600][0/8] Loss_D: 0.7029 Loss_G: 1.9028 D(x): 0.6166 D(G(z)): 0.0717 / 0.0735\n", + "[1084/1600][2/8] Loss_D: 0.7877 Loss_G: 1.8198 D(x): 0.6113 D(G(z)): 0.0862 / 0.0845\n", + "[1084/1600][4/8] Loss_D: 0.7948 Loss_G: 1.9666 D(x): 0.6659 D(G(z)): 0.0734 / 0.0679\n", + "[1084/1600][6/8] Loss_D: 0.6973 Loss_G: 1.9440 D(x): 0.6339 D(G(z)): 0.0722 / 0.0710\n", + "[1085/1600][0/8] Loss_D: 0.7297 Loss_G: 1.9488 D(x): 0.6340 D(G(z)): 0.0681 / 0.0685\n", + "[1085/1600][2/8] Loss_D: 0.7106 Loss_G: 1.9455 D(x): 0.6233 D(G(z)): 0.0698 / 0.0704\n", + "[1085/1600][4/8] Loss_D: 0.7248 Loss_G: 1.8440 D(x): 0.6017 D(G(z)): 0.0787 / 0.0810\n", + "[1085/1600][6/8] Loss_D: 0.7670 Loss_G: 1.8947 D(x): 0.6199 D(G(z)): 0.0799 / 0.0781\n", + "[1086/1600][0/8] Loss_D: 0.7159 Loss_G: 1.8997 D(x): 0.6505 D(G(z)): 0.0764 / 0.0761\n", + "[1086/1600][2/8] Loss_D: 0.7054 Loss_G: 1.8598 D(x): 0.6192 D(G(z)): 0.0774 / 0.0794\n", + "[1086/1600][4/8] Loss_D: 0.7126 Loss_G: 1.8767 D(x): 0.6391 D(G(z)): 0.0786 / 0.0795\n", + "[1086/1600][6/8] Loss_D: 0.7860 Loss_G: 1.9056 D(x): 0.5967 D(G(z)): 0.0793 / 0.0754\n", + "[1087/1600][0/8] Loss_D: 0.7359 Loss_G: 1.9778 D(x): 0.6737 D(G(z)): 0.0695 / 0.0665\n", + "[1087/1600][2/8] Loss_D: 0.7098 Loss_G: 1.9368 D(x): 0.6153 D(G(z)): 0.0668 / 0.0693\n", + "[1087/1600][4/8] Loss_D: 0.7670 Loss_G: 1.8711 D(x): 0.5945 D(G(z)): 0.0792 / 0.0786\n", + "[1087/1600][6/8] Loss_D: 0.7046 Loss_G: 1.8947 D(x): 0.6246 D(G(z)): 0.0736 / 0.0754\n", + "[1088/1600][0/8] Loss_D: 0.6943 Loss_G: 1.8454 D(x): 0.6367 D(G(z)): 0.0808 / 0.0825\n", + "[1088/1600][2/8] Loss_D: 0.7461 Loss_G: 1.8680 D(x): 0.6503 D(G(z)): 0.0815 / 0.0793\n", + "[1088/1600][4/8] Loss_D: 0.7255 Loss_G: 1.9276 D(x): 0.6100 D(G(z)): 0.0739 / 0.0734\n", + "[1088/1600][6/8] Loss_D: 0.7003 Loss_G: 2.0268 D(x): 0.6055 D(G(z)): 0.0615 / 0.0622\n", + "[1089/1600][0/8] Loss_D: 0.7683 Loss_G: 1.9407 D(x): 0.6265 D(G(z)): 0.0747 / 0.0726\n", + "[1089/1600][2/8] Loss_D: 0.7496 Loss_G: 2.0898 D(x): 0.6163 D(G(z)): 0.0600 / 0.0577\n", + "[1089/1600][4/8] Loss_D: 0.7318 Loss_G: 1.9627 D(x): 0.6000 D(G(z)): 0.0702 / 0.0696\n", + "[1089/1600][6/8] Loss_D: 0.7184 Loss_G: 1.9331 D(x): 0.5826 D(G(z)): 0.0686 / 0.0727\n", + "[1090/1600][0/8] Loss_D: 0.7336 Loss_G: 1.9643 D(x): 0.6272 D(G(z)): 0.0690 / 0.0691\n", + "[1090/1600][2/8] Loss_D: 0.6510 Loss_G: 1.9224 D(x): 0.6559 D(G(z)): 0.0727 / 0.0734\n", + "[1090/1600][4/8] Loss_D: 0.7312 Loss_G: 1.9171 D(x): 0.6059 D(G(z)): 0.0712 / 0.0738\n", + "[1090/1600][6/8] Loss_D: 0.7421 Loss_G: 1.9280 D(x): 0.6223 D(G(z)): 0.0737 / 0.0721\n", + "[1091/1600][0/8] Loss_D: 0.7404 Loss_G: 1.8883 D(x): 0.6107 D(G(z)): 0.0782 / 0.0789\n", + "[1091/1600][2/8] Loss_D: 0.7553 Loss_G: 1.9670 D(x): 0.6474 D(G(z)): 0.0698 / 0.0683\n", + "[1091/1600][4/8] Loss_D: 0.7482 Loss_G: 1.9181 D(x): 0.6650 D(G(z)): 0.0750 / 0.0717\n", + "[1091/1600][6/8] Loss_D: 0.7173 Loss_G: 1.9627 D(x): 0.6404 D(G(z)): 0.0688 / 0.0696\n", + "[1092/1600][0/8] Loss_D: 0.6845 Loss_G: 1.9445 D(x): 0.6073 D(G(z)): 0.0673 / 0.0699\n", + "[1092/1600][2/8] Loss_D: 0.7749 Loss_G: 1.9524 D(x): 0.6863 D(G(z)): 0.0743 / 0.0702\n", + "[1092/1600][4/8] Loss_D: 0.7424 Loss_G: 1.9840 D(x): 0.5994 D(G(z)): 0.0680 / 0.0682\n", + "[1092/1600][6/8] Loss_D: 0.6464 Loss_G: 2.0035 D(x): 0.6523 D(G(z)): 0.0635 / 0.0650\n", + "[1093/1600][0/8] Loss_D: 0.7289 Loss_G: 1.9241 D(x): 0.6646 D(G(z)): 0.0750 / 0.0742\n", + "[1093/1600][2/8] Loss_D: 0.7419 Loss_G: 1.9249 D(x): 0.6656 D(G(z)): 0.0770 / 0.0732\n", + "[1093/1600][4/8] Loss_D: 0.7178 Loss_G: 1.9153 D(x): 0.6376 D(G(z)): 0.0748 / 0.0754\n", + "[1093/1600][6/8] Loss_D: 0.6921 Loss_G: 1.9832 D(x): 0.6294 D(G(z)): 0.0646 / 0.0667\n", + "[1094/1600][0/8] Loss_D: 0.7213 Loss_G: 1.8216 D(x): 0.5976 D(G(z)): 0.0815 / 0.0847\n", + "[1094/1600][2/8] Loss_D: 0.7005 Loss_G: 1.7934 D(x): 0.6171 D(G(z)): 0.0826 / 0.0867\n", + "[1094/1600][4/8] Loss_D: 0.7378 Loss_G: 1.8626 D(x): 0.6891 D(G(z)): 0.0829 / 0.0798\n", + "[1094/1600][6/8] Loss_D: 0.7651 Loss_G: 1.9246 D(x): 0.6555 D(G(z)): 0.0771 / 0.0725\n", + "[1095/1600][0/8] Loss_D: 0.7154 Loss_G: 2.0069 D(x): 0.6231 D(G(z)): 0.0651 / 0.0655\n", + "[1095/1600][2/8] Loss_D: 0.7620 Loss_G: 1.9864 D(x): 0.6593 D(G(z)): 0.0688 / 0.0661\n", + "[1095/1600][4/8] Loss_D: 0.7672 Loss_G: 2.0725 D(x): 0.6388 D(G(z)): 0.0623 / 0.0590\n", + "[1095/1600][6/8] Loss_D: 0.7479 Loss_G: 1.9906 D(x): 0.5602 D(G(z)): 0.0657 / 0.0683\n", + "[1096/1600][0/8] Loss_D: 0.7378 Loss_G: 1.8785 D(x): 0.6071 D(G(z)): 0.0759 / 0.0786\n", + "[1096/1600][2/8] Loss_D: 0.7665 Loss_G: 1.9608 D(x): 0.6529 D(G(z)): 0.0723 / 0.0700\n", + "[1096/1600][4/8] Loss_D: 0.6662 Loss_G: 1.9106 D(x): 0.6628 D(G(z)): 0.0738 / 0.0744\n", + "[1096/1600][6/8] Loss_D: 0.7398 Loss_G: 1.8448 D(x): 0.6514 D(G(z)): 0.0832 / 0.0823\n", + "[1097/1600][0/8] Loss_D: 0.7857 Loss_G: 1.8986 D(x): 0.6629 D(G(z)): 0.0786 / 0.0764\n", + "[1097/1600][2/8] Loss_D: 0.7227 Loss_G: 1.9203 D(x): 0.6516 D(G(z)): 0.0731 / 0.0724\n", + "[1097/1600][4/8] Loss_D: 0.7252 Loss_G: 1.9660 D(x): 0.6390 D(G(z)): 0.0700 / 0.0685\n", + "[1097/1600][6/8] Loss_D: 0.7432 Loss_G: 1.8806 D(x): 0.5672 D(G(z)): 0.0742 / 0.0776\n", + "[1098/1600][0/8] Loss_D: 0.7346 Loss_G: 1.9242 D(x): 0.6742 D(G(z)): 0.0759 / 0.0737\n", + "[1098/1600][2/8] Loss_D: 0.7503 Loss_G: 1.9598 D(x): 0.6322 D(G(z)): 0.0690 / 0.0680\n", + "[1098/1600][4/8] Loss_D: 0.7103 Loss_G: 1.8699 D(x): 0.6411 D(G(z)): 0.0785 / 0.0783\n", + "[1098/1600][6/8] Loss_D: 0.7033 Loss_G: 1.8632 D(x): 0.6448 D(G(z)): 0.0794 / 0.0793\n", + "[1099/1600][0/8] Loss_D: 0.7134 Loss_G: 1.9386 D(x): 0.6029 D(G(z)): 0.0707 / 0.0730\n", + "[1099/1600][2/8] Loss_D: 0.7154 Loss_G: 1.8626 D(x): 0.6559 D(G(z)): 0.0799 / 0.0801\n", + "[1099/1600][4/8] Loss_D: 0.7896 Loss_G: 1.9744 D(x): 0.6717 D(G(z)): 0.0728 / 0.0683\n", + "[1099/1600][6/8] Loss_D: 0.7102 Loss_G: 2.0708 D(x): 0.6782 D(G(z)): 0.0624 / 0.0587\n", + "[1100/1600][0/8] Loss_D: 0.7423 Loss_G: 2.0533 D(x): 0.6294 D(G(z)): 0.0637 / 0.0613\n", + "[1100/1600][2/8] Loss_D: 0.7327 Loss_G: 2.0358 D(x): 0.6217 D(G(z)): 0.0607 / 0.0612\n", + "[1100/1600][4/8] Loss_D: 0.7547 Loss_G: 2.0355 D(x): 0.5785 D(G(z)): 0.0606 / 0.0604\n", + "[1100/1600][6/8] Loss_D: 0.7674 Loss_G: 2.0212 D(x): 0.6238 D(G(z)): 0.0637 / 0.0634\n", + "[1101/1600][0/8] Loss_D: 0.7464 Loss_G: 1.9757 D(x): 0.6153 D(G(z)): 0.0678 / 0.0685\n", + "[1101/1600][2/8] Loss_D: 0.7780 Loss_G: 1.9690 D(x): 0.6129 D(G(z)): 0.0717 / 0.0691\n", + "[1101/1600][4/8] Loss_D: 0.7377 Loss_G: 2.0415 D(x): 0.5899 D(G(z)): 0.0640 / 0.0635\n", + "[1101/1600][6/8] Loss_D: 0.7224 Loss_G: 1.9915 D(x): 0.5915 D(G(z)): 0.0655 / 0.0664\n", + "[1102/1600][0/8] Loss_D: 0.7327 Loss_G: 1.9744 D(x): 0.6092 D(G(z)): 0.0670 / 0.0677\n", + "[1102/1600][2/8] Loss_D: 0.7766 Loss_G: 2.0148 D(x): 0.5804 D(G(z)): 0.0654 / 0.0655\n", + "[1102/1600][4/8] Loss_D: 0.7269 Loss_G: 1.9249 D(x): 0.6301 D(G(z)): 0.0714 / 0.0723\n", + "[1102/1600][6/8] Loss_D: 0.7065 Loss_G: 1.9703 D(x): 0.6280 D(G(z)): 0.0679 / 0.0687\n", + "[1103/1600][0/8] Loss_D: 0.7307 Loss_G: 1.9406 D(x): 0.6336 D(G(z)): 0.0699 / 0.0700\n", + "[1103/1600][2/8] Loss_D: 0.7800 Loss_G: 1.9816 D(x): 0.6256 D(G(z)): 0.0706 / 0.0672\n", + "[1103/1600][4/8] Loss_D: 0.7515 Loss_G: 1.9916 D(x): 0.6417 D(G(z)): 0.0691 / 0.0667\n", + "[1103/1600][6/8] Loss_D: 0.7202 Loss_G: 1.9255 D(x): 0.5767 D(G(z)): 0.0705 / 0.0743\n", + "[1104/1600][0/8] Loss_D: 0.7395 Loss_G: 1.9254 D(x): 0.6252 D(G(z)): 0.0731 / 0.0719\n", + "[1104/1600][2/8] Loss_D: 0.7102 Loss_G: 2.0040 D(x): 0.6275 D(G(z)): 0.0657 / 0.0654\n", + "[1104/1600][4/8] Loss_D: 0.7699 Loss_G: 2.0608 D(x): 0.6179 D(G(z)): 0.0621 / 0.0593\n", + "[1104/1600][6/8] Loss_D: 0.7367 Loss_G: 1.9689 D(x): 0.5941 D(G(z)): 0.0682 / 0.0676\n", + "[1105/1600][0/8] Loss_D: 0.6979 Loss_G: 1.9653 D(x): 0.6537 D(G(z)): 0.0669 / 0.0689\n", + "[1105/1600][2/8] Loss_D: 0.7231 Loss_G: 1.9782 D(x): 0.6138 D(G(z)): 0.0646 / 0.0658\n", + "[1105/1600][4/8] Loss_D: 0.7208 Loss_G: 1.9861 D(x): 0.5874 D(G(z)): 0.0640 / 0.0654\n", + "[1105/1600][6/8] Loss_D: 0.7118 Loss_G: 1.8809 D(x): 0.6330 D(G(z)): 0.0771 / 0.0780\n", + "[1106/1600][0/8] Loss_D: 0.7290 Loss_G: 1.9102 D(x): 0.6191 D(G(z)): 0.0737 / 0.0734\n", + "[1106/1600][2/8] Loss_D: 0.7472 Loss_G: 1.8863 D(x): 0.5944 D(G(z)): 0.0762 / 0.0773\n", + "[1106/1600][4/8] Loss_D: 0.7005 Loss_G: 1.9619 D(x): 0.6302 D(G(z)): 0.0701 / 0.0697\n", + "[1106/1600][6/8] Loss_D: 0.6894 Loss_G: 1.8895 D(x): 0.6272 D(G(z)): 0.0749 / 0.0759\n", + "[1107/1600][0/8] Loss_D: 0.7731 Loss_G: 1.9662 D(x): 0.6255 D(G(z)): 0.0711 / 0.0679\n", + "[1107/1600][2/8] Loss_D: 0.6981 Loss_G: 1.9840 D(x): 0.6132 D(G(z)): 0.0663 / 0.0677\n", + "[1107/1600][4/8] Loss_D: 0.7535 Loss_G: 1.9863 D(x): 0.5704 D(G(z)): 0.0658 / 0.0669\n", + "[1107/1600][6/8] Loss_D: 0.6597 Loss_G: 1.9883 D(x): 0.6560 D(G(z)): 0.0653 / 0.0676\n", + "[1108/1600][0/8] Loss_D: 0.6591 Loss_G: 1.9987 D(x): 0.6557 D(G(z)): 0.0641 / 0.0646\n", + "[1108/1600][2/8] Loss_D: 0.7634 Loss_G: 1.8819 D(x): 0.6374 D(G(z)): 0.0800 / 0.0777\n", + "[1108/1600][4/8] Loss_D: 0.6890 Loss_G: 1.8869 D(x): 0.6255 D(G(z)): 0.0760 / 0.0765\n", + "[1108/1600][6/8] Loss_D: 0.7038 Loss_G: 1.9344 D(x): 0.6372 D(G(z)): 0.0720 / 0.0722\n", + "[1109/1600][0/8] Loss_D: 0.7166 Loss_G: 2.0019 D(x): 0.6331 D(G(z)): 0.0654 / 0.0647\n", + "[1109/1600][2/8] Loss_D: 0.7611 Loss_G: 2.0205 D(x): 0.5849 D(G(z)): 0.0646 / 0.0639\n", + "[1109/1600][4/8] Loss_D: 0.7404 Loss_G: 1.9563 D(x): 0.5655 D(G(z)): 0.0661 / 0.0692\n", + "[1109/1600][6/8] Loss_D: 0.7304 Loss_G: 1.9530 D(x): 0.6606 D(G(z)): 0.0714 / 0.0705\n", + "[1110/1600][0/8] Loss_D: 0.7428 Loss_G: 1.9677 D(x): 0.6498 D(G(z)): 0.0707 / 0.0688\n", + "[1110/1600][2/8] Loss_D: 0.7718 Loss_G: 2.0087 D(x): 0.6565 D(G(z)): 0.0676 / 0.0661\n", + "[1110/1600][4/8] Loss_D: 0.7453 Loss_G: 1.9282 D(x): 0.5771 D(G(z)): 0.0682 / 0.0694\n", + "[1110/1600][6/8] Loss_D: 0.7209 Loss_G: 1.8919 D(x): 0.5988 D(G(z)): 0.0764 / 0.0773\n", + "[1111/1600][0/8] Loss_D: 0.6977 Loss_G: 1.9474 D(x): 0.6766 D(G(z)): 0.0728 / 0.0714\n", + "[1111/1600][2/8] Loss_D: 0.7721 Loss_G: 1.9810 D(x): 0.6162 D(G(z)): 0.0687 / 0.0664\n", + "[1111/1600][4/8] Loss_D: 0.7373 Loss_G: 2.0492 D(x): 0.6052 D(G(z)): 0.0614 / 0.0597\n", + "[1111/1600][6/8] Loss_D: 0.7521 Loss_G: 2.0104 D(x): 0.5746 D(G(z)): 0.0615 / 0.0629\n", + "[1112/1600][0/8] Loss_D: 0.6911 Loss_G: 1.9497 D(x): 0.6205 D(G(z)): 0.0658 / 0.0700\n", + "[1112/1600][2/8] Loss_D: 0.7847 Loss_G: 1.9328 D(x): 0.6661 D(G(z)): 0.0768 / 0.0733\n", + "[1112/1600][4/8] Loss_D: 0.7620 Loss_G: 1.9916 D(x): 0.5707 D(G(z)): 0.0680 / 0.0667\n", + "[1112/1600][6/8] Loss_D: 0.7466 Loss_G: 2.0462 D(x): 0.6040 D(G(z)): 0.0620 / 0.0607\n", + "[1113/1600][0/8] Loss_D: 0.7382 Loss_G: 2.0053 D(x): 0.6005 D(G(z)): 0.0632 / 0.0641\n", + "[1113/1600][2/8] Loss_D: 0.7552 Loss_G: 1.9182 D(x): 0.5855 D(G(z)): 0.0715 / 0.0733\n", + "[1113/1600][4/8] Loss_D: 0.6896 Loss_G: 1.9059 D(x): 0.6190 D(G(z)): 0.0736 / 0.0760\n", + "[1113/1600][6/8] Loss_D: 0.6512 Loss_G: 1.9162 D(x): 0.6660 D(G(z)): 0.0734 / 0.0745\n", + "[1114/1600][0/8] Loss_D: 0.7602 Loss_G: 1.9594 D(x): 0.5929 D(G(z)): 0.0701 / 0.0687\n", + "[1114/1600][2/8] Loss_D: 0.7808 Loss_G: 1.9560 D(x): 0.6455 D(G(z)): 0.0735 / 0.0712\n", + "[1114/1600][4/8] Loss_D: 0.7272 Loss_G: 1.9691 D(x): 0.6601 D(G(z)): 0.0699 / 0.0678\n", + "[1114/1600][6/8] Loss_D: 0.7069 Loss_G: 1.9302 D(x): 0.6100 D(G(z)): 0.0697 / 0.0720\n", + "[1115/1600][0/8] Loss_D: 0.7683 Loss_G: 1.9512 D(x): 0.6214 D(G(z)): 0.0700 / 0.0692\n", + "[1115/1600][2/8] Loss_D: 0.7421 Loss_G: 1.9729 D(x): 0.6242 D(G(z)): 0.0686 / 0.0672\n", + "[1115/1600][4/8] Loss_D: 0.7874 Loss_G: 2.0013 D(x): 0.6691 D(G(z)): 0.0686 / 0.0643\n", + "[1115/1600][6/8] Loss_D: 0.7627 Loss_G: 2.0803 D(x): 0.6082 D(G(z)): 0.0606 / 0.0583\n", + "[1116/1600][0/8] Loss_D: 0.6834 Loss_G: 2.0758 D(x): 0.6073 D(G(z)): 0.0561 / 0.0585\n", + "[1116/1600][2/8] Loss_D: 0.6517 Loss_G: 1.9233 D(x): 0.6619 D(G(z)): 0.0690 / 0.0731\n", + "[1116/1600][4/8] Loss_D: 0.7373 Loss_G: 1.9390 D(x): 0.6543 D(G(z)): 0.0714 / 0.0701\n", + "[1116/1600][6/8] Loss_D: 0.7286 Loss_G: 1.8597 D(x): 0.6109 D(G(z)): 0.0786 / 0.0798\n", + "[1117/1600][0/8] Loss_D: 0.7243 Loss_G: 1.9521 D(x): 0.6068 D(G(z)): 0.0691 / 0.0695\n", + "[1117/1600][2/8] Loss_D: 0.6733 Loss_G: 1.8703 D(x): 0.6535 D(G(z)): 0.0774 / 0.0783\n", + "[1117/1600][4/8] Loss_D: 0.6993 Loss_G: 1.8403 D(x): 0.6362 D(G(z)): 0.0816 / 0.0819\n", + "[1117/1600][6/8] Loss_D: 0.7343 Loss_G: 1.9010 D(x): 0.6299 D(G(z)): 0.0770 / 0.0755\n", + "[1118/1600][0/8] Loss_D: 0.6820 Loss_G: 1.9849 D(x): 0.6205 D(G(z)): 0.0675 / 0.0673\n", + "[1118/1600][2/8] Loss_D: 0.7650 Loss_G: 1.9485 D(x): 0.5894 D(G(z)): 0.0723 / 0.0723\n", + "[1118/1600][4/8] Loss_D: 0.7565 Loss_G: 1.9717 D(x): 0.6220 D(G(z)): 0.0710 / 0.0695\n", + "[1118/1600][6/8] Loss_D: 0.7605 Loss_G: 2.0023 D(x): 0.5916 D(G(z)): 0.0658 / 0.0648\n", + "[1119/1600][0/8] Loss_D: 0.7081 Loss_G: 1.9323 D(x): 0.6133 D(G(z)): 0.0692 / 0.0709\n", + "[1119/1600][2/8] Loss_D: 0.6804 Loss_G: 1.9466 D(x): 0.6302 D(G(z)): 0.0687 / 0.0705\n", + "[1119/1600][4/8] Loss_D: 0.7726 Loss_G: 1.9923 D(x): 0.5717 D(G(z)): 0.0669 / 0.0657\n", + "[1119/1600][6/8] Loss_D: 0.7743 Loss_G: 1.9621 D(x): 0.6040 D(G(z)): 0.0710 / 0.0695\n", + "[1120/1600][0/8] Loss_D: 0.7209 Loss_G: 1.9633 D(x): 0.6320 D(G(z)): 0.0673 / 0.0679\n", + "[1120/1600][2/8] Loss_D: 0.7471 Loss_G: 1.8776 D(x): 0.5909 D(G(z)): 0.0766 / 0.0772\n", + "[1120/1600][4/8] Loss_D: 0.7163 Loss_G: 1.9802 D(x): 0.6291 D(G(z)): 0.0674 / 0.0669\n", + "[1120/1600][6/8] Loss_D: 0.7402 Loss_G: 2.0017 D(x): 0.6091 D(G(z)): 0.0656 / 0.0646\n", + "[1121/1600][0/8] Loss_D: 0.7821 Loss_G: 2.0270 D(x): 0.5649 D(G(z)): 0.0645 / 0.0636\n", + "[1121/1600][2/8] Loss_D: 0.7571 Loss_G: 1.9713 D(x): 0.5751 D(G(z)): 0.0657 / 0.0669\n", + "[1121/1600][4/8] Loss_D: 0.7414 Loss_G: 1.9500 D(x): 0.6000 D(G(z)): 0.0698 / 0.0691\n", + "[1121/1600][6/8] Loss_D: 0.7042 Loss_G: 2.0029 D(x): 0.6353 D(G(z)): 0.0628 / 0.0638\n", + "[1122/1600][0/8] Loss_D: 0.6895 Loss_G: 1.9512 D(x): 0.6608 D(G(z)): 0.0682 / 0.0690\n", + "[1122/1600][2/8] Loss_D: 0.7075 Loss_G: 1.8872 D(x): 0.6516 D(G(z)): 0.0765 / 0.0780\n", + "[1122/1600][4/8] Loss_D: 0.7608 Loss_G: 1.8465 D(x): 0.5912 D(G(z)): 0.0830 / 0.0826\n", + "[1122/1600][6/8] Loss_D: 0.7385 Loss_G: 1.9459 D(x): 0.6528 D(G(z)): 0.0719 / 0.0699\n", + "[1123/1600][0/8] Loss_D: 0.7764 Loss_G: 1.9935 D(x): 0.6300 D(G(z)): 0.0703 / 0.0673\n", + "[1123/1600][2/8] Loss_D: 0.7431 Loss_G: 1.9717 D(x): 0.6465 D(G(z)): 0.0680 / 0.0670\n", + "[1123/1600][4/8] Loss_D: 0.7532 Loss_G: 2.0351 D(x): 0.6028 D(G(z)): 0.0641 / 0.0623\n", + "[1123/1600][6/8] Loss_D: 0.7189 Loss_G: 2.0111 D(x): 0.6117 D(G(z)): 0.0648 / 0.0650\n", + "[1124/1600][0/8] Loss_D: 0.7317 Loss_G: 2.0102 D(x): 0.6278 D(G(z)): 0.0652 / 0.0656\n", + "[1124/1600][2/8] Loss_D: 0.7736 Loss_G: 2.0056 D(x): 0.6346 D(G(z)): 0.0683 / 0.0652\n", + "[1124/1600][4/8] Loss_D: 0.7239 Loss_G: 2.0115 D(x): 0.6003 D(G(z)): 0.0654 / 0.0656\n", + "[1124/1600][6/8] Loss_D: 0.7240 Loss_G: 2.0360 D(x): 0.6097 D(G(z)): 0.0615 / 0.0620\n", + "[1125/1600][0/8] Loss_D: 0.6995 Loss_G: 1.9493 D(x): 0.6342 D(G(z)): 0.0683 / 0.0703\n", + "[1125/1600][2/8] Loss_D: 0.7148 Loss_G: 1.9313 D(x): 0.6005 D(G(z)): 0.0697 / 0.0713\n", + "[1125/1600][4/8] Loss_D: 0.7870 Loss_G: 1.8930 D(x): 0.6220 D(G(z)): 0.0784 / 0.0752\n", + "[1125/1600][6/8] Loss_D: 0.7191 Loss_G: 1.9390 D(x): 0.5899 D(G(z)): 0.0692 / 0.0712\n", + "[1126/1600][0/8] Loss_D: 0.7089 Loss_G: 1.9011 D(x): 0.6219 D(G(z)): 0.0721 / 0.0744\n", + "[1126/1600][2/8] Loss_D: 0.6992 Loss_G: 1.8378 D(x): 0.6171 D(G(z)): 0.0770 / 0.0800\n", + "[1126/1600][4/8] Loss_D: 0.7246 Loss_G: 1.8565 D(x): 0.6696 D(G(z)): 0.0830 / 0.0808\n", + "[1126/1600][6/8] Loss_D: 0.7107 Loss_G: 1.9146 D(x): 0.5990 D(G(z)): 0.0709 / 0.0726\n", + "[1127/1600][0/8] Loss_D: 0.7257 Loss_G: 1.9349 D(x): 0.6372 D(G(z)): 0.0719 / 0.0710\n", + "[1127/1600][2/8] Loss_D: 0.7255 Loss_G: 1.9613 D(x): 0.6648 D(G(z)): 0.0712 / 0.0689\n", + "[1127/1600][4/8] Loss_D: 0.7277 Loss_G: 1.8516 D(x): 0.6094 D(G(z)): 0.0803 / 0.0813\n", + "[1127/1600][6/8] Loss_D: 0.6856 Loss_G: 1.9706 D(x): 0.6186 D(G(z)): 0.0675 / 0.0695\n", + "[1128/1600][0/8] Loss_D: 0.6892 Loss_G: 1.9245 D(x): 0.6717 D(G(z)): 0.0734 / 0.0737\n", + "[1128/1600][2/8] Loss_D: 0.7455 Loss_G: 1.8430 D(x): 0.6238 D(G(z)): 0.0816 / 0.0813\n", + "[1128/1600][4/8] Loss_D: 0.7201 Loss_G: 1.8478 D(x): 0.6627 D(G(z)): 0.0828 / 0.0811\n", + "[1128/1600][6/8] Loss_D: 0.6824 Loss_G: 1.8741 D(x): 0.6611 D(G(z)): 0.0790 / 0.0783\n", + "[1129/1600][0/8] Loss_D: 0.7831 Loss_G: 1.9609 D(x): 0.6513 D(G(z)): 0.0723 / 0.0690\n", + "[1129/1600][2/8] Loss_D: 0.6851 Loss_G: 2.0693 D(x): 0.6099 D(G(z)): 0.0573 / 0.0579\n", + "[1129/1600][4/8] Loss_D: 0.6848 Loss_G: 2.0173 D(x): 0.6060 D(G(z)): 0.0603 / 0.0630\n", + "[1129/1600][6/8] Loss_D: 0.7790 Loss_G: 1.8889 D(x): 0.6136 D(G(z)): 0.0794 / 0.0769\n", + "[1130/1600][0/8] Loss_D: 0.7153 Loss_G: 1.9211 D(x): 0.6343 D(G(z)): 0.0736 / 0.0738\n", + "[1130/1600][2/8] Loss_D: 0.6887 Loss_G: 2.0060 D(x): 0.6555 D(G(z)): 0.0639 / 0.0636\n", + "[1130/1600][4/8] Loss_D: 0.6921 Loss_G: 1.9243 D(x): 0.6241 D(G(z)): 0.0705 / 0.0731\n", + "[1130/1600][6/8] Loss_D: 0.7293 Loss_G: 1.9267 D(x): 0.6155 D(G(z)): 0.0729 / 0.0725\n", + "[1131/1600][0/8] Loss_D: 0.7590 Loss_G: 1.9044 D(x): 0.5926 D(G(z)): 0.0758 / 0.0744\n", + "[1131/1600][2/8] Loss_D: 0.6840 Loss_G: 1.9786 D(x): 0.6447 D(G(z)): 0.0647 / 0.0657\n", + "[1131/1600][4/8] Loss_D: 0.7708 Loss_G: 1.9556 D(x): 0.5606 D(G(z)): 0.0688 / 0.0698\n", + "[1131/1600][6/8] Loss_D: 0.7731 Loss_G: 1.9226 D(x): 0.6498 D(G(z)): 0.0729 / 0.0717\n", + "[1132/1600][0/8] Loss_D: 0.7859 Loss_G: 1.9710 D(x): 0.6397 D(G(z)): 0.0721 / 0.0684\n", + "[1132/1600][2/8] Loss_D: 0.7706 Loss_G: 2.0732 D(x): 0.5891 D(G(z)): 0.0607 / 0.0586\n", + "[1132/1600][4/8] Loss_D: 0.7322 Loss_G: 2.0467 D(x): 0.6265 D(G(z)): 0.0632 / 0.0619\n", + "[1132/1600][6/8] Loss_D: 0.7245 Loss_G: 2.0664 D(x): 0.6199 D(G(z)): 0.0600 / 0.0594\n", + "[1133/1600][0/8] Loss_D: 0.7697 Loss_G: 2.0776 D(x): 0.5712 D(G(z)): 0.0584 / 0.0583\n", + "[1133/1600][2/8] Loss_D: 0.7465 Loss_G: 2.0450 D(x): 0.5748 D(G(z)): 0.0603 / 0.0612\n", + "[1133/1600][4/8] Loss_D: 0.6765 Loss_G: 1.9981 D(x): 0.6286 D(G(z)): 0.0632 / 0.0661\n", + "[1133/1600][6/8] Loss_D: 0.7384 Loss_G: 1.9771 D(x): 0.6326 D(G(z)): 0.0666 / 0.0675\n", + "[1134/1600][0/8] Loss_D: 0.7237 Loss_G: 1.9576 D(x): 0.6504 D(G(z)): 0.0689 / 0.0688\n", + "[1134/1600][2/8] Loss_D: 0.7353 Loss_G: 1.9129 D(x): 0.6630 D(G(z)): 0.0759 / 0.0740\n", + "[1134/1600][4/8] Loss_D: 0.6823 Loss_G: 1.9860 D(x): 0.6229 D(G(z)): 0.0645 / 0.0655\n", + "[1134/1600][6/8] Loss_D: 0.7514 Loss_G: 1.9723 D(x): 0.6159 D(G(z)): 0.0700 / 0.0691\n", + "[1135/1600][0/8] Loss_D: 0.7783 Loss_G: 1.9021 D(x): 0.6326 D(G(z)): 0.0765 / 0.0737\n", + "[1135/1600][2/8] Loss_D: 0.7038 Loss_G: 1.9881 D(x): 0.6354 D(G(z)): 0.0681 / 0.0682\n", + "[1135/1600][4/8] Loss_D: 0.7358 Loss_G: 1.9388 D(x): 0.6421 D(G(z)): 0.0703 / 0.0700\n", + "[1135/1600][6/8] Loss_D: 0.7036 Loss_G: 1.9006 D(x): 0.6321 D(G(z)): 0.0744 / 0.0747\n", + "[1136/1600][0/8] Loss_D: 0.7180 Loss_G: 1.8704 D(x): 0.6330 D(G(z)): 0.0789 / 0.0791\n", + "[1136/1600][2/8] Loss_D: 0.7170 Loss_G: 1.8401 D(x): 0.6159 D(G(z)): 0.0798 / 0.0816\n", + "[1136/1600][4/8] Loss_D: 0.7645 Loss_G: 1.9520 D(x): 0.6594 D(G(z)): 0.0708 / 0.0695\n", + "[1136/1600][6/8] Loss_D: 0.7721 Loss_G: 1.8710 D(x): 0.6379 D(G(z)): 0.0800 / 0.0773\n", + "[1137/1600][0/8] Loss_D: 0.7748 Loss_G: 1.9348 D(x): 0.6286 D(G(z)): 0.0743 / 0.0733\n", + "[1137/1600][2/8] Loss_D: 0.6875 Loss_G: 1.9185 D(x): 0.6265 D(G(z)): 0.0720 / 0.0734\n", + "[1137/1600][4/8] Loss_D: 0.7584 Loss_G: 1.9356 D(x): 0.6018 D(G(z)): 0.0724 / 0.0706\n", + "[1137/1600][6/8] Loss_D: 0.7630 Loss_G: 1.9867 D(x): 0.5931 D(G(z)): 0.0695 / 0.0664\n", + "[1138/1600][0/8] Loss_D: 0.7425 Loss_G: 2.0440 D(x): 0.6411 D(G(z)): 0.0642 / 0.0619\n", + "[1138/1600][2/8] Loss_D: 0.7375 Loss_G: 1.9858 D(x): 0.5605 D(G(z)): 0.0619 / 0.0652\n", + "[1138/1600][4/8] Loss_D: 0.7211 Loss_G: 1.8930 D(x): 0.5775 D(G(z)): 0.0712 / 0.0756\n", + "[1138/1600][6/8] Loss_D: 0.7851 Loss_G: 1.9498 D(x): 0.6700 D(G(z)): 0.0706 / 0.0696\n", + "[1139/1600][0/8] Loss_D: 0.7162 Loss_G: 1.9016 D(x): 0.6719 D(G(z)): 0.0768 / 0.0749\n", + "[1139/1600][2/8] Loss_D: 0.7868 Loss_G: 1.9142 D(x): 0.6246 D(G(z)): 0.0759 / 0.0717\n", + "[1139/1600][4/8] Loss_D: 0.7007 Loss_G: 1.9721 D(x): 0.6500 D(G(z)): 0.0687 / 0.0687\n", + "[1139/1600][6/8] Loss_D: 0.7410 Loss_G: 1.9666 D(x): 0.6233 D(G(z)): 0.0707 / 0.0690\n", + "[1140/1600][0/8] Loss_D: 0.7723 Loss_G: 1.8961 D(x): 0.6090 D(G(z)): 0.0771 / 0.0758\n", + "[1140/1600][2/8] Loss_D: 0.6691 Loss_G: 2.0097 D(x): 0.6602 D(G(z)): 0.0651 / 0.0662\n", + "[1140/1600][4/8] Loss_D: 0.6917 Loss_G: 1.9171 D(x): 0.6462 D(G(z)): 0.0721 / 0.0725\n", + "[1140/1600][6/8] Loss_D: 0.7456 Loss_G: 2.0629 D(x): 0.6034 D(G(z)): 0.0606 / 0.0597\n", + "[1141/1600][0/8] Loss_D: 0.7027 Loss_G: 2.0377 D(x): 0.6211 D(G(z)): 0.0617 / 0.0613\n", + "[1141/1600][2/8] Loss_D: 0.7565 Loss_G: 2.0006 D(x): 0.5776 D(G(z)): 0.0647 / 0.0643\n", + "[1141/1600][4/8] Loss_D: 0.7596 Loss_G: 2.0298 D(x): 0.6044 D(G(z)): 0.0647 / 0.0627\n", + "[1141/1600][6/8] Loss_D: 0.7325 Loss_G: 2.0542 D(x): 0.5596 D(G(z)): 0.0570 / 0.0586\n", + "[1142/1600][0/8] Loss_D: 0.7676 Loss_G: 2.0051 D(x): 0.6304 D(G(z)): 0.0653 / 0.0654\n", + "[1142/1600][2/8] Loss_D: 0.7415 Loss_G: 1.9120 D(x): 0.6108 D(G(z)): 0.0729 / 0.0736\n", + "[1142/1600][4/8] Loss_D: 0.7294 Loss_G: 1.9743 D(x): 0.6098 D(G(z)): 0.0673 / 0.0668\n", + "[1142/1600][6/8] Loss_D: 0.7652 Loss_G: 1.9765 D(x): 0.5833 D(G(z)): 0.0661 / 0.0663\n", + "[1143/1600][0/8] Loss_D: 0.7317 Loss_G: 1.9249 D(x): 0.5771 D(G(z)): 0.0700 / 0.0713\n", + "[1143/1600][2/8] Loss_D: 0.7718 Loss_G: 1.8864 D(x): 0.6612 D(G(z)): 0.0772 / 0.0747\n", + "[1143/1600][4/8] Loss_D: 0.6520 Loss_G: 1.9991 D(x): 0.6502 D(G(z)): 0.0640 / 0.0640\n", + "[1143/1600][6/8] Loss_D: 0.7077 Loss_G: 1.9812 D(x): 0.5985 D(G(z)): 0.0657 / 0.0669\n", + "[1144/1600][0/8] Loss_D: 0.7187 Loss_G: 1.9583 D(x): 0.5767 D(G(z)): 0.0654 / 0.0684\n", + "[1144/1600][2/8] Loss_D: 0.7374 Loss_G: 1.9414 D(x): 0.6454 D(G(z)): 0.0699 / 0.0707\n", + "[1144/1600][4/8] Loss_D: 0.7922 Loss_G: 1.8455 D(x): 0.6439 D(G(z)): 0.0819 / 0.0803\n", + "[1144/1600][6/8] Loss_D: 0.7005 Loss_G: 1.9119 D(x): 0.6818 D(G(z)): 0.0770 / 0.0746\n", + "[1145/1600][0/8] Loss_D: 0.7348 Loss_G: 1.9954 D(x): 0.5912 D(G(z)): 0.0672 / 0.0657\n", + "[1145/1600][2/8] Loss_D: 0.7380 Loss_G: 1.9432 D(x): 0.5882 D(G(z)): 0.0691 / 0.0705\n", + "[1145/1600][4/8] Loss_D: 0.7333 Loss_G: 1.9558 D(x): 0.6153 D(G(z)): 0.0680 / 0.0682\n", + "[1145/1600][6/8] Loss_D: 0.7745 Loss_G: 1.9296 D(x): 0.6037 D(G(z)): 0.0724 / 0.0711\n", + "[1146/1600][0/8] Loss_D: 0.7561 Loss_G: 1.9881 D(x): 0.6693 D(G(z)): 0.0684 / 0.0663\n", + "[1146/1600][2/8] Loss_D: 0.7523 Loss_G: 1.9331 D(x): 0.6165 D(G(z)): 0.0728 / 0.0711\n", + "[1146/1600][4/8] Loss_D: 0.7620 Loss_G: 2.0748 D(x): 0.5979 D(G(z)): 0.0598 / 0.0592\n", + "[1146/1600][6/8] Loss_D: 0.7483 Loss_G: 1.9468 D(x): 0.6090 D(G(z)): 0.0686 / 0.0690\n", + "[1147/1600][0/8] Loss_D: 0.6981 Loss_G: 1.8738 D(x): 0.6344 D(G(z)): 0.0755 / 0.0764\n", + "[1147/1600][2/8] Loss_D: 0.7088 Loss_G: 1.9306 D(x): 0.6429 D(G(z)): 0.0720 / 0.0722\n", + "[1147/1600][4/8] Loss_D: 0.7308 Loss_G: 1.8773 D(x): 0.6447 D(G(z)): 0.0774 / 0.0777\n", + "[1147/1600][6/8] Loss_D: 0.6719 Loss_G: 1.8503 D(x): 0.6405 D(G(z)): 0.0771 / 0.0795\n", + "[1148/1600][0/8] Loss_D: 0.7008 Loss_G: 1.8770 D(x): 0.6391 D(G(z)): 0.0766 / 0.0765\n", + "[1148/1600][2/8] Loss_D: 0.7667 Loss_G: 1.9527 D(x): 0.6519 D(G(z)): 0.0742 / 0.0692\n", + "[1148/1600][4/8] Loss_D: 0.7879 Loss_G: 1.9750 D(x): 0.5855 D(G(z)): 0.0695 / 0.0664\n", + "[1148/1600][6/8] Loss_D: 0.7472 Loss_G: 2.0148 D(x): 0.6083 D(G(z)): 0.0630 / 0.0622\n", + "[1149/1600][0/8] Loss_D: 0.7509 Loss_G: 1.9631 D(x): 0.6035 D(G(z)): 0.0672 / 0.0683\n", + "[1149/1600][2/8] Loss_D: 0.7127 Loss_G: 1.8920 D(x): 0.6306 D(G(z)): 0.0732 / 0.0750\n", + "[1149/1600][4/8] Loss_D: 0.7095 Loss_G: 1.9361 D(x): 0.5978 D(G(z)): 0.0674 / 0.0698\n", + "[1149/1600][6/8] Loss_D: 0.7798 Loss_G: 1.9707 D(x): 0.6499 D(G(z)): 0.0683 / 0.0662\n", + "[1150/1600][0/8] Loss_D: 0.7656 Loss_G: 1.8816 D(x): 0.5704 D(G(z)): 0.0753 / 0.0764\n", + "[1150/1600][2/8] Loss_D: 0.6978 Loss_G: 1.8511 D(x): 0.6535 D(G(z)): 0.0796 / 0.0800\n", + "[1150/1600][4/8] Loss_D: 0.7661 Loss_G: 1.8871 D(x): 0.6261 D(G(z)): 0.0799 / 0.0762\n", + "[1150/1600][6/8] Loss_D: 0.7290 Loss_G: 2.0175 D(x): 0.6513 D(G(z)): 0.0655 / 0.0628\n", + "[1151/1600][0/8] Loss_D: 0.7059 Loss_G: 1.9677 D(x): 0.6035 D(G(z)): 0.0657 / 0.0681\n", + "[1151/1600][2/8] Loss_D: 0.7858 Loss_G: 1.8665 D(x): 0.6015 D(G(z)): 0.0789 / 0.0791\n", + "[1151/1600][4/8] Loss_D: 0.7491 Loss_G: 1.8801 D(x): 0.6479 D(G(z)): 0.0780 / 0.0776\n", + "[1151/1600][6/8] Loss_D: 0.6617 Loss_G: 1.8489 D(x): 0.6725 D(G(z)): 0.0791 / 0.0794\n", + "[1152/1600][0/8] Loss_D: 0.7738 Loss_G: 1.8162 D(x): 0.5964 D(G(z)): 0.0856 / 0.0844\n", + "[1152/1600][2/8] Loss_D: 0.7273 Loss_G: 1.8333 D(x): 0.6339 D(G(z)): 0.0831 / 0.0823\n", + "[1152/1600][4/8] Loss_D: 0.7629 Loss_G: 1.8801 D(x): 0.6325 D(G(z)): 0.0794 / 0.0773\n", + "[1152/1600][6/8] Loss_D: 0.7532 Loss_G: 1.9115 D(x): 0.6277 D(G(z)): 0.0760 / 0.0742\n", + "[1153/1600][0/8] Loss_D: 0.7816 Loss_G: 1.9595 D(x): 0.6050 D(G(z)): 0.0695 / 0.0681\n", + "[1153/1600][2/8] Loss_D: 0.7483 Loss_G: 1.8366 D(x): 0.6612 D(G(z)): 0.0832 / 0.0814\n", + "[1153/1600][4/8] Loss_D: 0.7293 Loss_G: 1.9614 D(x): 0.6373 D(G(z)): 0.0688 / 0.0675\n", + "[1153/1600][6/8] Loss_D: 0.7138 Loss_G: 1.8385 D(x): 0.6299 D(G(z)): 0.0798 / 0.0814\n", + "[1154/1600][0/8] Loss_D: 0.7689 Loss_G: 1.8782 D(x): 0.6065 D(G(z)): 0.0795 / 0.0773\n", + "[1154/1600][2/8] Loss_D: 0.7099 Loss_G: 1.9463 D(x): 0.6339 D(G(z)): 0.0695 / 0.0684\n", + "[1154/1600][4/8] Loss_D: 0.7664 Loss_G: 1.8552 D(x): 0.6347 D(G(z)): 0.0826 / 0.0812\n", + "[1154/1600][6/8] Loss_D: 0.7794 Loss_G: 1.9271 D(x): 0.6225 D(G(z)): 0.0746 / 0.0722\n", + "[1155/1600][0/8] Loss_D: 0.7364 Loss_G: 1.9568 D(x): 0.6141 D(G(z)): 0.0684 / 0.0675\n", + "[1155/1600][2/8] Loss_D: 0.7513 Loss_G: 1.9552 D(x): 0.5877 D(G(z)): 0.0682 / 0.0684\n", + "[1155/1600][4/8] Loss_D: 0.7559 Loss_G: 1.9120 D(x): 0.6000 D(G(z)): 0.0735 / 0.0738\n", + "[1155/1600][6/8] Loss_D: 0.7221 Loss_G: 1.9104 D(x): 0.6157 D(G(z)): 0.0711 / 0.0738\n", + "[1156/1600][0/8] Loss_D: 0.7544 Loss_G: 1.8301 D(x): 0.6102 D(G(z)): 0.0836 / 0.0833\n", + "[1156/1600][2/8] Loss_D: 0.7204 Loss_G: 1.8800 D(x): 0.6557 D(G(z)): 0.0770 / 0.0766\n", + "[1156/1600][4/8] Loss_D: 0.7142 Loss_G: 1.8917 D(x): 0.6019 D(G(z)): 0.0742 / 0.0749\n", + "[1156/1600][6/8] Loss_D: 0.7508 Loss_G: 1.7745 D(x): 0.6086 D(G(z)): 0.0891 / 0.0900\n", + "[1157/1600][0/8] Loss_D: 0.7021 Loss_G: 1.7973 D(x): 0.6183 D(G(z)): 0.0864 / 0.0885\n", + "[1157/1600][2/8] Loss_D: 0.7000 Loss_G: 1.8612 D(x): 0.6602 D(G(z)): 0.0797 / 0.0781\n", + "[1157/1600][4/8] Loss_D: 0.7320 Loss_G: 1.7865 D(x): 0.6161 D(G(z)): 0.0877 / 0.0886\n", + "[1157/1600][6/8] Loss_D: 0.8042 Loss_G: 1.8106 D(x): 0.6826 D(G(z)): 0.0891 / 0.0843\n", + "[1158/1600][0/8] Loss_D: 0.6896 Loss_G: 1.8612 D(x): 0.6307 D(G(z)): 0.0792 / 0.0797\n", + "[1158/1600][2/8] Loss_D: 0.6808 Loss_G: 1.9275 D(x): 0.6562 D(G(z)): 0.0693 / 0.0707\n", + "[1158/1600][4/8] Loss_D: 0.7842 Loss_G: 1.8722 D(x): 0.6381 D(G(z)): 0.0820 / 0.0776\n", + "[1158/1600][6/8] Loss_D: 0.7633 Loss_G: 1.9133 D(x): 0.6029 D(G(z)): 0.0778 / 0.0754\n", + "[1159/1600][0/8] Loss_D: 0.7127 Loss_G: 1.9932 D(x): 0.6505 D(G(z)): 0.0655 / 0.0644\n", + "[1159/1600][2/8] Loss_D: 0.7307 Loss_G: 2.0128 D(x): 0.5926 D(G(z)): 0.0638 / 0.0635\n", + "[1159/1600][4/8] Loss_D: 0.7765 Loss_G: 1.8884 D(x): 0.6054 D(G(z)): 0.0776 / 0.0769\n", + "[1159/1600][6/8] Loss_D: 0.7152 Loss_G: 1.8557 D(x): 0.6047 D(G(z)): 0.0771 / 0.0793\n", + "[1160/1600][0/8] Loss_D: 0.7730 Loss_G: 1.8612 D(x): 0.5529 D(G(z)): 0.0784 / 0.0803\n", + "[1160/1600][2/8] Loss_D: 0.6663 Loss_G: 1.8441 D(x): 0.6778 D(G(z)): 0.0808 / 0.0812\n", + "[1160/1600][4/8] Loss_D: 0.7346 Loss_G: 1.8478 D(x): 0.6419 D(G(z)): 0.0807 / 0.0807\n", + "[1160/1600][6/8] Loss_D: 0.7723 Loss_G: 1.9343 D(x): 0.5911 D(G(z)): 0.0726 / 0.0704\n", + "[1161/1600][0/8] Loss_D: 0.6834 Loss_G: 1.9316 D(x): 0.6484 D(G(z)): 0.0700 / 0.0705\n", + "[1161/1600][2/8] Loss_D: 0.7772 Loss_G: 1.8807 D(x): 0.6285 D(G(z)): 0.0812 / 0.0773\n", + "[1161/1600][4/8] Loss_D: 0.7159 Loss_G: 1.9285 D(x): 0.6141 D(G(z)): 0.0700 / 0.0713\n", + "[1161/1600][6/8] Loss_D: 0.7796 Loss_G: 1.9234 D(x): 0.5811 D(G(z)): 0.0748 / 0.0729\n", + "[1162/1600][0/8] Loss_D: 0.7077 Loss_G: 1.9387 D(x): 0.6243 D(G(z)): 0.0707 / 0.0705\n", + "[1162/1600][2/8] Loss_D: 0.7299 Loss_G: 2.0844 D(x): 0.6459 D(G(z)): 0.0587 / 0.0574\n", + "[1162/1600][4/8] Loss_D: 0.7174 Loss_G: 2.0645 D(x): 0.5779 D(G(z)): 0.0579 / 0.0596\n", + "[1162/1600][6/8] Loss_D: 0.7129 Loss_G: 1.8743 D(x): 0.6152 D(G(z)): 0.0719 / 0.0759\n", + "[1163/1600][0/8] Loss_D: 0.7495 Loss_G: 1.8084 D(x): 0.6450 D(G(z)): 0.0874 / 0.0848\n", + "[1163/1600][2/8] Loss_D: 0.7841 Loss_G: 1.8896 D(x): 0.6084 D(G(z)): 0.0784 / 0.0761\n", + "[1163/1600][4/8] Loss_D: 0.7488 Loss_G: 1.9674 D(x): 0.6214 D(G(z)): 0.0691 / 0.0680\n", + "[1163/1600][6/8] Loss_D: 0.7742 Loss_G: 1.9303 D(x): 0.5570 D(G(z)): 0.0716 / 0.0712\n", + "[1164/1600][0/8] Loss_D: 0.6859 Loss_G: 1.8974 D(x): 0.6192 D(G(z)): 0.0707 / 0.0739\n", + "[1164/1600][2/8] Loss_D: 0.7196 Loss_G: 1.8490 D(x): 0.6262 D(G(z)): 0.0771 / 0.0796\n", + "[1164/1600][4/8] Loss_D: 0.7505 Loss_G: 1.8021 D(x): 0.6141 D(G(z)): 0.0841 / 0.0845\n", + "[1164/1600][6/8] Loss_D: 0.7178 Loss_G: 1.8565 D(x): 0.6295 D(G(z)): 0.0781 / 0.0772\n", + "[1165/1600][0/8] Loss_D: 0.6794 Loss_G: 1.8518 D(x): 0.6261 D(G(z)): 0.0777 / 0.0793\n", + "[1165/1600][2/8] Loss_D: 0.7005 Loss_G: 1.8741 D(x): 0.6051 D(G(z)): 0.0755 / 0.0773\n", + "[1165/1600][4/8] Loss_D: 0.6929 Loss_G: 1.8573 D(x): 0.6686 D(G(z)): 0.0781 / 0.0787\n", + "[1165/1600][6/8] Loss_D: 0.7167 Loss_G: 1.8491 D(x): 0.6645 D(G(z)): 0.0822 / 0.0808\n", + "[1166/1600][0/8] Loss_D: 0.7105 Loss_G: 1.8239 D(x): 0.6109 D(G(z)): 0.0807 / 0.0836\n", + "[1166/1600][2/8] Loss_D: 0.7173 Loss_G: 1.8433 D(x): 0.6116 D(G(z)): 0.0797 / 0.0811\n", + "[1166/1600][4/8] Loss_D: 0.7846 Loss_G: 1.8314 D(x): 0.6366 D(G(z)): 0.0855 / 0.0806\n", + "[1166/1600][6/8] Loss_D: 0.7197 Loss_G: 1.8101 D(x): 0.5927 D(G(z)): 0.0825 / 0.0838\n", + "[1167/1600][0/8] Loss_D: 0.7179 Loss_G: 1.8049 D(x): 0.6045 D(G(z)): 0.0813 / 0.0846\n", + "[1167/1600][2/8] Loss_D: 0.7809 Loss_G: 1.8458 D(x): 0.6910 D(G(z)): 0.0834 / 0.0790\n", + "[1167/1600][4/8] Loss_D: 0.7807 Loss_G: 1.8828 D(x): 0.5940 D(G(z)): 0.0799 / 0.0763\n", + "[1167/1600][6/8] Loss_D: 0.6579 Loss_G: 1.9098 D(x): 0.6494 D(G(z)): 0.0700 / 0.0719\n", + "[1168/1600][0/8] Loss_D: 0.7461 Loss_G: 1.8182 D(x): 0.6396 D(G(z)): 0.0850 / 0.0839\n", + "[1168/1600][2/8] Loss_D: 0.6842 Loss_G: 1.8342 D(x): 0.6276 D(G(z)): 0.0805 / 0.0818\n", + "[1168/1600][4/8] Loss_D: 0.7749 Loss_G: 1.8172 D(x): 0.6534 D(G(z)): 0.0848 / 0.0823\n", + "[1168/1600][6/8] Loss_D: 0.7800 Loss_G: 1.9020 D(x): 0.6386 D(G(z)): 0.0764 / 0.0729\n", + "[1169/1600][0/8] Loss_D: 0.7386 Loss_G: 1.9241 D(x): 0.6168 D(G(z)): 0.0717 / 0.0718\n", + "[1169/1600][2/8] Loss_D: 0.7042 Loss_G: 1.8715 D(x): 0.6438 D(G(z)): 0.0771 / 0.0782\n", + "[1169/1600][4/8] Loss_D: 0.7102 Loss_G: 1.7955 D(x): 0.6101 D(G(z)): 0.0852 / 0.0872\n", + "[1169/1600][6/8] Loss_D: 0.7513 Loss_G: 1.8918 D(x): 0.6677 D(G(z)): 0.0785 / 0.0752\n", + "[1170/1600][0/8] Loss_D: 0.7804 Loss_G: 1.9665 D(x): 0.5973 D(G(z)): 0.0706 / 0.0676\n", + "[1170/1600][2/8] Loss_D: 0.6876 Loss_G: 1.8940 D(x): 0.6210 D(G(z)): 0.0735 / 0.0751\n", + "[1170/1600][4/8] Loss_D: 0.6765 Loss_G: 1.7969 D(x): 0.6511 D(G(z)): 0.0846 / 0.0877\n", + "[1170/1600][6/8] Loss_D: 0.7317 Loss_G: 1.8778 D(x): 0.6068 D(G(z)): 0.0763 / 0.0756\n", + "[1171/1600][0/8] Loss_D: 0.7573 Loss_G: 1.7886 D(x): 0.5493 D(G(z)): 0.0823 / 0.0868\n", + "[1171/1600][2/8] Loss_D: 0.7921 Loss_G: 1.8691 D(x): 0.6600 D(G(z)): 0.0786 / 0.0773\n", + "[1171/1600][4/8] Loss_D: 0.7807 Loss_G: 1.8287 D(x): 0.5992 D(G(z)): 0.0862 / 0.0820\n", + "[1171/1600][6/8] Loss_D: 0.7364 Loss_G: 1.8350 D(x): 0.6060 D(G(z)): 0.0845 / 0.0842\n", + "[1172/1600][0/8] Loss_D: 0.7353 Loss_G: 1.8688 D(x): 0.6380 D(G(z)): 0.0791 / 0.0779\n", + "[1172/1600][2/8] Loss_D: 0.7438 Loss_G: 1.8179 D(x): 0.6088 D(G(z)): 0.0819 / 0.0835\n", + "[1172/1600][4/8] Loss_D: 0.8080 Loss_G: 1.8265 D(x): 0.6799 D(G(z)): 0.0891 / 0.0829\n", + "[1172/1600][6/8] Loss_D: 0.7842 Loss_G: 1.9335 D(x): 0.5367 D(G(z)): 0.0727 / 0.0702\n", + "[1173/1600][0/8] Loss_D: 0.7465 Loss_G: 1.9768 D(x): 0.5997 D(G(z)): 0.0656 / 0.0647\n", + "[1173/1600][2/8] Loss_D: 0.7732 Loss_G: 1.9631 D(x): 0.5766 D(G(z)): 0.0688 / 0.0689\n", + "[1173/1600][4/8] Loss_D: 0.7863 Loss_G: 1.9289 D(x): 0.5670 D(G(z)): 0.0708 / 0.0709\n", + "[1173/1600][6/8] Loss_D: 0.7257 Loss_G: 1.9102 D(x): 0.5755 D(G(z)): 0.0684 / 0.0723\n", + "[1174/1600][0/8] Loss_D: 0.7186 Loss_G: 1.8099 D(x): 0.6186 D(G(z)): 0.0818 / 0.0842\n", + "[1174/1600][2/8] Loss_D: 0.7012 Loss_G: 1.8016 D(x): 0.6509 D(G(z)): 0.0828 / 0.0835\n", + "[1174/1600][4/8] Loss_D: 0.7882 Loss_G: 1.8091 D(x): 0.5696 D(G(z)): 0.0852 / 0.0830\n", + "[1174/1600][6/8] Loss_D: 0.7315 Loss_G: 1.8718 D(x): 0.6178 D(G(z)): 0.0758 / 0.0755\n", + "[1175/1600][0/8] Loss_D: 0.6723 Loss_G: 1.8130 D(x): 0.6670 D(G(z)): 0.0841 / 0.0833\n", + "[1175/1600][2/8] Loss_D: 0.6839 Loss_G: 1.8830 D(x): 0.6223 D(G(z)): 0.0734 / 0.0756\n", + "[1175/1600][4/8] Loss_D: 0.7068 Loss_G: 1.8177 D(x): 0.6534 D(G(z)): 0.0829 / 0.0838\n", + "[1175/1600][6/8] Loss_D: 0.6952 Loss_G: 1.8564 D(x): 0.6715 D(G(z)): 0.0811 / 0.0790\n", + "[1176/1600][0/8] Loss_D: 0.7325 Loss_G: 1.8407 D(x): 0.6275 D(G(z)): 0.0794 / 0.0807\n", + "[1176/1600][2/8] Loss_D: 0.7625 Loss_G: 1.8353 D(x): 0.6540 D(G(z)): 0.0835 / 0.0798\n", + "[1176/1600][4/8] Loss_D: 0.7896 Loss_G: 1.8557 D(x): 0.6081 D(G(z)): 0.0818 / 0.0800\n", + "[1176/1600][6/8] Loss_D: 0.7736 Loss_G: 1.8785 D(x): 0.5890 D(G(z)): 0.0781 / 0.0770\n", + "[1177/1600][0/8] Loss_D: 0.7818 Loss_G: 1.7844 D(x): 0.5929 D(G(z)): 0.0912 / 0.0896\n", + "[1177/1600][2/8] Loss_D: 0.6877 Loss_G: 1.8154 D(x): 0.6178 D(G(z)): 0.0814 / 0.0832\n", + "[1177/1600][4/8] Loss_D: 0.7047 Loss_G: 1.8694 D(x): 0.6491 D(G(z)): 0.0767 / 0.0770\n", + "[1177/1600][6/8] Loss_D: 0.7025 Loss_G: 1.8232 D(x): 0.6289 D(G(z)): 0.0795 / 0.0819\n", + "[1178/1600][0/8] Loss_D: 0.7690 Loss_G: 1.8068 D(x): 0.6499 D(G(z)): 0.0860 / 0.0839\n", + "[1178/1600][2/8] Loss_D: 0.7908 Loss_G: 1.8267 D(x): 0.6257 D(G(z)): 0.0864 / 0.0825\n", + "[1178/1600][4/8] Loss_D: 0.7083 Loss_G: 1.7939 D(x): 0.6034 D(G(z)): 0.0835 / 0.0871\n", + "[1178/1600][6/8] Loss_D: 0.7357 Loss_G: 1.7531 D(x): 0.6413 D(G(z)): 0.0921 / 0.0927\n", + "[1179/1600][0/8] Loss_D: 0.6964 Loss_G: 1.6849 D(x): 0.6431 D(G(z)): 0.1006 / 0.1023\n", + "[1179/1600][2/8] Loss_D: 0.7961 Loss_G: 1.7772 D(x): 0.6214 D(G(z)): 0.0927 / 0.0901\n", + "[1179/1600][4/8] Loss_D: 0.7916 Loss_G: 1.7663 D(x): 0.6195 D(G(z)): 0.0972 / 0.0912\n", + "[1179/1600][6/8] Loss_D: 0.7591 Loss_G: 1.8646 D(x): 0.6424 D(G(z)): 0.0829 / 0.0788\n", + "[1180/1600][0/8] Loss_D: 0.7326 Loss_G: 2.0202 D(x): 0.5749 D(G(z)): 0.0604 / 0.0612\n", + "[1180/1600][2/8] Loss_D: 0.7717 Loss_G: 1.9256 D(x): 0.5760 D(G(z)): 0.0720 / 0.0710\n", + "[1180/1600][4/8] Loss_D: 0.7194 Loss_G: 1.9101 D(x): 0.6006 D(G(z)): 0.0735 / 0.0741\n", + "[1180/1600][6/8] Loss_D: 0.7844 Loss_G: 1.9077 D(x): 0.6543 D(G(z)): 0.0757 / 0.0734\n", + "[1181/1600][0/8] Loss_D: 0.7700 Loss_G: 2.0112 D(x): 0.6049 D(G(z)): 0.0669 / 0.0644\n", + "[1181/1600][2/8] Loss_D: 0.7046 Loss_G: 1.9792 D(x): 0.5939 D(G(z)): 0.0643 / 0.0666\n", + "[1181/1600][4/8] Loss_D: 0.7041 Loss_G: 1.8834 D(x): 0.5995 D(G(z)): 0.0718 / 0.0758\n", + "[1181/1600][6/8] Loss_D: 0.7463 Loss_G: 1.8760 D(x): 0.5912 D(G(z)): 0.0752 / 0.0767\n", + "[1182/1600][0/8] Loss_D: 0.7132 Loss_G: 1.8404 D(x): 0.6397 D(G(z)): 0.0801 / 0.0800\n", + "[1182/1600][2/8] Loss_D: 0.7699 Loss_G: 1.8777 D(x): 0.5784 D(G(z)): 0.0774 / 0.0765\n", + "[1182/1600][4/8] Loss_D: 0.7925 Loss_G: 1.8612 D(x): 0.6223 D(G(z)): 0.0804 / 0.0781\n", + "[1182/1600][6/8] Loss_D: 0.7414 Loss_G: 1.8998 D(x): 0.6032 D(G(z)): 0.0731 / 0.0747\n", + "[1183/1600][0/8] Loss_D: 0.6724 Loss_G: 1.8253 D(x): 0.6423 D(G(z)): 0.0809 / 0.0826\n", + "[1183/1600][2/8] Loss_D: 0.7667 Loss_G: 1.7877 D(x): 0.6154 D(G(z)): 0.0886 / 0.0878\n", + "[1183/1600][4/8] Loss_D: 0.7547 Loss_G: 1.8286 D(x): 0.5897 D(G(z)): 0.0831 / 0.0837\n", + "[1183/1600][6/8] Loss_D: 0.7689 Loss_G: 1.8379 D(x): 0.6963 D(G(z)): 0.0860 / 0.0817\n", + "[1184/1600][0/8] Loss_D: 0.7848 Loss_G: 1.9067 D(x): 0.6331 D(G(z)): 0.0788 / 0.0749\n", + "[1184/1600][2/8] Loss_D: 0.7288 Loss_G: 1.9030 D(x): 0.6336 D(G(z)): 0.0761 / 0.0747\n", + "[1184/1600][4/8] Loss_D: 0.7349 Loss_G: 1.8731 D(x): 0.5837 D(G(z)): 0.0759 / 0.0771\n", + "[1184/1600][6/8] Loss_D: 0.7236 Loss_G: 1.8414 D(x): 0.6139 D(G(z)): 0.0795 / 0.0799\n", + "[1185/1600][0/8] Loss_D: 0.7440 Loss_G: 1.8499 D(x): 0.6348 D(G(z)): 0.0799 / 0.0792\n", + "[1185/1600][2/8] Loss_D: 0.7184 Loss_G: 1.9724 D(x): 0.6315 D(G(z)): 0.0670 / 0.0662\n", + "[1185/1600][4/8] Loss_D: 0.6920 Loss_G: 1.8357 D(x): 0.6245 D(G(z)): 0.0786 / 0.0816\n", + "[1185/1600][6/8] Loss_D: 0.7505 Loss_G: 1.8808 D(x): 0.5751 D(G(z)): 0.0738 / 0.0755\n", + "[1186/1600][0/8] Loss_D: 0.7277 Loss_G: 1.8506 D(x): 0.6235 D(G(z)): 0.0782 / 0.0786\n", + "[1186/1600][2/8] Loss_D: 0.7336 Loss_G: 1.8985 D(x): 0.6522 D(G(z)): 0.0759 / 0.0742\n", + "[1186/1600][4/8] Loss_D: 0.7380 Loss_G: 1.8800 D(x): 0.6062 D(G(z)): 0.0791 / 0.0775\n", + "[1186/1600][6/8] Loss_D: 0.7396 Loss_G: 1.8436 D(x): 0.5833 D(G(z)): 0.0786 / 0.0805\n", + "[1187/1600][0/8] Loss_D: 0.7067 Loss_G: 1.8848 D(x): 0.6445 D(G(z)): 0.0746 / 0.0747\n", + "[1187/1600][2/8] Loss_D: 0.6715 Loss_G: 1.8547 D(x): 0.6604 D(G(z)): 0.0787 / 0.0793\n", + "[1187/1600][4/8] Loss_D: 0.7312 Loss_G: 1.8476 D(x): 0.5926 D(G(z)): 0.0788 / 0.0792\n", + "[1187/1600][6/8] Loss_D: 0.7535 Loss_G: 1.8351 D(x): 0.6145 D(G(z)): 0.0830 / 0.0836\n", + "[1188/1600][0/8] Loss_D: 0.7252 Loss_G: 1.7833 D(x): 0.5978 D(G(z)): 0.0847 / 0.0877\n", + "[1188/1600][2/8] Loss_D: 0.7676 Loss_G: 1.8417 D(x): 0.6441 D(G(z)): 0.0823 / 0.0804\n", + "[1188/1600][4/8] Loss_D: 0.7594 Loss_G: 1.8281 D(x): 0.6203 D(G(z)): 0.0852 / 0.0826\n", + "[1188/1600][6/8] Loss_D: 0.7349 Loss_G: 1.8858 D(x): 0.5749 D(G(z)): 0.0748 / 0.0753\n", + "[1189/1600][0/8] Loss_D: 0.7723 Loss_G: 1.8817 D(x): 0.5868 D(G(z)): 0.0750 / 0.0745\n", + "[1189/1600][2/8] Loss_D: 0.7690 Loss_G: 1.7545 D(x): 0.5945 D(G(z)): 0.0918 / 0.0923\n", + "[1189/1600][4/8] Loss_D: 0.7842 Loss_G: 1.9056 D(x): 0.6323 D(G(z)): 0.0796 / 0.0738\n", + "[1189/1600][6/8] Loss_D: 0.7730 Loss_G: 1.9808 D(x): 0.5839 D(G(z)): 0.0679 / 0.0657\n", + "[1190/1600][0/8] Loss_D: 0.7560 Loss_G: 1.8896 D(x): 0.5955 D(G(z)): 0.0748 / 0.0748\n", + "[1190/1600][2/8] Loss_D: 0.7681 Loss_G: 1.9634 D(x): 0.5848 D(G(z)): 0.0678 / 0.0667\n", + "[1190/1600][4/8] Loss_D: 0.7311 Loss_G: 1.8508 D(x): 0.5655 D(G(z)): 0.0734 / 0.0793\n", + "[1190/1600][6/8] Loss_D: 0.7081 Loss_G: 1.8941 D(x): 0.6151 D(G(z)): 0.0719 / 0.0743\n", + "[1191/1600][0/8] Loss_D: 0.7282 Loss_G: 1.6957 D(x): 0.6357 D(G(z)): 0.0981 / 0.1004\n", + "[1191/1600][2/8] Loss_D: 0.7458 Loss_G: 1.7917 D(x): 0.6274 D(G(z)): 0.0885 / 0.0861\n", + "[1191/1600][4/8] Loss_D: 0.7542 Loss_G: 1.8505 D(x): 0.5970 D(G(z)): 0.0833 / 0.0808\n", + "[1191/1600][6/8] Loss_D: 0.6952 Loss_G: 1.8911 D(x): 0.5986 D(G(z)): 0.0720 / 0.0743\n", + "[1192/1600][0/8] Loss_D: 0.7275 Loss_G: 1.7481 D(x): 0.6296 D(G(z)): 0.0906 / 0.0915\n", + "[1192/1600][2/8] Loss_D: 0.7334 Loss_G: 1.8235 D(x): 0.6118 D(G(z)): 0.0830 / 0.0825\n", + "[1192/1600][4/8] Loss_D: 0.7985 Loss_G: 1.8283 D(x): 0.6117 D(G(z)): 0.0839 / 0.0818\n", + "[1192/1600][6/8] Loss_D: 0.7374 Loss_G: 1.9720 D(x): 0.6229 D(G(z)): 0.0682 / 0.0660\n", + "[1193/1600][0/8] Loss_D: 0.7609 Loss_G: 1.9471 D(x): 0.5669 D(G(z)): 0.0708 / 0.0704\n", + "[1193/1600][2/8] Loss_D: 0.6929 Loss_G: 1.8566 D(x): 0.6170 D(G(z)): 0.0751 / 0.0778\n", + "[1193/1600][4/8] Loss_D: 0.7312 Loss_G: 1.9146 D(x): 0.6155 D(G(z)): 0.0729 / 0.0726\n", + "[1193/1600][6/8] Loss_D: 0.6879 Loss_G: 1.8712 D(x): 0.6224 D(G(z)): 0.0752 / 0.0775\n", + "[1194/1600][0/8] Loss_D: 0.7001 Loss_G: 1.8471 D(x): 0.6308 D(G(z)): 0.0793 / 0.0802\n", + "[1194/1600][2/8] Loss_D: 0.7795 Loss_G: 1.8739 D(x): 0.6113 D(G(z)): 0.0810 / 0.0777\n", + "[1194/1600][4/8] Loss_D: 0.7280 Loss_G: 1.8789 D(x): 0.6101 D(G(z)): 0.0757 / 0.0765\n", + "[1194/1600][6/8] Loss_D: 0.6996 Loss_G: 1.8579 D(x): 0.6098 D(G(z)): 0.0752 / 0.0773\n", + "[1195/1600][0/8] Loss_D: 0.7479 Loss_G: 1.8275 D(x): 0.6220 D(G(z)): 0.0818 / 0.0823\n", + "[1195/1600][2/8] Loss_D: 0.7016 Loss_G: 1.7527 D(x): 0.6160 D(G(z)): 0.0922 / 0.0940\n", + "[1195/1600][4/8] Loss_D: 0.7378 Loss_G: 1.7973 D(x): 0.6181 D(G(z)): 0.0869 / 0.0869\n", + "[1195/1600][6/8] Loss_D: 0.7623 Loss_G: 1.9080 D(x): 0.5883 D(G(z)): 0.0752 / 0.0739\n", + "[1196/1600][0/8] Loss_D: 0.7728 Loss_G: 1.9163 D(x): 0.5959 D(G(z)): 0.0749 / 0.0738\n", + "[1196/1600][2/8] Loss_D: 0.7143 Loss_G: 1.8501 D(x): 0.5994 D(G(z)): 0.0785 / 0.0793\n", + "[1196/1600][4/8] Loss_D: 0.6867 Loss_G: 1.8349 D(x): 0.6212 D(G(z)): 0.0779 / 0.0821\n", + "[1196/1600][6/8] Loss_D: 0.7569 Loss_G: 1.8329 D(x): 0.6047 D(G(z)): 0.0821 / 0.0810\n", + "[1197/1600][0/8] Loss_D: 0.7681 Loss_G: 1.8316 D(x): 0.6343 D(G(z)): 0.0853 / 0.0818\n", + "[1197/1600][2/8] Loss_D: 0.6902 Loss_G: 1.8438 D(x): 0.6165 D(G(z)): 0.0776 / 0.0806\n", + "[1197/1600][4/8] Loss_D: 0.7906 Loss_G: 1.8309 D(x): 0.6180 D(G(z)): 0.0837 / 0.0824\n", + "[1197/1600][6/8] Loss_D: 0.7645 Loss_G: 1.9181 D(x): 0.6224 D(G(z)): 0.0754 / 0.0727\n", + "[1198/1600][0/8] Loss_D: 0.7617 Loss_G: 1.9892 D(x): 0.6175 D(G(z)): 0.0676 / 0.0649\n", + "[1198/1600][2/8] Loss_D: 0.7570 Loss_G: 1.8363 D(x): 0.6288 D(G(z)): 0.0826 / 0.0822\n", + "[1198/1600][4/8] Loss_D: 0.7203 Loss_G: 1.8889 D(x): 0.6215 D(G(z)): 0.0764 / 0.0763\n", + "[1198/1600][6/8] Loss_D: 0.7331 Loss_G: 1.7979 D(x): 0.5866 D(G(z)): 0.0857 / 0.0889\n", + "[1199/1600][0/8] Loss_D: 0.7910 Loss_G: 1.8633 D(x): 0.6633 D(G(z)): 0.0844 / 0.0786\n", + "[1199/1600][2/8] Loss_D: 0.7585 Loss_G: 1.8982 D(x): 0.6029 D(G(z)): 0.0761 / 0.0750\n", + "[1199/1600][4/8] Loss_D: 0.7462 Loss_G: 1.9873 D(x): 0.5764 D(G(z)): 0.0640 / 0.0650\n", + "[1199/1600][6/8] Loss_D: 0.7239 Loss_G: 1.8479 D(x): 0.5781 D(G(z)): 0.0745 / 0.0788\n", + "[1200/1600][0/8] Loss_D: 0.6950 Loss_G: 1.7439 D(x): 0.6356 D(G(z)): 0.0889 / 0.0930\n", + "[1200/1600][2/8] Loss_D: 0.7960 Loss_G: 1.7009 D(x): 0.6248 D(G(z)): 0.1003 / 0.0983\n", + "[1200/1600][4/8] Loss_D: 0.7450 Loss_G: 1.7088 D(x): 0.6743 D(G(z)): 0.1018 / 0.0983\n", + "[1200/1600][6/8] Loss_D: 0.7828 Loss_G: 1.8152 D(x): 0.6385 D(G(z)): 0.0875 / 0.0841\n", + "[1201/1600][0/8] Loss_D: 0.8146 Loss_G: 1.8190 D(x): 0.6648 D(G(z)): 0.0893 / 0.0829\n", + "[1201/1600][2/8] Loss_D: 0.7380 Loss_G: 1.9558 D(x): 0.5869 D(G(z)): 0.0673 / 0.0679\n", + "[1201/1600][4/8] Loss_D: 0.7558 Loss_G: 1.8466 D(x): 0.6099 D(G(z)): 0.0807 / 0.0816\n", + "[1201/1600][6/8] Loss_D: 0.7335 Loss_G: 1.8042 D(x): 0.6063 D(G(z)): 0.0857 / 0.0846\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1202/1600][0/8] Loss_D: 0.7113 Loss_G: 1.8066 D(x): 0.6194 D(G(z)): 0.0847 / 0.0860\n", + "[1202/1600][2/8] Loss_D: 0.7730 Loss_G: 1.9024 D(x): 0.5643 D(G(z)): 0.0739 / 0.0742\n", + "[1202/1600][4/8] Loss_D: 0.7595 Loss_G: 1.8596 D(x): 0.6667 D(G(z)): 0.0817 / 0.0795\n", + "[1202/1600][6/8] Loss_D: 0.7894 Loss_G: 1.8148 D(x): 0.5860 D(G(z)): 0.0847 / 0.0828\n", + "[1203/1600][0/8] Loss_D: 0.7589 Loss_G: 1.9076 D(x): 0.5869 D(G(z)): 0.0737 / 0.0734\n", + "[1203/1600][2/8] Loss_D: 0.7279 Loss_G: 1.8755 D(x): 0.6325 D(G(z)): 0.0778 / 0.0781\n", + "[1203/1600][4/8] Loss_D: 0.7111 Loss_G: 1.7903 D(x): 0.6442 D(G(z)): 0.0867 / 0.0873\n", + "[1203/1600][6/8] Loss_D: 0.6824 Loss_G: 1.7407 D(x): 0.6493 D(G(z)): 0.0930 / 0.0954\n", + "[1204/1600][0/8] Loss_D: 0.6764 Loss_G: 1.7446 D(x): 0.6719 D(G(z)): 0.0923 / 0.0940\n", + "[1204/1600][2/8] Loss_D: 0.7789 Loss_G: 1.7278 D(x): 0.6121 D(G(z)): 0.0967 / 0.0956\n", + "[1204/1600][4/8] Loss_D: 0.7179 Loss_G: 1.8286 D(x): 0.6131 D(G(z)): 0.0818 / 0.0809\n", + "[1204/1600][6/8] Loss_D: 0.7586 Loss_G: 1.8327 D(x): 0.5890 D(G(z)): 0.0838 / 0.0818\n", + "[1205/1600][0/8] Loss_D: 0.7078 Loss_G: 1.8774 D(x): 0.5955 D(G(z)): 0.0753 / 0.0769\n", + "[1205/1600][2/8] Loss_D: 0.7390 Loss_G: 1.8257 D(x): 0.5993 D(G(z)): 0.0821 / 0.0824\n", + "[1205/1600][4/8] Loss_D: 0.7965 Loss_G: 1.8189 D(x): 0.6340 D(G(z)): 0.0879 / 0.0837\n", + "[1205/1600][6/8] Loss_D: 0.6898 Loss_G: 1.9075 D(x): 0.6248 D(G(z)): 0.0722 / 0.0740\n", + "[1206/1600][0/8] Loss_D: 0.7491 Loss_G: 1.8657 D(x): 0.6094 D(G(z)): 0.0779 / 0.0773\n", + "[1206/1600][2/8] Loss_D: 0.7114 Loss_G: 1.8494 D(x): 0.6400 D(G(z)): 0.0798 / 0.0808\n", + "[1206/1600][4/8] Loss_D: 0.6764 Loss_G: 1.7972 D(x): 0.6675 D(G(z)): 0.0850 / 0.0870\n", + "[1206/1600][6/8] Loss_D: 0.7108 Loss_G: 1.7562 D(x): 0.6423 D(G(z)): 0.0921 / 0.0933\n", + "[1207/1600][0/8] Loss_D: 0.7439 Loss_G: 1.8093 D(x): 0.6348 D(G(z)): 0.0857 / 0.0855\n", + "[1207/1600][2/8] Loss_D: 0.7091 Loss_G: 1.7890 D(x): 0.6421 D(G(z)): 0.0893 / 0.0880\n", + "[1207/1600][4/8] Loss_D: 0.7281 Loss_G: 1.6856 D(x): 0.6228 D(G(z)): 0.1013 / 0.1026\n", + "[1207/1600][6/8] Loss_D: 0.7020 Loss_G: 1.7701 D(x): 0.6316 D(G(z)): 0.0889 / 0.0897\n", + "[1208/1600][0/8] Loss_D: 0.7789 Loss_G: 1.7985 D(x): 0.6868 D(G(z)): 0.0915 / 0.0866\n", + "[1208/1600][2/8] Loss_D: 0.7923 Loss_G: 1.8605 D(x): 0.6132 D(G(z)): 0.0838 / 0.0788\n", + "[1208/1600][4/8] Loss_D: 0.7464 Loss_G: 1.8478 D(x): 0.6333 D(G(z)): 0.0809 / 0.0805\n", + "[1208/1600][6/8] Loss_D: 0.7740 Loss_G: 1.8689 D(x): 0.6049 D(G(z)): 0.0787 / 0.0769\n", + "[1209/1600][0/8] Loss_D: 0.7763 Loss_G: 1.9341 D(x): 0.6377 D(G(z)): 0.0762 / 0.0708\n", + "[1209/1600][2/8] Loss_D: 0.7247 Loss_G: 2.0104 D(x): 0.6010 D(G(z)): 0.0631 / 0.0627\n", + "[1209/1600][4/8] Loss_D: 0.7274 Loss_G: 1.9933 D(x): 0.6052 D(G(z)): 0.0638 / 0.0657\n", + "[1209/1600][6/8] Loss_D: 0.7036 Loss_G: 1.8765 D(x): 0.6065 D(G(z)): 0.0733 / 0.0775\n", + "[1210/1600][0/8] Loss_D: 0.7855 Loss_G: 1.7933 D(x): 0.5842 D(G(z)): 0.0839 / 0.0852\n", + "[1210/1600][2/8] Loss_D: 0.7133 Loss_G: 1.8229 D(x): 0.6420 D(G(z)): 0.0847 / 0.0834\n", + "[1210/1600][4/8] Loss_D: 0.7788 Loss_G: 1.9119 D(x): 0.5873 D(G(z)): 0.0757 / 0.0722\n", + "[1210/1600][6/8] Loss_D: 0.7206 Loss_G: 1.8604 D(x): 0.5988 D(G(z)): 0.0771 / 0.0789\n", + "[1211/1600][0/8] Loss_D: 0.7865 Loss_G: 1.8764 D(x): 0.5902 D(G(z)): 0.0776 / 0.0754\n", + "[1211/1600][2/8] Loss_D: 0.7555 Loss_G: 1.8677 D(x): 0.6086 D(G(z)): 0.0771 / 0.0767\n", + "[1211/1600][4/8] Loss_D: 0.7070 Loss_G: 1.7680 D(x): 0.6464 D(G(z)): 0.0858 / 0.0881\n", + "[1211/1600][6/8] Loss_D: 0.7555 Loss_G: 1.9113 D(x): 0.6263 D(G(z)): 0.0764 / 0.0744\n", + "[1212/1600][0/8] Loss_D: 0.6909 Loss_G: 1.8776 D(x): 0.6479 D(G(z)): 0.0747 / 0.0759\n", + "[1212/1600][2/8] Loss_D: 0.7330 Loss_G: 1.8562 D(x): 0.6053 D(G(z)): 0.0792 / 0.0790\n", + "[1212/1600][4/8] Loss_D: 0.7295 Loss_G: 1.7501 D(x): 0.5971 D(G(z)): 0.0884 / 0.0923\n", + "[1212/1600][6/8] Loss_D: 0.7677 Loss_G: 1.7746 D(x): 0.5934 D(G(z)): 0.0901 / 0.0898\n", + "[1213/1600][0/8] Loss_D: 0.7874 Loss_G: 1.7891 D(x): 0.6239 D(G(z)): 0.0912 / 0.0877\n", + "[1213/1600][2/8] Loss_D: 0.7101 Loss_G: 1.8071 D(x): 0.6260 D(G(z)): 0.0826 / 0.0845\n", + "[1213/1600][4/8] Loss_D: 0.7198 Loss_G: 1.7284 D(x): 0.6563 D(G(z)): 0.0969 / 0.0958\n", + "[1213/1600][6/8] Loss_D: 0.6856 Loss_G: 1.8536 D(x): 0.6766 D(G(z)): 0.0815 / 0.0801\n", + "[1214/1600][0/8] Loss_D: 0.7223 Loss_G: 1.8957 D(x): 0.6556 D(G(z)): 0.0796 / 0.0761\n", + "[1214/1600][2/8] Loss_D: 0.7344 Loss_G: 1.9411 D(x): 0.5955 D(G(z)): 0.0706 / 0.0697\n", + "[1214/1600][4/8] Loss_D: 0.7696 Loss_G: 1.8363 D(x): 0.6098 D(G(z)): 0.0814 / 0.0803\n", + "[1214/1600][6/8] Loss_D: 0.7215 Loss_G: 1.8985 D(x): 0.5706 D(G(z)): 0.0720 / 0.0751\n", + "[1215/1600][0/8] Loss_D: 0.7264 Loss_G: 1.9373 D(x): 0.6243 D(G(z)): 0.0684 / 0.0702\n", + "[1215/1600][2/8] Loss_D: 0.7477 Loss_G: 1.9015 D(x): 0.6131 D(G(z)): 0.0765 / 0.0745\n", + "[1215/1600][4/8] Loss_D: 0.7554 Loss_G: 1.9175 D(x): 0.6086 D(G(z)): 0.0716 / 0.0715\n", + "[1215/1600][6/8] Loss_D: 0.7461 Loss_G: 1.8176 D(x): 0.6359 D(G(z)): 0.0844 / 0.0827\n", + "[1216/1600][0/8] Loss_D: 0.7604 Loss_G: 1.8450 D(x): 0.5635 D(G(z)): 0.0771 / 0.0799\n", + "[1216/1600][2/8] Loss_D: 0.7694 Loss_G: 1.9065 D(x): 0.6582 D(G(z)): 0.0788 / 0.0749\n", + "[1216/1600][4/8] Loss_D: 0.7706 Loss_G: 1.8603 D(x): 0.6216 D(G(z)): 0.0807 / 0.0783\n", + "[1216/1600][6/8] Loss_D: 0.7738 Loss_G: 1.8525 D(x): 0.5617 D(G(z)): 0.0787 / 0.0795\n", + "[1217/1600][0/8] Loss_D: 0.7808 Loss_G: 1.9320 D(x): 0.6301 D(G(z)): 0.0751 / 0.0728\n", + "[1217/1600][2/8] Loss_D: 0.7609 Loss_G: 1.9986 D(x): 0.5592 D(G(z)): 0.0640 / 0.0644\n", + "[1217/1600][4/8] Loss_D: 0.7126 Loss_G: 1.8227 D(x): 0.6030 D(G(z)): 0.0818 / 0.0850\n", + "[1217/1600][6/8] Loss_D: 0.7360 Loss_G: 1.8999 D(x): 0.6637 D(G(z)): 0.0740 / 0.0735\n", + "[1218/1600][0/8] Loss_D: 0.7782 Loss_G: 1.8859 D(x): 0.6104 D(G(z)): 0.0821 / 0.0776\n", + "[1218/1600][2/8] Loss_D: 0.7689 Loss_G: 1.9527 D(x): 0.5927 D(G(z)): 0.0723 / 0.0696\n", + "[1218/1600][4/8] Loss_D: 0.7583 Loss_G: 1.9560 D(x): 0.5741 D(G(z)): 0.0693 / 0.0686\n", + "[1218/1600][6/8] Loss_D: 0.7637 Loss_G: 1.9468 D(x): 0.5875 D(G(z)): 0.0688 / 0.0691\n", + "[1219/1600][0/8] Loss_D: 0.7423 Loss_G: 1.9218 D(x): 0.5835 D(G(z)): 0.0690 / 0.0720\n", + "[1219/1600][2/8] Loss_D: 0.7041 Loss_G: 1.7922 D(x): 0.6308 D(G(z)): 0.0821 / 0.0858\n", + "[1219/1600][4/8] Loss_D: 0.6843 Loss_G: 1.8065 D(x): 0.6556 D(G(z)): 0.0836 / 0.0851\n", + "[1219/1600][6/8] Loss_D: 0.7849 Loss_G: 1.8161 D(x): 0.6378 D(G(z)): 0.0889 / 0.0847\n", + "[1220/1600][0/8] Loss_D: 0.7603 Loss_G: 1.8301 D(x): 0.6912 D(G(z)): 0.0854 / 0.0814\n", + "[1220/1600][2/8] Loss_D: 0.7113 Loss_G: 1.8450 D(x): 0.5924 D(G(z)): 0.0773 / 0.0811\n", + "[1220/1600][4/8] Loss_D: 0.6671 Loss_G: 1.7845 D(x): 0.6490 D(G(z)): 0.0871 / 0.0891\n", + "[1220/1600][6/8] Loss_D: 0.7410 Loss_G: 1.7426 D(x): 0.5842 D(G(z)): 0.0907 / 0.0940\n", + "[1221/1600][0/8] Loss_D: 0.6903 Loss_G: 1.7811 D(x): 0.6432 D(G(z)): 0.0896 / 0.0899\n", + "[1221/1600][2/8] Loss_D: 0.7070 Loss_G: 1.7697 D(x): 0.6674 D(G(z)): 0.0907 / 0.0893\n", + "[1221/1600][4/8] Loss_D: 0.6802 Loss_G: 1.7522 D(x): 0.6681 D(G(z)): 0.0932 / 0.0926\n", + "[1221/1600][6/8] Loss_D: 0.7629 Loss_G: 1.7949 D(x): 0.5978 D(G(z)): 0.0856 / 0.0853\n", + "[1222/1600][0/8] Loss_D: 0.7260 Loss_G: 1.9129 D(x): 0.6507 D(G(z)): 0.0742 / 0.0709\n", + "[1222/1600][2/8] Loss_D: 0.6788 Loss_G: 1.8605 D(x): 0.6409 D(G(z)): 0.0782 / 0.0785\n", + "[1222/1600][4/8] Loss_D: 0.7215 Loss_G: 1.8333 D(x): 0.5808 D(G(z)): 0.0802 / 0.0833\n", + "[1222/1600][6/8] Loss_D: 0.7069 Loss_G: 1.8515 D(x): 0.6051 D(G(z)): 0.0756 / 0.0798\n", + "[1223/1600][0/8] Loss_D: 0.6791 Loss_G: 1.7933 D(x): 0.6553 D(G(z)): 0.0848 / 0.0859\n", + "[1223/1600][2/8] Loss_D: 0.7138 Loss_G: 1.7215 D(x): 0.6857 D(G(z)): 0.0980 / 0.0964\n", + "[1223/1600][4/8] Loss_D: 0.7624 Loss_G: 1.8052 D(x): 0.6401 D(G(z)): 0.0886 / 0.0864\n", + "[1223/1600][6/8] Loss_D: 0.7654 Loss_G: 1.8310 D(x): 0.6400 D(G(z)): 0.0859 / 0.0823\n", + "[1224/1600][0/8] Loss_D: 0.7812 Loss_G: 1.8900 D(x): 0.6076 D(G(z)): 0.0782 / 0.0750\n", + "[1224/1600][2/8] Loss_D: 0.6883 Loss_G: 1.9061 D(x): 0.6294 D(G(z)): 0.0732 / 0.0742\n", + "[1224/1600][4/8] Loss_D: 0.7315 Loss_G: 1.7849 D(x): 0.6345 D(G(z)): 0.0881 / 0.0888\n", + "[1224/1600][6/8] Loss_D: 0.7151 Loss_G: 1.7881 D(x): 0.6292 D(G(z)): 0.0867 / 0.0875\n", + "[1225/1600][0/8] Loss_D: 0.7835 Loss_G: 1.8340 D(x): 0.6099 D(G(z)): 0.0841 / 0.0823\n", + "[1225/1600][2/8] Loss_D: 0.7273 Loss_G: 1.8661 D(x): 0.6218 D(G(z)): 0.0789 / 0.0793\n", + "[1225/1600][4/8] Loss_D: 0.7782 Loss_G: 1.8444 D(x): 0.5995 D(G(z)): 0.0819 / 0.0806\n", + "[1225/1600][6/8] Loss_D: 0.6782 Loss_G: 1.8429 D(x): 0.6281 D(G(z)): 0.0795 / 0.0805\n", + "[1226/1600][0/8] Loss_D: 0.7628 Loss_G: 1.8683 D(x): 0.6325 D(G(z)): 0.0812 / 0.0792\n", + "[1226/1600][2/8] Loss_D: 0.6839 Loss_G: 1.8365 D(x): 0.6326 D(G(z)): 0.0799 / 0.0821\n", + "[1226/1600][4/8] Loss_D: 0.7206 Loss_G: 1.7855 D(x): 0.6321 D(G(z)): 0.0854 / 0.0874\n", + "[1226/1600][6/8] Loss_D: 0.8010 Loss_G: 1.8495 D(x): 0.6393 D(G(z)): 0.0861 / 0.0800\n", + "[1227/1600][0/8] Loss_D: 0.7583 Loss_G: 1.8004 D(x): 0.5900 D(G(z)): 0.0845 / 0.0848\n", + "[1227/1600][2/8] Loss_D: 0.6824 Loss_G: 1.8266 D(x): 0.6871 D(G(z)): 0.0825 / 0.0829\n", + "[1227/1600][4/8] Loss_D: 0.7803 Loss_G: 1.8298 D(x): 0.5972 D(G(z)): 0.0859 / 0.0825\n", + "[1227/1600][6/8] Loss_D: 0.7412 Loss_G: 1.8754 D(x): 0.5807 D(G(z)): 0.0765 / 0.0775\n", + "[1228/1600][0/8] Loss_D: 0.7044 Loss_G: 1.8417 D(x): 0.6105 D(G(z)): 0.0783 / 0.0809\n", + "[1228/1600][2/8] Loss_D: 0.7806 Loss_G: 1.8252 D(x): 0.5999 D(G(z)): 0.0854 / 0.0838\n", + "[1228/1600][4/8] Loss_D: 0.7596 Loss_G: 1.9426 D(x): 0.6411 D(G(z)): 0.0733 / 0.0706\n", + "[1228/1600][6/8] Loss_D: 0.7240 Loss_G: 1.9076 D(x): 0.5984 D(G(z)): 0.0708 / 0.0722\n", + "[1229/1600][0/8] Loss_D: 0.7315 Loss_G: 1.8763 D(x): 0.6259 D(G(z)): 0.0779 / 0.0773\n", + "[1229/1600][2/8] Loss_D: 0.7206 Loss_G: 1.8881 D(x): 0.5978 D(G(z)): 0.0742 / 0.0753\n", + "[1229/1600][4/8] Loss_D: 0.7717 Loss_G: 1.9053 D(x): 0.5415 D(G(z)): 0.0710 / 0.0733\n", + "[1229/1600][6/8] Loss_D: 0.6877 Loss_G: 1.6549 D(x): 0.6592 D(G(z)): 0.1046 / 0.1076\n", + "[1230/1600][0/8] Loss_D: 0.7647 Loss_G: 1.8405 D(x): 0.5612 D(G(z)): 0.0804 / 0.0809\n", + "[1230/1600][2/8] Loss_D: 0.7711 Loss_G: 1.8912 D(x): 0.6555 D(G(z)): 0.0783 / 0.0754\n", + "[1230/1600][4/8] Loss_D: 0.7061 Loss_G: 1.9180 D(x): 0.6405 D(G(z)): 0.0729 / 0.0728\n", + "[1230/1600][6/8] Loss_D: 0.7346 Loss_G: 1.8498 D(x): 0.5963 D(G(z)): 0.0801 / 0.0804\n", + "[1231/1600][0/8] Loss_D: 0.6922 Loss_G: 1.8699 D(x): 0.6316 D(G(z)): 0.0774 / 0.0777\n", + "[1231/1600][2/8] Loss_D: 0.7705 Loss_G: 1.9098 D(x): 0.6040 D(G(z)): 0.0747 / 0.0736\n", + "[1231/1600][4/8] Loss_D: 0.6803 Loss_G: 1.9016 D(x): 0.6457 D(G(z)): 0.0730 / 0.0732\n", + "[1231/1600][6/8] Loss_D: 0.7739 Loss_G: 1.9472 D(x): 0.6169 D(G(z)): 0.0742 / 0.0708\n", + "[1232/1600][0/8] Loss_D: 0.7344 Loss_G: 1.8825 D(x): 0.5908 D(G(z)): 0.0723 / 0.0755\n", + "[1232/1600][2/8] Loss_D: 0.7395 Loss_G: 1.8691 D(x): 0.5860 D(G(z)): 0.0756 / 0.0777\n", + "[1232/1600][4/8] Loss_D: 0.7009 Loss_G: 1.8275 D(x): 0.6082 D(G(z)): 0.0822 / 0.0849\n", + "[1232/1600][6/8] Loss_D: 0.7941 Loss_G: 1.8421 D(x): 0.6390 D(G(z)): 0.0861 / 0.0816\n", + "[1233/1600][0/8] Loss_D: 0.7486 Loss_G: 1.8659 D(x): 0.6027 D(G(z)): 0.0806 / 0.0794\n", + "[1233/1600][2/8] Loss_D: 0.7783 Loss_G: 1.9414 D(x): 0.6362 D(G(z)): 0.0741 / 0.0704\n", + "[1233/1600][4/8] Loss_D: 0.7456 Loss_G: 1.9024 D(x): 0.5781 D(G(z)): 0.0704 / 0.0732\n", + "[1233/1600][6/8] Loss_D: 0.7526 Loss_G: 1.9054 D(x): 0.6249 D(G(z)): 0.0759 / 0.0744\n", + "[1234/1600][0/8] Loss_D: 0.7531 Loss_G: 1.8952 D(x): 0.5594 D(G(z)): 0.0733 / 0.0760\n", + "[1234/1600][2/8] Loss_D: 0.7816 Loss_G: 1.8561 D(x): 0.5436 D(G(z)): 0.0770 / 0.0794\n", + "[1234/1600][4/8] Loss_D: 0.7105 Loss_G: 1.9108 D(x): 0.6674 D(G(z)): 0.0735 / 0.0728\n", + "[1234/1600][6/8] Loss_D: 0.7431 Loss_G: 1.8491 D(x): 0.6578 D(G(z)): 0.0828 / 0.0807\n", + "[1235/1600][0/8] Loss_D: 0.7186 Loss_G: 1.8508 D(x): 0.5860 D(G(z)): 0.0774 / 0.0800\n", + "[1235/1600][2/8] Loss_D: 0.7061 Loss_G: 1.7492 D(x): 0.6156 D(G(z)): 0.0904 / 0.0951\n", + "[1235/1600][4/8] Loss_D: 0.7996 Loss_G: 1.8135 D(x): 0.6655 D(G(z)): 0.0862 / 0.0831\n", + "[1235/1600][6/8] Loss_D: 0.7895 Loss_G: 1.7831 D(x): 0.6734 D(G(z)): 0.0939 / 0.0886\n", + "[1236/1600][0/8] Loss_D: 0.7019 Loss_G: 1.8024 D(x): 0.6641 D(G(z)): 0.0878 / 0.0859\n", + "[1236/1600][2/8] Loss_D: 0.7741 Loss_G: 1.8333 D(x): 0.5794 D(G(z)): 0.0818 / 0.0822\n", + "[1236/1600][4/8] Loss_D: 0.7066 Loss_G: 1.8188 D(x): 0.6406 D(G(z)): 0.0866 / 0.0862\n", + "[1236/1600][6/8] Loss_D: 0.7739 Loss_G: 1.9433 D(x): 0.5964 D(G(z)): 0.0714 / 0.0699\n", + "[1237/1600][0/8] Loss_D: 0.7536 Loss_G: 1.8702 D(x): 0.5946 D(G(z)): 0.0780 / 0.0779\n", + "[1237/1600][2/8] Loss_D: 0.7797 Loss_G: 1.8909 D(x): 0.6097 D(G(z)): 0.0793 / 0.0753\n", + "[1237/1600][4/8] Loss_D: 0.7297 Loss_G: 1.9223 D(x): 0.6112 D(G(z)): 0.0726 / 0.0721\n", + "[1237/1600][6/8] Loss_D: 0.7247 Loss_G: 1.8938 D(x): 0.5755 D(G(z)): 0.0743 / 0.0773\n", + "[1238/1600][0/8] Loss_D: 0.7545 Loss_G: 1.8903 D(x): 0.5835 D(G(z)): 0.0757 / 0.0764\n", + "[1238/1600][2/8] Loss_D: 0.7049 Loss_G: 1.8778 D(x): 0.6275 D(G(z)): 0.0750 / 0.0763\n", + "[1238/1600][4/8] Loss_D: 0.7074 Loss_G: 1.8090 D(x): 0.6058 D(G(z)): 0.0806 / 0.0853\n", + "[1238/1600][6/8] Loss_D: 0.7732 Loss_G: 1.8418 D(x): 0.5842 D(G(z)): 0.0818 / 0.0812\n", + "[1239/1600][0/8] Loss_D: 0.7189 Loss_G: 1.8113 D(x): 0.6700 D(G(z)): 0.0860 / 0.0840\n", + "[1239/1600][2/8] Loss_D: 0.7170 Loss_G: 1.8227 D(x): 0.5953 D(G(z)): 0.0830 / 0.0842\n", + "[1239/1600][4/8] Loss_D: 0.7208 Loss_G: 1.7968 D(x): 0.6113 D(G(z)): 0.0852 / 0.0874\n", + "[1239/1600][6/8] Loss_D: 0.7439 Loss_G: 1.8089 D(x): 0.6442 D(G(z)): 0.0860 / 0.0833\n", + "[1240/1600][0/8] Loss_D: 0.7441 Loss_G: 1.8289 D(x): 0.6369 D(G(z)): 0.0827 / 0.0822\n", + "[1240/1600][2/8] Loss_D: 0.6884 Loss_G: 1.7719 D(x): 0.6396 D(G(z)): 0.0895 / 0.0903\n", + "[1240/1600][4/8] Loss_D: 0.7292 Loss_G: 1.8387 D(x): 0.6465 D(G(z)): 0.0829 / 0.0831\n", + "[1240/1600][6/8] Loss_D: 0.7360 Loss_G: 1.8515 D(x): 0.6336 D(G(z)): 0.0834 / 0.0812\n", + "[1241/1600][0/8] Loss_D: 0.6804 Loss_G: 1.8284 D(x): 0.6288 D(G(z)): 0.0813 / 0.0828\n", + "[1241/1600][2/8] Loss_D: 0.7285 Loss_G: 1.8772 D(x): 0.5918 D(G(z)): 0.0744 / 0.0771\n", + "[1241/1600][4/8] Loss_D: 0.7990 Loss_G: 1.8418 D(x): 0.6398 D(G(z)): 0.0870 / 0.0814\n", + "[1241/1600][6/8] Loss_D: 0.7792 Loss_G: 1.9140 D(x): 0.6121 D(G(z)): 0.0777 / 0.0736\n", + "[1242/1600][0/8] Loss_D: 0.7802 Loss_G: 1.9534 D(x): 0.5863 D(G(z)): 0.0725 / 0.0699\n", + "[1242/1600][2/8] Loss_D: 0.7170 Loss_G: 1.8696 D(x): 0.6046 D(G(z)): 0.0738 / 0.0769\n", + "[1242/1600][4/8] Loss_D: 0.7224 Loss_G: 1.8294 D(x): 0.6113 D(G(z)): 0.0822 / 0.0842\n", + "[1242/1600][6/8] Loss_D: 0.7522 Loss_G: 1.8130 D(x): 0.5759 D(G(z)): 0.0840 / 0.0856\n", + "[1243/1600][0/8] Loss_D: 0.7536 Loss_G: 1.8573 D(x): 0.6072 D(G(z)): 0.0791 / 0.0798\n", + "[1243/1600][2/8] Loss_D: 0.7869 Loss_G: 1.9026 D(x): 0.5845 D(G(z)): 0.0778 / 0.0755\n", + "[1243/1600][4/8] Loss_D: 0.6861 Loss_G: 1.7966 D(x): 0.6428 D(G(z)): 0.0863 / 0.0870\n", + "[1243/1600][6/8] Loss_D: 0.7230 Loss_G: 1.8101 D(x): 0.6447 D(G(z)): 0.0851 / 0.0859\n", + "[1244/1600][0/8] Loss_D: 0.7025 Loss_G: 1.8606 D(x): 0.6926 D(G(z)): 0.0785 / 0.0776\n", + "[1244/1600][2/8] Loss_D: 0.7351 Loss_G: 1.8480 D(x): 0.6063 D(G(z)): 0.0819 / 0.0812\n", + "[1244/1600][4/8] Loss_D: 0.7436 Loss_G: 1.9705 D(x): 0.6502 D(G(z)): 0.0701 / 0.0670\n", + "[1244/1600][6/8] Loss_D: 0.7703 Loss_G: 1.9361 D(x): 0.5868 D(G(z)): 0.0714 / 0.0715\n", + "[1245/1600][0/8] Loss_D: 0.7711 Loss_G: 2.0054 D(x): 0.6266 D(G(z)): 0.0674 / 0.0638\n", + "[1245/1600][2/8] Loss_D: 0.7205 Loss_G: 1.8993 D(x): 0.5819 D(G(z)): 0.0727 / 0.0752\n", + "[1245/1600][4/8] Loss_D: 0.7748 Loss_G: 1.8459 D(x): 0.5388 D(G(z)): 0.0760 / 0.0809\n", + "[1245/1600][6/8] Loss_D: 0.7611 Loss_G: 1.9093 D(x): 0.6076 D(G(z)): 0.0745 / 0.0737\n", + "[1246/1600][0/8] Loss_D: 0.7506 Loss_G: 1.8328 D(x): 0.6403 D(G(z)): 0.0832 / 0.0819\n", + "[1246/1600][2/8] Loss_D: 0.7849 Loss_G: 1.8746 D(x): 0.6441 D(G(z)): 0.0811 / 0.0776\n", + "[1246/1600][4/8] Loss_D: 0.7098 Loss_G: 1.8017 D(x): 0.6006 D(G(z)): 0.0821 / 0.0855\n", + "[1246/1600][6/8] Loss_D: 0.7612 Loss_G: 1.9222 D(x): 0.6338 D(G(z)): 0.0751 / 0.0731\n", + "[1247/1600][0/8] Loss_D: 0.7136 Loss_G: 1.8490 D(x): 0.6238 D(G(z)): 0.0788 / 0.0801\n", + "[1247/1600][2/8] Loss_D: 0.7802 Loss_G: 1.9269 D(x): 0.6365 D(G(z)): 0.0755 / 0.0716\n", + "[1247/1600][4/8] Loss_D: 0.7556 Loss_G: 1.8926 D(x): 0.5766 D(G(z)): 0.0752 / 0.0757\n", + "[1247/1600][6/8] Loss_D: 0.7426 Loss_G: 1.9168 D(x): 0.5845 D(G(z)): 0.0727 / 0.0729\n", + "[1248/1600][0/8] Loss_D: 0.7720 Loss_G: 1.8143 D(x): 0.5547 D(G(z)): 0.0808 / 0.0833\n", + "[1248/1600][2/8] Loss_D: 0.7067 Loss_G: 1.8833 D(x): 0.6258 D(G(z)): 0.0768 / 0.0777\n", + "[1248/1600][4/8] Loss_D: 0.7325 Loss_G: 1.8535 D(x): 0.6426 D(G(z)): 0.0823 / 0.0800\n", + "[1248/1600][6/8] Loss_D: 0.7741 Loss_G: 1.8976 D(x): 0.6244 D(G(z)): 0.0783 / 0.0755\n", + "[1249/1600][0/8] Loss_D: 0.7336 Loss_G: 1.8673 D(x): 0.6065 D(G(z)): 0.0783 / 0.0797\n", + "[1249/1600][2/8] Loss_D: 0.7211 Loss_G: 1.9428 D(x): 0.6048 D(G(z)): 0.0697 / 0.0707\n", + "[1249/1600][4/8] Loss_D: 0.7996 Loss_G: 1.8207 D(x): 0.6465 D(G(z)): 0.0879 / 0.0849\n", + "[1249/1600][6/8] Loss_D: 0.7039 Loss_G: 1.9100 D(x): 0.6105 D(G(z)): 0.0737 / 0.0740\n", + "[1250/1600][0/8] Loss_D: 0.7463 Loss_G: 1.9104 D(x): 0.6221 D(G(z)): 0.0738 / 0.0735\n", + "[1250/1600][2/8] Loss_D: 0.7757 Loss_G: 1.8377 D(x): 0.6196 D(G(z)): 0.0834 / 0.0817\n", + "[1250/1600][4/8] Loss_D: 0.7895 Loss_G: 1.8364 D(x): 0.6161 D(G(z)): 0.0870 / 0.0838\n", + "[1250/1600][6/8] Loss_D: 0.7854 Loss_G: 1.8874 D(x): 0.5866 D(G(z)): 0.0784 / 0.0764\n", + "[1251/1600][0/8] Loss_D: 0.7469 Loss_G: 1.8456 D(x): 0.5650 D(G(z)): 0.0801 / 0.0823\n", + "[1251/1600][2/8] Loss_D: 0.7267 Loss_G: 1.9511 D(x): 0.6289 D(G(z)): 0.0677 / 0.0679\n", + "[1251/1600][4/8] Loss_D: 0.7405 Loss_G: 1.7871 D(x): 0.5890 D(G(z)): 0.0854 / 0.0893\n", + "[1251/1600][6/8] Loss_D: 0.7796 Loss_G: 1.8835 D(x): 0.6652 D(G(z)): 0.0799 / 0.0765\n", + "[1252/1600][0/8] Loss_D: 0.7342 Loss_G: 1.8461 D(x): 0.5976 D(G(z)): 0.0808 / 0.0812\n", + "[1252/1600][2/8] Loss_D: 0.7596 Loss_G: 1.7763 D(x): 0.6356 D(G(z)): 0.0920 / 0.0894\n", + "[1252/1600][4/8] Loss_D: 0.7282 Loss_G: 1.9923 D(x): 0.5968 D(G(z)): 0.0657 / 0.0656\n", + "[1252/1600][6/8] Loss_D: 0.7652 Loss_G: 1.9109 D(x): 0.5977 D(G(z)): 0.0751 / 0.0737\n", + "[1253/1600][0/8] Loss_D: 0.7194 Loss_G: 1.8542 D(x): 0.6081 D(G(z)): 0.0784 / 0.0803\n", + "[1253/1600][2/8] Loss_D: 0.7384 Loss_G: 1.9034 D(x): 0.6047 D(G(z)): 0.0738 / 0.0742\n", + "[1253/1600][4/8] Loss_D: 0.7726 Loss_G: 1.8351 D(x): 0.5922 D(G(z)): 0.0833 / 0.0826\n", + "[1253/1600][6/8] Loss_D: 0.7796 Loss_G: 1.8886 D(x): 0.6174 D(G(z)): 0.0784 / 0.0761\n", + "[1254/1600][0/8] Loss_D: 0.7167 Loss_G: 1.8963 D(x): 0.6554 D(G(z)): 0.0776 / 0.0757\n", + "[1254/1600][2/8] Loss_D: 0.7258 Loss_G: 1.8815 D(x): 0.6210 D(G(z)): 0.0761 / 0.0757\n", + "[1254/1600][4/8] Loss_D: 0.7685 Loss_G: 1.8684 D(x): 0.5875 D(G(z)): 0.0770 / 0.0777\n", + "[1254/1600][6/8] Loss_D: 0.7450 Loss_G: 1.8561 D(x): 0.6176 D(G(z)): 0.0791 / 0.0804\n", + "[1255/1600][0/8] Loss_D: 0.6888 Loss_G: 1.8521 D(x): 0.6269 D(G(z)): 0.0798 / 0.0827\n", + "[1255/1600][2/8] Loss_D: 0.7520 Loss_G: 1.8190 D(x): 0.6495 D(G(z)): 0.0876 / 0.0856\n", + "[1255/1600][4/8] Loss_D: 0.7366 Loss_G: 1.8562 D(x): 0.6751 D(G(z)): 0.0819 / 0.0786\n", + "[1255/1600][6/8] Loss_D: 0.7713 Loss_G: 1.8924 D(x): 0.6461 D(G(z)): 0.0787 / 0.0758\n", + "[1256/1600][0/8] Loss_D: 0.6920 Loss_G: 1.9528 D(x): 0.6497 D(G(z)): 0.0686 / 0.0688\n", + "[1256/1600][2/8] Loss_D: 0.7826 Loss_G: 1.9328 D(x): 0.6462 D(G(z)): 0.0758 / 0.0715\n", + "[1256/1600][4/8] Loss_D: 0.7343 Loss_G: 2.0391 D(x): 0.5805 D(G(z)): 0.0612 / 0.0610\n", + "[1256/1600][6/8] Loss_D: 0.7227 Loss_G: 1.8929 D(x): 0.5970 D(G(z)): 0.0737 / 0.0767\n", + "[1257/1600][0/8] Loss_D: 0.7508 Loss_G: 1.8080 D(x): 0.6315 D(G(z)): 0.0865 / 0.0866\n", + "[1257/1600][2/8] Loss_D: 0.6805 Loss_G: 1.8698 D(x): 0.6375 D(G(z)): 0.0754 / 0.0788\n", + "[1257/1600][4/8] Loss_D: 0.7161 Loss_G: 1.8532 D(x): 0.6554 D(G(z)): 0.0825 / 0.0813\n", + "[1257/1600][6/8] Loss_D: 0.7139 Loss_G: 1.8627 D(x): 0.6398 D(G(z)): 0.0773 / 0.0778\n", + "[1258/1600][0/8] Loss_D: 0.7424 Loss_G: 1.8263 D(x): 0.6174 D(G(z)): 0.0833 / 0.0834\n", + "[1258/1600][2/8] Loss_D: 0.7649 Loss_G: 1.8312 D(x): 0.6597 D(G(z)): 0.0872 / 0.0835\n", + "[1258/1600][4/8] Loss_D: 0.7027 Loss_G: 1.9149 D(x): 0.6393 D(G(z)): 0.0751 / 0.0735\n", + "[1258/1600][6/8] Loss_D: 0.7246 Loss_G: 1.8895 D(x): 0.6306 D(G(z)): 0.0752 / 0.0765\n", + "[1259/1600][0/8] Loss_D: 0.7627 Loss_G: 1.8638 D(x): 0.6113 D(G(z)): 0.0793 / 0.0784\n", + "[1259/1600][2/8] Loss_D: 0.7075 Loss_G: 1.9031 D(x): 0.6067 D(G(z)): 0.0735 / 0.0749\n", + "[1259/1600][4/8] Loss_D: 0.7616 Loss_G: 1.8793 D(x): 0.6160 D(G(z)): 0.0792 / 0.0764\n", + "[1259/1600][6/8] Loss_D: 0.7052 Loss_G: 1.8800 D(x): 0.6186 D(G(z)): 0.0757 / 0.0775\n", + "[1260/1600][0/8] Loss_D: 0.7186 Loss_G: 1.8534 D(x): 0.6353 D(G(z)): 0.0802 / 0.0804\n", + "[1260/1600][2/8] Loss_D: 0.7560 Loss_G: 1.7898 D(x): 0.5965 D(G(z)): 0.0873 / 0.0891\n", + "[1260/1600][4/8] Loss_D: 0.7723 Loss_G: 1.8740 D(x): 0.6469 D(G(z)): 0.0823 / 0.0778\n", + "[1260/1600][6/8] Loss_D: 0.7482 Loss_G: 1.9032 D(x): 0.6038 D(G(z)): 0.0757 / 0.0748\n", + "[1261/1600][0/8] Loss_D: 0.7352 Loss_G: 1.9074 D(x): 0.5816 D(G(z)): 0.0729 / 0.0739\n", + "[1261/1600][2/8] Loss_D: 0.7487 Loss_G: 1.8678 D(x): 0.6194 D(G(z)): 0.0798 / 0.0793\n", + "[1261/1600][4/8] Loss_D: 0.7305 Loss_G: 1.9151 D(x): 0.6456 D(G(z)): 0.0737 / 0.0732\n", + "[1261/1600][6/8] Loss_D: 0.7930 Loss_G: 1.8382 D(x): 0.6348 D(G(z)): 0.0852 / 0.0825\n", + "[1262/1600][0/8] Loss_D: 0.6936 Loss_G: 1.8704 D(x): 0.6276 D(G(z)): 0.0769 / 0.0776\n", + "[1262/1600][2/8] Loss_D: 0.6998 Loss_G: 1.8831 D(x): 0.6098 D(G(z)): 0.0761 / 0.0778\n", + "[1262/1600][4/8] Loss_D: 0.7337 Loss_G: 1.8336 D(x): 0.6187 D(G(z)): 0.0796 / 0.0820\n", + "[1262/1600][6/8] Loss_D: 0.6896 Loss_G: 1.8916 D(x): 0.6705 D(G(z)): 0.0780 / 0.0769\n", + "[1263/1600][0/8] Loss_D: 0.7185 Loss_G: 1.8588 D(x): 0.6283 D(G(z)): 0.0821 / 0.0806\n", + "[1263/1600][2/8] Loss_D: 0.7357 Loss_G: 1.9177 D(x): 0.5939 D(G(z)): 0.0711 / 0.0716\n", + "[1263/1600][4/8] Loss_D: 0.7907 Loss_G: 1.8861 D(x): 0.5828 D(G(z)): 0.0791 / 0.0781\n", + "[1263/1600][6/8] Loss_D: 0.6877 Loss_G: 1.8425 D(x): 0.6409 D(G(z)): 0.0809 / 0.0823\n", + "[1264/1600][0/8] Loss_D: 0.7351 Loss_G: 1.8079 D(x): 0.6053 D(G(z)): 0.0836 / 0.0848\n", + "[1264/1600][2/8] Loss_D: 0.7602 Loss_G: 1.9039 D(x): 0.6345 D(G(z)): 0.0753 / 0.0732\n", + "[1264/1600][4/8] Loss_D: 0.6722 Loss_G: 1.8848 D(x): 0.7221 D(G(z)): 0.0775 / 0.0755\n", + "[1264/1600][6/8] Loss_D: 0.7765 Loss_G: 1.8390 D(x): 0.5977 D(G(z)): 0.0829 / 0.0816\n", + "[1265/1600][0/8] Loss_D: 0.7209 Loss_G: 1.8502 D(x): 0.6237 D(G(z)): 0.0814 / 0.0818\n", + "[1265/1600][2/8] Loss_D: 0.7370 Loss_G: 1.8747 D(x): 0.6367 D(G(z)): 0.0791 / 0.0776\n", + "[1265/1600][4/8] Loss_D: 0.7070 Loss_G: 1.8811 D(x): 0.6034 D(G(z)): 0.0720 / 0.0756\n", + "[1265/1600][6/8] Loss_D: 0.7771 Loss_G: 1.8785 D(x): 0.5860 D(G(z)): 0.0794 / 0.0776\n", + "[1266/1600][0/8] Loss_D: 0.7693 Loss_G: 1.8715 D(x): 0.6037 D(G(z)): 0.0798 / 0.0793\n", + "[1266/1600][2/8] Loss_D: 0.7229 Loss_G: 1.7931 D(x): 0.6537 D(G(z)): 0.0881 / 0.0884\n", + "[1266/1600][4/8] Loss_D: 0.7028 Loss_G: 1.8240 D(x): 0.6245 D(G(z)): 0.0822 / 0.0830\n", + "[1266/1600][6/8] Loss_D: 0.7066 Loss_G: 1.7452 D(x): 0.6418 D(G(z)): 0.0925 / 0.0941\n", + "[1267/1600][0/8] Loss_D: 0.7912 Loss_G: 1.8329 D(x): 0.6406 D(G(z)): 0.0870 / 0.0830\n", + "[1267/1600][2/8] Loss_D: 0.6901 Loss_G: 1.8414 D(x): 0.6527 D(G(z)): 0.0827 / 0.0818\n", + "[1267/1600][4/8] Loss_D: 0.7810 Loss_G: 1.8953 D(x): 0.6301 D(G(z)): 0.0784 / 0.0749\n", + "[1267/1600][6/8] Loss_D: 0.7674 Loss_G: 1.8690 D(x): 0.5636 D(G(z)): 0.0790 / 0.0800\n", + "[1268/1600][0/8] Loss_D: 0.7060 Loss_G: 1.9372 D(x): 0.6438 D(G(z)): 0.0716 / 0.0712\n", + "[1268/1600][2/8] Loss_D: 0.7604 Loss_G: 1.8591 D(x): 0.5881 D(G(z)): 0.0790 / 0.0792\n", + "[1268/1600][4/8] Loss_D: 0.7641 Loss_G: 1.8451 D(x): 0.6250 D(G(z)): 0.0814 / 0.0807\n", + "[1268/1600][6/8] Loss_D: 0.7663 Loss_G: 1.9255 D(x): 0.6330 D(G(z)): 0.0739 / 0.0720\n", + "[1269/1600][0/8] Loss_D: 0.7435 Loss_G: 1.8790 D(x): 0.6479 D(G(z)): 0.0786 / 0.0772\n", + "[1269/1600][2/8] Loss_D: 0.7390 Loss_G: 1.8352 D(x): 0.6130 D(G(z)): 0.0816 / 0.0824\n", + "[1269/1600][4/8] Loss_D: 0.6935 Loss_G: 1.8460 D(x): 0.6818 D(G(z)): 0.0813 / 0.0798\n", + "[1269/1600][6/8] Loss_D: 0.6895 Loss_G: 1.8636 D(x): 0.6352 D(G(z)): 0.0760 / 0.0782\n", + "[1270/1600][0/8] Loss_D: 0.7225 Loss_G: 1.8064 D(x): 0.6527 D(G(z)): 0.0861 / 0.0854\n", + "[1270/1600][2/8] Loss_D: 0.7398 Loss_G: 1.8436 D(x): 0.6099 D(G(z)): 0.0839 / 0.0834\n", + "[1270/1600][4/8] Loss_D: 0.7686 Loss_G: 1.8618 D(x): 0.6249 D(G(z)): 0.0823 / 0.0784\n", + "[1270/1600][6/8] Loss_D: 0.6919 Loss_G: 1.9590 D(x): 0.6256 D(G(z)): 0.0681 / 0.0687\n", + "[1271/1600][0/8] Loss_D: 0.7337 Loss_G: 1.8995 D(x): 0.5966 D(G(z)): 0.0761 / 0.0766\n", + "[1271/1600][2/8] Loss_D: 0.7680 Loss_G: 1.9009 D(x): 0.5796 D(G(z)): 0.0758 / 0.0752\n", + "[1271/1600][4/8] Loss_D: 0.7459 Loss_G: 1.9436 D(x): 0.5722 D(G(z)): 0.0706 / 0.0705\n", + "[1271/1600][6/8] Loss_D: 0.7460 Loss_G: 1.9111 D(x): 0.5584 D(G(z)): 0.0711 / 0.0740\n", + "[1272/1600][0/8] Loss_D: 0.7179 Loss_G: 1.8714 D(x): 0.5907 D(G(z)): 0.0736 / 0.0783\n", + "[1272/1600][2/8] Loss_D: 0.6877 Loss_G: 1.7807 D(x): 0.6295 D(G(z)): 0.0849 / 0.0892\n", + "[1272/1600][4/8] Loss_D: 0.7172 Loss_G: 1.7206 D(x): 0.6210 D(G(z)): 0.0959 / 0.0976\n", + "[1272/1600][6/8] Loss_D: 0.6412 Loss_G: 1.8566 D(x): 0.6958 D(G(z)): 0.0803 / 0.0789\n", + "[1273/1600][0/8] Loss_D: 0.7505 Loss_G: 1.9412 D(x): 0.6512 D(G(z)): 0.0755 / 0.0717\n", + "[1273/1600][2/8] Loss_D: 0.7352 Loss_G: 1.9312 D(x): 0.6109 D(G(z)): 0.0706 / 0.0712\n", + "[1273/1600][4/8] Loss_D: 0.6605 Loss_G: 1.8307 D(x): 0.6545 D(G(z)): 0.0809 / 0.0823\n", + "[1273/1600][6/8] Loss_D: 0.7911 Loss_G: 1.8126 D(x): 0.6380 D(G(z)): 0.0884 / 0.0841\n", + "[1274/1600][0/8] Loss_D: 0.8008 Loss_G: 1.8172 D(x): 0.6180 D(G(z)): 0.0875 / 0.0837\n", + "[1274/1600][2/8] Loss_D: 0.7087 Loss_G: 1.9688 D(x): 0.5935 D(G(z)): 0.0667 / 0.0675\n", + "[1274/1600][4/8] Loss_D: 0.7277 Loss_G: 1.8680 D(x): 0.6215 D(G(z)): 0.0771 / 0.0776\n", + "[1274/1600][6/8] Loss_D: 0.7274 Loss_G: 1.8713 D(x): 0.6276 D(G(z)): 0.0791 / 0.0781\n", + "[1275/1600][0/8] Loss_D: 0.7299 Loss_G: 1.9682 D(x): 0.5855 D(G(z)): 0.0656 / 0.0668\n", + "[1275/1600][2/8] Loss_D: 0.6987 Loss_G: 1.8108 D(x): 0.6135 D(G(z)): 0.0779 / 0.0834\n", + "[1275/1600][4/8] Loss_D: 0.7702 Loss_G: 1.8473 D(x): 0.6899 D(G(z)): 0.0852 / 0.0804\n", + "[1275/1600][6/8] Loss_D: 0.7685 Loss_G: 1.9255 D(x): 0.6319 D(G(z)): 0.0776 / 0.0721\n", + "[1276/1600][0/8] Loss_D: 0.7315 Loss_G: 1.8818 D(x): 0.5774 D(G(z)): 0.0739 / 0.0770\n", + "[1276/1600][2/8] Loss_D: 0.7304 Loss_G: 1.8720 D(x): 0.6789 D(G(z)): 0.0779 / 0.0778\n", + "[1276/1600][4/8] Loss_D: 0.7352 Loss_G: 1.7469 D(x): 0.6094 D(G(z)): 0.0902 / 0.0931\n", + "[1276/1600][6/8] Loss_D: 0.7243 Loss_G: 1.8317 D(x): 0.6491 D(G(z)): 0.0841 / 0.0825\n", + "[1277/1600][0/8] Loss_D: 0.7038 Loss_G: 1.8448 D(x): 0.6361 D(G(z)): 0.0805 / 0.0804\n", + "[1277/1600][2/8] Loss_D: 0.7513 Loss_G: 1.8324 D(x): 0.6136 D(G(z)): 0.0825 / 0.0828\n", + "[1277/1600][4/8] Loss_D: 0.6914 Loss_G: 1.8493 D(x): 0.6259 D(G(z)): 0.0796 / 0.0814\n", + "[1277/1600][6/8] Loss_D: 0.7884 Loss_G: 1.8580 D(x): 0.6631 D(G(z)): 0.0827 / 0.0785\n", + "[1278/1600][0/8] Loss_D: 0.7638 Loss_G: 1.7651 D(x): 0.6091 D(G(z)): 0.0934 / 0.0922\n", + "[1278/1600][2/8] Loss_D: 0.7583 Loss_G: 1.8510 D(x): 0.6558 D(G(z)): 0.0853 / 0.0818\n", + "[1278/1600][4/8] Loss_D: 0.7669 Loss_G: 1.9346 D(x): 0.6059 D(G(z)): 0.0754 / 0.0724\n", + "[1278/1600][6/8] Loss_D: 0.7547 Loss_G: 1.9917 D(x): 0.6010 D(G(z)): 0.0660 / 0.0645\n", + "[1279/1600][0/8] Loss_D: 0.7318 Loss_G: 2.0131 D(x): 0.5985 D(G(z)): 0.0636 / 0.0637\n", + "[1279/1600][2/8] Loss_D: 0.7104 Loss_G: 1.9418 D(x): 0.6072 D(G(z)): 0.0664 / 0.0699\n", + "[1279/1600][4/8] Loss_D: 0.7642 Loss_G: 1.9459 D(x): 0.6140 D(G(z)): 0.0701 / 0.0697\n", + "[1279/1600][6/8] Loss_D: 0.7205 Loss_G: 1.9235 D(x): 0.6466 D(G(z)): 0.0724 / 0.0714\n", + "[1280/1600][0/8] Loss_D: 0.7171 Loss_G: 1.8410 D(x): 0.5793 D(G(z)): 0.0770 / 0.0800\n", + "[1280/1600][2/8] Loss_D: 0.7305 Loss_G: 1.9271 D(x): 0.6149 D(G(z)): 0.0720 / 0.0718\n", + "[1280/1600][4/8] Loss_D: 0.7604 Loss_G: 1.9625 D(x): 0.6168 D(G(z)): 0.0701 / 0.0680\n", + "[1280/1600][6/8] Loss_D: 0.7255 Loss_G: 1.9488 D(x): 0.6067 D(G(z)): 0.0688 / 0.0693\n", + "[1281/1600][0/8] Loss_D: 0.6877 Loss_G: 1.9407 D(x): 0.6365 D(G(z)): 0.0701 / 0.0711\n", + "[1281/1600][2/8] Loss_D: 0.6754 Loss_G: 1.8289 D(x): 0.6696 D(G(z)): 0.0814 / 0.0835\n", + "[1281/1600][4/8] Loss_D: 0.6939 Loss_G: 1.7828 D(x): 0.6552 D(G(z)): 0.0869 / 0.0876\n", + "[1281/1600][6/8] Loss_D: 0.7993 Loss_G: 1.8383 D(x): 0.6483 D(G(z)): 0.0837 / 0.0815\n", + "[1282/1600][0/8] Loss_D: 0.7570 Loss_G: 1.8267 D(x): 0.6079 D(G(z)): 0.0847 / 0.0839\n", + "[1282/1600][2/8] Loss_D: 0.7487 Loss_G: 1.8590 D(x): 0.6166 D(G(z)): 0.0805 / 0.0794\n", + "[1282/1600][4/8] Loss_D: 0.7278 Loss_G: 1.9701 D(x): 0.6854 D(G(z)): 0.0716 / 0.0684\n", + "[1282/1600][6/8] Loss_D: 0.7648 Loss_G: 1.9663 D(x): 0.6365 D(G(z)): 0.0717 / 0.0691\n", + "[1283/1600][0/8] Loss_D: 0.7225 Loss_G: 1.8729 D(x): 0.5774 D(G(z)): 0.0748 / 0.0781\n", + "[1283/1600][2/8] Loss_D: 0.7575 Loss_G: 1.9027 D(x): 0.6489 D(G(z)): 0.0758 / 0.0752\n", + "[1283/1600][4/8] Loss_D: 0.7450 Loss_G: 1.9137 D(x): 0.6493 D(G(z)): 0.0737 / 0.0729\n", + "[1283/1600][6/8] Loss_D: 0.7410 Loss_G: 1.8794 D(x): 0.5681 D(G(z)): 0.0755 / 0.0774\n", + "[1284/1600][0/8] Loss_D: 0.6967 Loss_G: 1.8045 D(x): 0.6142 D(G(z)): 0.0836 / 0.0873\n", + "[1284/1600][2/8] Loss_D: 0.7392 Loss_G: 1.9065 D(x): 0.6406 D(G(z)): 0.0764 / 0.0742\n", + "[1284/1600][4/8] Loss_D: 0.7842 Loss_G: 1.9003 D(x): 0.6078 D(G(z)): 0.0774 / 0.0741\n", + "[1284/1600][6/8] Loss_D: 0.7144 Loss_G: 1.9686 D(x): 0.6161 D(G(z)): 0.0706 / 0.0692\n", + "[1285/1600][0/8] Loss_D: 0.7294 Loss_G: 1.9196 D(x): 0.6110 D(G(z)): 0.0707 / 0.0726\n", + "[1285/1600][2/8] Loss_D: 0.6775 Loss_G: 1.9336 D(x): 0.6258 D(G(z)): 0.0678 / 0.0717\n", + "[1285/1600][4/8] Loss_D: 0.8033 Loss_G: 1.8483 D(x): 0.6727 D(G(z)): 0.0844 / 0.0803\n", + "[1285/1600][6/8] Loss_D: 0.7279 Loss_G: 1.9774 D(x): 0.6383 D(G(z)): 0.0708 / 0.0681\n", + "[1286/1600][0/8] Loss_D: 0.7642 Loss_G: 1.9582 D(x): 0.6111 D(G(z)): 0.0720 / 0.0685\n", + "[1286/1600][2/8] Loss_D: 0.7157 Loss_G: 2.0263 D(x): 0.6077 D(G(z)): 0.0617 / 0.0625\n", + "[1286/1600][4/8] Loss_D: 0.7407 Loss_G: 1.9819 D(x): 0.5930 D(G(z)): 0.0646 / 0.0660\n", + "[1286/1600][6/8] Loss_D: 0.7863 Loss_G: 1.9578 D(x): 0.5948 D(G(z)): 0.0707 / 0.0698\n", + "[1287/1600][0/8] Loss_D: 0.6804 Loss_G: 2.0295 D(x): 0.6381 D(G(z)): 0.0604 / 0.0625\n", + "[1287/1600][2/8] Loss_D: 0.6860 Loss_G: 1.8740 D(x): 0.6360 D(G(z)): 0.0758 / 0.0779\n", + "[1287/1600][4/8] Loss_D: 0.7546 Loss_G: 1.8381 D(x): 0.6050 D(G(z)): 0.0823 / 0.0818\n", + "[1287/1600][6/8] Loss_D: 0.6769 Loss_G: 1.8464 D(x): 0.6583 D(G(z)): 0.0804 / 0.0803\n", + "[1288/1600][0/8] Loss_D: 0.7422 Loss_G: 1.8918 D(x): 0.5953 D(G(z)): 0.0761 / 0.0758\n", + "[1288/1600][2/8] Loss_D: 0.7464 Loss_G: 1.9219 D(x): 0.6115 D(G(z)): 0.0755 / 0.0730\n", + "[1288/1600][4/8] Loss_D: 0.7111 Loss_G: 1.8908 D(x): 0.5955 D(G(z)): 0.0717 / 0.0757\n", + "[1288/1600][6/8] Loss_D: 0.7593 Loss_G: 1.7906 D(x): 0.6068 D(G(z)): 0.0866 / 0.0883\n", + "[1289/1600][0/8] Loss_D: 0.6986 Loss_G: 1.8374 D(x): 0.6407 D(G(z)): 0.0819 / 0.0816\n", + "[1289/1600][2/8] Loss_D: 0.6976 Loss_G: 1.7725 D(x): 0.6342 D(G(z)): 0.0905 / 0.0912\n", + "[1289/1600][4/8] Loss_D: 0.7773 Loss_G: 1.8302 D(x): 0.5981 D(G(z)): 0.0846 / 0.0827\n", + "[1289/1600][6/8] Loss_D: 0.7329 Loss_G: 1.8467 D(x): 0.6232 D(G(z)): 0.0813 / 0.0797\n", + "[1290/1600][0/8] Loss_D: 0.7478 Loss_G: 1.8656 D(x): 0.6119 D(G(z)): 0.0809 / 0.0797\n", + "[1290/1600][2/8] Loss_D: 0.6580 Loss_G: 1.9188 D(x): 0.6536 D(G(z)): 0.0702 / 0.0729\n", + "[1290/1600][4/8] Loss_D: 0.6986 Loss_G: 1.7653 D(x): 0.6454 D(G(z)): 0.0913 / 0.0932\n", + "[1290/1600][6/8] Loss_D: 0.6571 Loss_G: 1.8735 D(x): 0.6652 D(G(z)): 0.0781 / 0.0779\n", + "[1291/1600][0/8] Loss_D: 0.7631 Loss_G: 1.7836 D(x): 0.6351 D(G(z)): 0.0906 / 0.0892\n", + "[1291/1600][2/8] Loss_D: 0.7737 Loss_G: 1.8889 D(x): 0.6640 D(G(z)): 0.0821 / 0.0769\n", + "[1291/1600][4/8] Loss_D: 0.7807 Loss_G: 1.9714 D(x): 0.6046 D(G(z)): 0.0731 / 0.0685\n", + "[1291/1600][6/8] Loss_D: 0.7530 Loss_G: 1.9784 D(x): 0.5840 D(G(z)): 0.0665 / 0.0675\n", + "[1292/1600][0/8] Loss_D: 0.7652 Loss_G: 1.9678 D(x): 0.6461 D(G(z)): 0.0698 / 0.0686\n", + "[1292/1600][2/8] Loss_D: 0.7317 Loss_G: 1.9412 D(x): 0.5742 D(G(z)): 0.0678 / 0.0700\n", + "[1292/1600][4/8] Loss_D: 0.6951 Loss_G: 1.8318 D(x): 0.6204 D(G(z)): 0.0802 / 0.0828\n", + "[1292/1600][6/8] Loss_D: 0.7389 Loss_G: 1.8641 D(x): 0.5841 D(G(z)): 0.0762 / 0.0795\n", + "[1293/1600][0/8] Loss_D: 0.7132 Loss_G: 1.9499 D(x): 0.6444 D(G(z)): 0.0715 / 0.0708\n", + "[1293/1600][2/8] Loss_D: 0.7563 Loss_G: 1.8443 D(x): 0.6149 D(G(z)): 0.0805 / 0.0801\n", + "[1293/1600][4/8] Loss_D: 0.7592 Loss_G: 1.8579 D(x): 0.6332 D(G(z)): 0.0820 / 0.0792\n", + "[1293/1600][6/8] Loss_D: 0.7598 Loss_G: 1.9327 D(x): 0.6173 D(G(z)): 0.0730 / 0.0708\n", + "[1294/1600][0/8] Loss_D: 0.6925 Loss_G: 1.9520 D(x): 0.6217 D(G(z)): 0.0688 / 0.0707\n", + "[1294/1600][2/8] Loss_D: 0.7108 Loss_G: 1.8050 D(x): 0.6172 D(G(z)): 0.0856 / 0.0885\n", + "[1294/1600][4/8] Loss_D: 0.7676 Loss_G: 1.8281 D(x): 0.6417 D(G(z)): 0.0859 / 0.0843\n", + "[1294/1600][6/8] Loss_D: 0.6929 Loss_G: 1.8018 D(x): 0.6431 D(G(z)): 0.0835 / 0.0862\n", + "[1295/1600][0/8] Loss_D: 0.7090 Loss_G: 1.8036 D(x): 0.6410 D(G(z)): 0.0870 / 0.0867\n", + "[1295/1600][2/8] Loss_D: 0.7302 Loss_G: 1.7950 D(x): 0.7031 D(G(z)): 0.0887 / 0.0862\n", + "[1295/1600][4/8] Loss_D: 0.7531 Loss_G: 1.8895 D(x): 0.6021 D(G(z)): 0.0783 / 0.0766\n", + "[1295/1600][6/8] Loss_D: 0.7743 Loss_G: 1.9417 D(x): 0.6253 D(G(z)): 0.0749 / 0.0710\n", + "[1296/1600][0/8] Loss_D: 0.7008 Loss_G: 1.8331 D(x): 0.6302 D(G(z)): 0.0824 / 0.0839\n", + "[1296/1600][2/8] Loss_D: 0.7902 Loss_G: 1.7730 D(x): 0.6026 D(G(z)): 0.0898 / 0.0891\n", + "[1296/1600][4/8] Loss_D: 0.6760 Loss_G: 1.8531 D(x): 0.6357 D(G(z)): 0.0780 / 0.0806\n", + "[1296/1600][6/8] Loss_D: 0.7396 Loss_G: 1.7938 D(x): 0.6456 D(G(z)): 0.0880 / 0.0882\n", + "[1297/1600][0/8] Loss_D: 0.7556 Loss_G: 1.8788 D(x): 0.5778 D(G(z)): 0.0767 / 0.0762\n", + "[1297/1600][2/8] Loss_D: 0.6975 Loss_G: 1.8270 D(x): 0.6153 D(G(z)): 0.0809 / 0.0821\n", + "[1297/1600][4/8] Loss_D: 0.7283 Loss_G: 1.7747 D(x): 0.6540 D(G(z)): 0.0928 / 0.0921\n", + "[1297/1600][6/8] Loss_D: 0.7498 Loss_G: 1.8455 D(x): 0.6433 D(G(z)): 0.0840 / 0.0815\n", + "[1298/1600][0/8] Loss_D: 0.7549 Loss_G: 1.8822 D(x): 0.6610 D(G(z)): 0.0800 / 0.0772\n", + "[1298/1600][2/8] Loss_D: 0.7325 Loss_G: 1.8647 D(x): 0.6170 D(G(z)): 0.0797 / 0.0795\n", + "[1298/1600][4/8] Loss_D: 0.7539 Loss_G: 1.8668 D(x): 0.6223 D(G(z)): 0.0784 / 0.0791\n", + "[1298/1600][6/8] Loss_D: 0.7470 Loss_G: 1.8906 D(x): 0.6427 D(G(z)): 0.0765 / 0.0740\n", + "[1299/1600][0/8] Loss_D: 0.7052 Loss_G: 1.9136 D(x): 0.6367 D(G(z)): 0.0713 / 0.0722\n", + "[1299/1600][2/8] Loss_D: 0.7230 Loss_G: 1.8754 D(x): 0.6184 D(G(z)): 0.0776 / 0.0767\n", + "[1299/1600][4/8] Loss_D: 0.7461 Loss_G: 1.8901 D(x): 0.6464 D(G(z)): 0.0764 / 0.0755\n", + "[1299/1600][6/8] Loss_D: 0.6868 Loss_G: 1.8390 D(x): 0.6346 D(G(z)): 0.0782 / 0.0804\n", + "[1300/1600][0/8] Loss_D: 0.7591 Loss_G: 1.8605 D(x): 0.5577 D(G(z)): 0.0774 / 0.0808\n", + "[1300/1600][2/8] Loss_D: 0.7708 Loss_G: 1.8374 D(x): 0.6474 D(G(z)): 0.0864 / 0.0831\n", + "[1300/1600][4/8] Loss_D: 0.7187 Loss_G: 1.9177 D(x): 0.6358 D(G(z)): 0.0760 / 0.0740\n", + "[1300/1600][6/8] Loss_D: 0.6700 Loss_G: 1.9405 D(x): 0.6338 D(G(z)): 0.0679 / 0.0702\n", + "[1301/1600][0/8] Loss_D: 0.7345 Loss_G: 1.8402 D(x): 0.6215 D(G(z)): 0.0797 / 0.0810\n", + "[1301/1600][2/8] Loss_D: 0.7477 Loss_G: 1.8457 D(x): 0.6106 D(G(z)): 0.0840 / 0.0815\n", + "[1301/1600][4/8] Loss_D: 0.7458 Loss_G: 1.8553 D(x): 0.6127 D(G(z)): 0.0789 / 0.0793\n", + "[1301/1600][6/8] Loss_D: 0.7826 Loss_G: 1.9361 D(x): 0.5957 D(G(z)): 0.0740 / 0.0702\n", + "[1302/1600][0/8] Loss_D: 0.7325 Loss_G: 1.9737 D(x): 0.6077 D(G(z)): 0.0695 / 0.0687\n", + "[1302/1600][2/8] Loss_D: 0.7003 Loss_G: 1.8784 D(x): 0.6074 D(G(z)): 0.0738 / 0.0774\n", + "[1302/1600][4/8] Loss_D: 0.7340 Loss_G: 1.8202 D(x): 0.6116 D(G(z)): 0.0817 / 0.0845\n", + "[1302/1600][6/8] Loss_D: 0.7305 Loss_G: 1.8034 D(x): 0.6479 D(G(z)): 0.0855 / 0.0866\n", + "[1303/1600][0/8] Loss_D: 0.6709 Loss_G: 1.7350 D(x): 0.6629 D(G(z)): 0.0908 / 0.0945\n", + "[1303/1600][2/8] Loss_D: 0.7704 Loss_G: 1.7840 D(x): 0.6247 D(G(z)): 0.0921 / 0.0892\n", + "[1303/1600][4/8] Loss_D: 0.7982 Loss_G: 1.8882 D(x): 0.6678 D(G(z)): 0.0830 / 0.0765\n", + "[1303/1600][6/8] Loss_D: 0.6771 Loss_G: 1.9640 D(x): 0.6140 D(G(z)): 0.0691 / 0.0693\n", + "[1304/1600][0/8] Loss_D: 0.7368 Loss_G: 1.8775 D(x): 0.6386 D(G(z)): 0.0772 / 0.0780\n", + "[1304/1600][2/8] Loss_D: 0.7967 Loss_G: 1.8719 D(x): 0.6333 D(G(z)): 0.0829 / 0.0787\n", + "[1304/1600][4/8] Loss_D: 0.7421 Loss_G: 1.9153 D(x): 0.5844 D(G(z)): 0.0727 / 0.0739\n", + "[1304/1600][6/8] Loss_D: 0.7595 Loss_G: 1.8924 D(x): 0.6651 D(G(z)): 0.0782 / 0.0770\n", + "[1305/1600][0/8] Loss_D: 0.7655 Loss_G: 1.9276 D(x): 0.6468 D(G(z)): 0.0752 / 0.0714\n", + "[1305/1600][2/8] Loss_D: 0.7154 Loss_G: 1.8505 D(x): 0.5939 D(G(z)): 0.0793 / 0.0815\n", + "[1305/1600][4/8] Loss_D: 0.7787 Loss_G: 1.8716 D(x): 0.6155 D(G(z)): 0.0804 / 0.0789\n", + "[1305/1600][6/8] Loss_D: 0.6822 Loss_G: 1.9256 D(x): 0.6206 D(G(z)): 0.0700 / 0.0709\n", + "[1306/1600][0/8] Loss_D: 0.7678 Loss_G: 1.8361 D(x): 0.5949 D(G(z)): 0.0796 / 0.0806\n", + "[1306/1600][2/8] Loss_D: 0.6842 Loss_G: 1.9434 D(x): 0.6193 D(G(z)): 0.0669 / 0.0705\n", + "[1306/1600][4/8] Loss_D: 0.6969 Loss_G: 1.7365 D(x): 0.6486 D(G(z)): 0.0923 / 0.0939\n", + "[1306/1600][6/8] Loss_D: 0.7785 Loss_G: 1.8872 D(x): 0.6649 D(G(z)): 0.0798 / 0.0767\n", + "[1307/1600][0/8] Loss_D: 0.6990 Loss_G: 1.8517 D(x): 0.6389 D(G(z)): 0.0816 / 0.0799\n", + "[1307/1600][2/8] Loss_D: 0.6678 Loss_G: 1.9506 D(x): 0.6613 D(G(z)): 0.0713 / 0.0706\n", + "[1307/1600][4/8] Loss_D: 0.6778 Loss_G: 1.8090 D(x): 0.6712 D(G(z)): 0.0854 / 0.0862\n", + "[1307/1600][6/8] Loss_D: 0.7577 Loss_G: 1.8321 D(x): 0.6105 D(G(z)): 0.0837 / 0.0831\n", + "[1308/1600][0/8] Loss_D: 0.7065 Loss_G: 1.8812 D(x): 0.6296 D(G(z)): 0.0774 / 0.0780\n", + "[1308/1600][2/8] Loss_D: 0.7257 Loss_G: 1.8734 D(x): 0.6088 D(G(z)): 0.0798 / 0.0802\n", + "[1308/1600][4/8] Loss_D: 0.6634 Loss_G: 1.8791 D(x): 0.6550 D(G(z)): 0.0753 / 0.0775\n", + "[1308/1600][6/8] Loss_D: 0.6839 Loss_G: 1.7841 D(x): 0.6525 D(G(z)): 0.0900 / 0.0901\n", + "[1309/1600][0/8] Loss_D: 0.7841 Loss_G: 1.9477 D(x): 0.6265 D(G(z)): 0.0744 / 0.0707\n", + "[1309/1600][2/8] Loss_D: 0.7634 Loss_G: 1.8527 D(x): 0.5526 D(G(z)): 0.0766 / 0.0791\n", + "[1309/1600][4/8] Loss_D: 0.7466 Loss_G: 1.8775 D(x): 0.6735 D(G(z)): 0.0796 / 0.0765\n", + "[1309/1600][6/8] Loss_D: 0.6846 Loss_G: 1.9249 D(x): 0.6358 D(G(z)): 0.0727 / 0.0726\n", + "[1310/1600][0/8] Loss_D: 0.6891 Loss_G: 1.8807 D(x): 0.6504 D(G(z)): 0.0747 / 0.0760\n", + "[1310/1600][2/8] Loss_D: 0.7672 Loss_G: 1.8645 D(x): 0.6404 D(G(z)): 0.0794 / 0.0785\n", + "[1310/1600][4/8] Loss_D: 0.7158 Loss_G: 1.8452 D(x): 0.6102 D(G(z)): 0.0792 / 0.0819\n", + "[1310/1600][6/8] Loss_D: 0.7204 Loss_G: 1.7975 D(x): 0.6277 D(G(z)): 0.0866 / 0.0879\n", + "[1311/1600][0/8] Loss_D: 0.6975 Loss_G: 1.8198 D(x): 0.6360 D(G(z)): 0.0844 / 0.0848\n", + "[1311/1600][2/8] Loss_D: 0.6848 Loss_G: 1.7670 D(x): 0.6477 D(G(z)): 0.0895 / 0.0908\n", + "[1311/1600][4/8] Loss_D: 0.7194 Loss_G: 1.7741 D(x): 0.6587 D(G(z)): 0.0901 / 0.0897\n", + "[1311/1600][6/8] Loss_D: 0.7742 Loss_G: 1.7857 D(x): 0.6573 D(G(z)): 0.0924 / 0.0893\n", + "[1312/1600][0/8] Loss_D: 0.7762 Loss_G: 1.9319 D(x): 0.6260 D(G(z)): 0.0764 / 0.0717\n", + "[1312/1600][2/8] Loss_D: 0.6744 Loss_G: 1.9570 D(x): 0.6251 D(G(z)): 0.0686 / 0.0690\n", + "[1312/1600][4/8] Loss_D: 0.7284 Loss_G: 1.8902 D(x): 0.5729 D(G(z)): 0.0747 / 0.0778\n", + "[1312/1600][6/8] Loss_D: 0.7251 Loss_G: 1.8035 D(x): 0.6140 D(G(z)): 0.0838 / 0.0867\n", + "[1313/1600][0/8] Loss_D: 0.6970 Loss_G: 1.7951 D(x): 0.6490 D(G(z)): 0.0863 / 0.0871\n", + "[1313/1600][2/8] Loss_D: 0.6885 Loss_G: 1.8451 D(x): 0.6558 D(G(z)): 0.0817 / 0.0818\n", + "[1313/1600][4/8] Loss_D: 0.7072 Loss_G: 1.7546 D(x): 0.6460 D(G(z)): 0.0925 / 0.0918\n", + "[1313/1600][6/8] Loss_D: 0.7776 Loss_G: 1.8713 D(x): 0.6920 D(G(z)): 0.0834 / 0.0791\n", + "[1314/1600][0/8] Loss_D: 0.7948 Loss_G: 1.8587 D(x): 0.5881 D(G(z)): 0.0831 / 0.0802\n", + "[1314/1600][2/8] Loss_D: 0.7135 Loss_G: 1.8417 D(x): 0.6002 D(G(z)): 0.0799 / 0.0813\n", + "[1314/1600][4/8] Loss_D: 0.7357 Loss_G: 1.7997 D(x): 0.6132 D(G(z)): 0.0875 / 0.0875\n", + "[1314/1600][6/8] Loss_D: 0.7521 Loss_G: 1.9939 D(x): 0.5853 D(G(z)): 0.0676 / 0.0664\n", + "[1315/1600][0/8] Loss_D: 0.7154 Loss_G: 1.8965 D(x): 0.5885 D(G(z)): 0.0737 / 0.0765\n", + "[1315/1600][2/8] Loss_D: 0.7637 Loss_G: 1.8897 D(x): 0.6222 D(G(z)): 0.0781 / 0.0775\n", + "[1315/1600][4/8] Loss_D: 0.7669 Loss_G: 1.9227 D(x): 0.5939 D(G(z)): 0.0748 / 0.0731\n", + "[1315/1600][6/8] Loss_D: 0.7266 Loss_G: 1.9242 D(x): 0.6170 D(G(z)): 0.0733 / 0.0730\n", + "[1316/1600][0/8] Loss_D: 0.7095 Loss_G: 1.8889 D(x): 0.6271 D(G(z)): 0.0735 / 0.0749\n", + "[1316/1600][2/8] Loss_D: 0.6810 Loss_G: 1.7962 D(x): 0.6562 D(G(z)): 0.0840 / 0.0877\n", + "[1316/1600][4/8] Loss_D: 0.7167 Loss_G: 1.7344 D(x): 0.6176 D(G(z)): 0.0955 / 0.0972\n", + "[1316/1600][6/8] Loss_D: 0.7374 Loss_G: 1.7582 D(x): 0.6784 D(G(z)): 0.0942 / 0.0925\n", + "[1317/1600][0/8] Loss_D: 0.6840 Loss_G: 1.7736 D(x): 0.6426 D(G(z)): 0.0882 / 0.0898\n", + "[1317/1600][2/8] Loss_D: 0.7476 Loss_G: 1.7661 D(x): 0.6422 D(G(z)): 0.0913 / 0.0905\n", + "[1317/1600][4/8] Loss_D: 0.7059 Loss_G: 1.8480 D(x): 0.6909 D(G(z)): 0.0836 / 0.0799\n", + "[1317/1600][6/8] Loss_D: 0.7537 Loss_G: 1.8579 D(x): 0.6651 D(G(z)): 0.0837 / 0.0794\n", + "[1318/1600][0/8] Loss_D: 0.7728 Loss_G: 1.9568 D(x): 0.5939 D(G(z)): 0.0726 / 0.0696\n", + "[1318/1600][2/8] Loss_D: 0.7618 Loss_G: 1.9673 D(x): 0.5999 D(G(z)): 0.0686 / 0.0667\n", + "[1318/1600][4/8] Loss_D: 0.6856 Loss_G: 2.0093 D(x): 0.6086 D(G(z)): 0.0623 / 0.0645\n", + "[1318/1600][6/8] Loss_D: 0.7632 Loss_G: 1.8939 D(x): 0.5659 D(G(z)): 0.0736 / 0.0748\n", + "[1319/1600][0/8] Loss_D: 0.7556 Loss_G: 1.8436 D(x): 0.5992 D(G(z)): 0.0787 / 0.0815\n", + "[1319/1600][2/8] Loss_D: 0.7850 Loss_G: 1.8035 D(x): 0.6265 D(G(z)): 0.0880 / 0.0864\n", + "[1319/1600][4/8] Loss_D: 0.7583 Loss_G: 1.7704 D(x): 0.5647 D(G(z)): 0.0869 / 0.0903\n", + "[1319/1600][6/8] Loss_D: 0.7129 Loss_G: 1.8143 D(x): 0.6361 D(G(z)): 0.0826 / 0.0831\n", + "[1320/1600][0/8] Loss_D: 0.7835 Loss_G: 1.8844 D(x): 0.6144 D(G(z)): 0.0812 / 0.0786\n", + "[1320/1600][2/8] Loss_D: 0.7791 Loss_G: 1.8606 D(x): 0.6162 D(G(z)): 0.0820 / 0.0791\n", + "[1320/1600][4/8] Loss_D: 0.7174 Loss_G: 1.8944 D(x): 0.6151 D(G(z)): 0.0727 / 0.0742\n", + "[1320/1600][6/8] Loss_D: 0.7366 Loss_G: 1.8666 D(x): 0.6478 D(G(z)): 0.0821 / 0.0792\n", + "[1321/1600][0/8] Loss_D: 0.7391 Loss_G: 1.8935 D(x): 0.6277 D(G(z)): 0.0781 / 0.0755\n", + "[1321/1600][2/8] Loss_D: 0.7089 Loss_G: 1.8714 D(x): 0.5988 D(G(z)): 0.0747 / 0.0777\n", + "[1321/1600][4/8] Loss_D: 0.7609 Loss_G: 1.9392 D(x): 0.6137 D(G(z)): 0.0719 / 0.0708\n", + "[1321/1600][6/8] Loss_D: 0.7369 Loss_G: 1.9143 D(x): 0.6280 D(G(z)): 0.0726 / 0.0732\n", + "[1322/1600][0/8] Loss_D: 0.7090 Loss_G: 1.8064 D(x): 0.6078 D(G(z)): 0.0830 / 0.0870\n", + "[1322/1600][2/8] Loss_D: 0.7086 Loss_G: 1.8724 D(x): 0.6184 D(G(z)): 0.0778 / 0.0782\n", + "[1322/1600][4/8] Loss_D: 0.7166 Loss_G: 1.9254 D(x): 0.6216 D(G(z)): 0.0727 / 0.0722\n", + "[1322/1600][6/8] Loss_D: 0.6555 Loss_G: 1.8812 D(x): 0.6610 D(G(z)): 0.0761 / 0.0768\n", + "[1323/1600][0/8] Loss_D: 0.7255 Loss_G: 1.8179 D(x): 0.6093 D(G(z)): 0.0809 / 0.0840\n", + "[1323/1600][2/8] Loss_D: 0.7235 Loss_G: 1.7994 D(x): 0.6705 D(G(z)): 0.0888 / 0.0858\n", + "[1323/1600][4/8] Loss_D: 0.7914 Loss_G: 1.8609 D(x): 0.6358 D(G(z)): 0.0819 / 0.0788\n", + "[1323/1600][6/8] Loss_D: 0.7135 Loss_G: 1.8877 D(x): 0.6292 D(G(z)): 0.0748 / 0.0754\n", + "[1324/1600][0/8] Loss_D: 0.6856 Loss_G: 1.8165 D(x): 0.6435 D(G(z)): 0.0821 / 0.0839\n", + "[1324/1600][2/8] Loss_D: 0.7815 Loss_G: 1.8336 D(x): 0.6313 D(G(z)): 0.0842 / 0.0822\n", + "[1324/1600][4/8] Loss_D: 0.8203 Loss_G: 1.7924 D(x): 0.6817 D(G(z)): 0.0940 / 0.0883\n", + "[1324/1600][6/8] Loss_D: 0.7611 Loss_G: 2.0030 D(x): 0.6392 D(G(z)): 0.0669 / 0.0634\n", + "[1325/1600][0/8] Loss_D: 0.7625 Loss_G: 1.9292 D(x): 0.5961 D(G(z)): 0.0753 / 0.0728\n", + "[1325/1600][2/8] Loss_D: 0.7473 Loss_G: 1.8960 D(x): 0.6255 D(G(z)): 0.0762 / 0.0750\n", + "[1325/1600][4/8] Loss_D: 0.7438 Loss_G: 1.8801 D(x): 0.5942 D(G(z)): 0.0749 / 0.0770\n", + "[1325/1600][6/8] Loss_D: 0.6980 Loss_G: 1.8397 D(x): 0.6181 D(G(z)): 0.0774 / 0.0813\n", + "[1326/1600][0/8] Loss_D: 0.8093 Loss_G: 1.8649 D(x): 0.6614 D(G(z)): 0.0839 / 0.0800\n", + "[1326/1600][2/8] Loss_D: 0.7656 Loss_G: 1.8890 D(x): 0.6176 D(G(z)): 0.0775 / 0.0759\n", + "[1326/1600][4/8] Loss_D: 0.7807 Loss_G: 1.8444 D(x): 0.6406 D(G(z)): 0.0856 / 0.0829\n", + "[1326/1600][6/8] Loss_D: 0.7293 Loss_G: 1.9066 D(x): 0.5879 D(G(z)): 0.0736 / 0.0751\n", + "[1327/1600][0/8] Loss_D: 0.7232 Loss_G: 1.8159 D(x): 0.6258 D(G(z)): 0.0828 / 0.0843\n", + "[1327/1600][2/8] Loss_D: 0.7593 Loss_G: 1.8381 D(x): 0.6440 D(G(z)): 0.0851 / 0.0834\n", + "[1327/1600][4/8] Loss_D: 0.7796 Loss_G: 1.8205 D(x): 0.5941 D(G(z)): 0.0847 / 0.0839\n", + "[1327/1600][6/8] Loss_D: 0.7773 Loss_G: 1.8958 D(x): 0.5823 D(G(z)): 0.0780 / 0.0752\n", + "[1328/1600][0/8] Loss_D: 0.7099 Loss_G: 1.8830 D(x): 0.6132 D(G(z)): 0.0749 / 0.0759\n", + "[1328/1600][2/8] Loss_D: 0.7147 Loss_G: 1.9257 D(x): 0.6010 D(G(z)): 0.0694 / 0.0718\n", + "[1328/1600][4/8] Loss_D: 0.6902 Loss_G: 1.8500 D(x): 0.6407 D(G(z)): 0.0787 / 0.0793\n", + "[1328/1600][6/8] Loss_D: 0.7375 Loss_G: 1.9201 D(x): 0.6044 D(G(z)): 0.0745 / 0.0733\n", + "[1329/1600][0/8] Loss_D: 0.7720 Loss_G: 1.8697 D(x): 0.6275 D(G(z)): 0.0783 / 0.0770\n", + "[1329/1600][2/8] Loss_D: 0.7901 Loss_G: 1.8449 D(x): 0.6119 D(G(z)): 0.0841 / 0.0819\n", + "[1329/1600][4/8] Loss_D: 0.7440 Loss_G: 1.9573 D(x): 0.6276 D(G(z)): 0.0701 / 0.0680\n", + "[1329/1600][6/8] Loss_D: 0.7627 Loss_G: 1.9936 D(x): 0.6141 D(G(z)): 0.0677 / 0.0657\n", + "[1330/1600][0/8] Loss_D: 0.7458 Loss_G: 1.8921 D(x): 0.6102 D(G(z)): 0.0746 / 0.0758\n", + "[1330/1600][2/8] Loss_D: 0.7015 Loss_G: 1.8749 D(x): 0.5964 D(G(z)): 0.0756 / 0.0783\n", + "[1330/1600][4/8] Loss_D: 0.7307 Loss_G: 1.8668 D(x): 0.6179 D(G(z)): 0.0761 / 0.0775\n", + "[1330/1600][6/8] Loss_D: 0.7499 Loss_G: 1.7415 D(x): 0.6131 D(G(z)): 0.0938 / 0.0940\n", + "[1331/1600][0/8] Loss_D: 0.7606 Loss_G: 1.8407 D(x): 0.6247 D(G(z)): 0.0835 / 0.0810\n", + "[1331/1600][2/8] Loss_D: 0.7457 Loss_G: 1.9908 D(x): 0.6138 D(G(z)): 0.0666 / 0.0656\n", + "[1331/1600][4/8] Loss_D: 0.7778 Loss_G: 1.8587 D(x): 0.6180 D(G(z)): 0.0824 / 0.0803\n", + "[1331/1600][6/8] Loss_D: 0.7629 Loss_G: 1.8162 D(x): 0.6015 D(G(z)): 0.0837 / 0.0845\n", + "[1332/1600][0/8] Loss_D: 0.7744 Loss_G: 1.7773 D(x): 0.5966 D(G(z)): 0.0906 / 0.0903\n", + "[1332/1600][2/8] Loss_D: 0.7332 Loss_G: 1.8704 D(x): 0.6489 D(G(z)): 0.0785 / 0.0770\n", + "[1332/1600][4/8] Loss_D: 0.7922 Loss_G: 1.8358 D(x): 0.6561 D(G(z)): 0.0852 / 0.0818\n", + "[1332/1600][6/8] Loss_D: 0.6701 Loss_G: 1.9572 D(x): 0.6354 D(G(z)): 0.0685 / 0.0687\n", + "[1333/1600][0/8] Loss_D: 0.6902 Loss_G: 1.7984 D(x): 0.6242 D(G(z)): 0.0832 / 0.0872\n", + "[1333/1600][2/8] Loss_D: 0.7805 Loss_G: 1.8109 D(x): 0.6415 D(G(z)): 0.0857 / 0.0848\n", + "[1333/1600][4/8] Loss_D: 0.7963 Loss_G: 1.7666 D(x): 0.6040 D(G(z)): 0.0945 / 0.0914\n", + "[1333/1600][6/8] Loss_D: 0.7069 Loss_G: 1.9425 D(x): 0.6138 D(G(z)): 0.0711 / 0.0707\n", + "[1334/1600][0/8] Loss_D: 0.7186 Loss_G: 1.8557 D(x): 0.6551 D(G(z)): 0.0801 / 0.0799\n", + "[1334/1600][2/8] Loss_D: 0.7652 Loss_G: 1.8368 D(x): 0.6520 D(G(z)): 0.0845 / 0.0814\n", + "[1334/1600][4/8] Loss_D: 0.7502 Loss_G: 1.7896 D(x): 0.5834 D(G(z)): 0.0889 / 0.0899\n", + "[1334/1600][6/8] Loss_D: 0.7641 Loss_G: 1.9262 D(x): 0.6510 D(G(z)): 0.0750 / 0.0708\n", + "[1335/1600][0/8] Loss_D: 0.7575 Loss_G: 1.8691 D(x): 0.6045 D(G(z)): 0.0793 / 0.0776\n", + "[1335/1600][2/8] Loss_D: 0.6938 Loss_G: 1.9119 D(x): 0.6158 D(G(z)): 0.0704 / 0.0738\n", + "[1335/1600][4/8] Loss_D: 0.6708 Loss_G: 1.8758 D(x): 0.6564 D(G(z)): 0.0765 / 0.0781\n", + "[1335/1600][6/8] Loss_D: 0.7115 Loss_G: 1.8340 D(x): 0.6225 D(G(z)): 0.0800 / 0.0819\n", + "[1336/1600][0/8] Loss_D: 0.7386 Loss_G: 1.8231 D(x): 0.6161 D(G(z)): 0.0847 / 0.0837\n", + "[1336/1600][2/8] Loss_D: 0.7357 Loss_G: 1.9179 D(x): 0.6325 D(G(z)): 0.0740 / 0.0730\n", + "[1336/1600][4/8] Loss_D: 0.7198 Loss_G: 1.9065 D(x): 0.6070 D(G(z)): 0.0731 / 0.0742\n", + "[1336/1600][6/8] Loss_D: 0.7849 Loss_G: 1.8464 D(x): 0.5822 D(G(z)): 0.0829 / 0.0813\n", + "[1337/1600][0/8] Loss_D: 0.7854 Loss_G: 1.8194 D(x): 0.5658 D(G(z)): 0.0840 / 0.0842\n", + "[1337/1600][2/8] Loss_D: 0.7673 Loss_G: 1.8848 D(x): 0.6161 D(G(z)): 0.0760 / 0.0745\n", + "[1337/1600][4/8] Loss_D: 0.7454 Loss_G: 1.9052 D(x): 0.5820 D(G(z)): 0.0756 / 0.0750\n", + "[1337/1600][6/8] Loss_D: 0.7417 Loss_G: 1.8365 D(x): 0.6075 D(G(z)): 0.0811 / 0.0823\n", + "[1338/1600][0/8] Loss_D: 0.6863 Loss_G: 1.8317 D(x): 0.6440 D(G(z)): 0.0796 / 0.0818\n", + "[1338/1600][2/8] Loss_D: 0.7170 Loss_G: 1.8125 D(x): 0.6519 D(G(z)): 0.0855 / 0.0854\n", + "[1338/1600][4/8] Loss_D: 0.7310 Loss_G: 1.7707 D(x): 0.6800 D(G(z)): 0.0946 / 0.0933\n", + "[1338/1600][6/8] Loss_D: 0.7741 Loss_G: 1.7871 D(x): 0.6266 D(G(z)): 0.0918 / 0.0879\n", + "[1339/1600][0/8] Loss_D: 0.7050 Loss_G: 1.8030 D(x): 0.6075 D(G(z)): 0.0822 / 0.0857\n", + "[1339/1600][2/8] Loss_D: 0.7173 Loss_G: 1.7300 D(x): 0.6477 D(G(z)): 0.0939 / 0.0951\n", + "[1339/1600][4/8] Loss_D: 0.8110 Loss_G: 1.8445 D(x): 0.6635 D(G(z)): 0.0874 / 0.0807\n", + "[1339/1600][6/8] Loss_D: 0.7065 Loss_G: 1.8508 D(x): 0.6229 D(G(z)): 0.0806 / 0.0789\n", + "[1340/1600][0/8] Loss_D: 0.7759 Loss_G: 1.9210 D(x): 0.5421 D(G(z)): 0.0721 / 0.0720\n", + "[1340/1600][2/8] Loss_D: 0.7457 Loss_G: 1.9223 D(x): 0.5674 D(G(z)): 0.0699 / 0.0717\n", + "[1340/1600][4/8] Loss_D: 0.7325 Loss_G: 1.8834 D(x): 0.6162 D(G(z)): 0.0753 / 0.0772\n", + "[1340/1600][6/8] Loss_D: 0.7290 Loss_G: 1.8444 D(x): 0.6438 D(G(z)): 0.0826 / 0.0812\n", + "[1341/1600][0/8] Loss_D: 0.7426 Loss_G: 1.8737 D(x): 0.6040 D(G(z)): 0.0783 / 0.0777\n", + "[1341/1600][2/8] Loss_D: 0.7333 Loss_G: 1.8751 D(x): 0.6134 D(G(z)): 0.0758 / 0.0763\n", + "[1341/1600][4/8] Loss_D: 0.6882 Loss_G: 1.7239 D(x): 0.6575 D(G(z)): 0.0936 / 0.0970\n", + "[1341/1600][6/8] Loss_D: 0.7921 Loss_G: 1.7746 D(x): 0.6461 D(G(z)): 0.0925 / 0.0889\n", + "[1342/1600][0/8] Loss_D: 0.7808 Loss_G: 1.8720 D(x): 0.5996 D(G(z)): 0.0811 / 0.0780\n", + "[1342/1600][2/8] Loss_D: 0.6996 Loss_G: 1.8864 D(x): 0.6421 D(G(z)): 0.0759 / 0.0764\n", + "[1342/1600][4/8] Loss_D: 0.7705 Loss_G: 1.8078 D(x): 0.6381 D(G(z)): 0.0889 / 0.0866\n", + "[1342/1600][6/8] Loss_D: 0.6982 Loss_G: 1.8493 D(x): 0.6702 D(G(z)): 0.0847 / 0.0817\n", + "[1343/1600][0/8] Loss_D: 0.6971 Loss_G: 1.9781 D(x): 0.6533 D(G(z)): 0.0676 / 0.0665\n", + "[1343/1600][2/8] Loss_D: 0.7641 Loss_G: 1.8342 D(x): 0.6172 D(G(z)): 0.0839 / 0.0828\n", + "[1343/1600][4/8] Loss_D: 0.7599 Loss_G: 1.8964 D(x): 0.5710 D(G(z)): 0.0748 / 0.0756\n", + "[1343/1600][6/8] Loss_D: 0.6854 Loss_G: 1.9489 D(x): 0.6193 D(G(z)): 0.0664 / 0.0688\n", + "[1344/1600][0/8] Loss_D: 0.7614 Loss_G: 1.7577 D(x): 0.5633 D(G(z)): 0.0859 / 0.0914\n", + "[1344/1600][2/8] Loss_D: 0.7025 Loss_G: 1.8007 D(x): 0.6552 D(G(z)): 0.0843 / 0.0859\n", + "[1344/1600][4/8] Loss_D: 0.7857 Loss_G: 1.7763 D(x): 0.7064 D(G(z)): 0.0928 / 0.0883\n", + "[1344/1600][6/8] Loss_D: 0.7565 Loss_G: 1.8633 D(x): 0.6149 D(G(z)): 0.0820 / 0.0785\n", + "[1345/1600][0/8] Loss_D: 0.7193 Loss_G: 1.7539 D(x): 0.6106 D(G(z)): 0.0883 / 0.0912\n", + "[1345/1600][2/8] Loss_D: 0.7006 Loss_G: 1.8203 D(x): 0.6678 D(G(z)): 0.0817 / 0.0829\n", + "[1345/1600][4/8] Loss_D: 0.7805 Loss_G: 1.8326 D(x): 0.6514 D(G(z)): 0.0866 / 0.0813\n", + "[1345/1600][6/8] Loss_D: 0.7598 Loss_G: 1.9086 D(x): 0.6128 D(G(z)): 0.0763 / 0.0735\n", + "[1346/1600][0/8] Loss_D: 0.7737 Loss_G: 1.9866 D(x): 0.5775 D(G(z)): 0.0697 / 0.0669\n", + "[1346/1600][2/8] Loss_D: 0.7360 Loss_G: 1.9335 D(x): 0.5966 D(G(z)): 0.0703 / 0.0701\n", + "[1346/1600][4/8] Loss_D: 0.7885 Loss_G: 1.8595 D(x): 0.5549 D(G(z)): 0.0780 / 0.0787\n", + "[1346/1600][6/8] Loss_D: 0.7637 Loss_G: 1.9343 D(x): 0.6102 D(G(z)): 0.0721 / 0.0710\n", + "[1347/1600][0/8] Loss_D: 0.7132 Loss_G: 2.0227 D(x): 0.6066 D(G(z)): 0.0620 / 0.0620\n", + "[1347/1600][2/8] Loss_D: 0.7085 Loss_G: 1.9047 D(x): 0.6138 D(G(z)): 0.0721 / 0.0746\n", + "[1347/1600][4/8] Loss_D: 0.7080 Loss_G: 1.8638 D(x): 0.5943 D(G(z)): 0.0723 / 0.0781\n", + "[1347/1600][6/8] Loss_D: 0.7465 Loss_G: 1.7517 D(x): 0.6198 D(G(z)): 0.0915 / 0.0929\n", + "[1348/1600][0/8] Loss_D: 0.7487 Loss_G: 1.7319 D(x): 0.6331 D(G(z)): 0.0973 / 0.0958\n", + "[1348/1600][2/8] Loss_D: 0.7767 Loss_G: 1.8829 D(x): 0.5986 D(G(z)): 0.0803 / 0.0768\n", + "[1348/1600][4/8] Loss_D: 0.7246 Loss_G: 1.8344 D(x): 0.6058 D(G(z)): 0.0815 / 0.0832\n", + "[1348/1600][6/8] Loss_D: 0.7822 Loss_G: 1.7913 D(x): 0.6225 D(G(z)): 0.0923 / 0.0884\n", + "[1349/1600][0/8] Loss_D: 0.7429 Loss_G: 1.8737 D(x): 0.6091 D(G(z)): 0.0784 / 0.0775\n", + "[1349/1600][2/8] Loss_D: 0.7632 Loss_G: 1.8637 D(x): 0.5540 D(G(z)): 0.0773 / 0.0788\n", + "[1349/1600][4/8] Loss_D: 0.7333 Loss_G: 1.8419 D(x): 0.6645 D(G(z)): 0.0798 / 0.0803\n", + "[1349/1600][6/8] Loss_D: 0.6770 Loss_G: 1.8248 D(x): 0.6488 D(G(z)): 0.0803 / 0.0821\n", + "[1350/1600][0/8] Loss_D: 0.7856 Loss_G: 1.8394 D(x): 0.6240 D(G(z)): 0.0871 / 0.0832\n", + "[1350/1600][2/8] Loss_D: 0.7434 Loss_G: 1.8174 D(x): 0.5980 D(G(z)): 0.0855 / 0.0860\n", + "[1350/1600][4/8] Loss_D: 0.7339 Loss_G: 1.8522 D(x): 0.6193 D(G(z)): 0.0804 / 0.0811\n", + "[1350/1600][6/8] Loss_D: 0.6823 Loss_G: 1.8445 D(x): 0.6496 D(G(z)): 0.0801 / 0.0811\n", + "[1351/1600][0/8] Loss_D: 0.7075 Loss_G: 1.7100 D(x): 0.6109 D(G(z)): 0.0932 / 0.0987\n", + "[1351/1600][2/8] Loss_D: 0.8122 Loss_G: 1.7540 D(x): 0.6732 D(G(z)): 0.0971 / 0.0918\n", + "[1351/1600][4/8] Loss_D: 0.7622 Loss_G: 1.8143 D(x): 0.6275 D(G(z)): 0.0888 / 0.0849\n", + "[1351/1600][6/8] Loss_D: 0.7924 Loss_G: 1.8471 D(x): 0.6301 D(G(z)): 0.0853 / 0.0815\n", + "[1352/1600][0/8] Loss_D: 0.7657 Loss_G: 1.8297 D(x): 0.6125 D(G(z)): 0.0844 / 0.0835\n", + "[1352/1600][2/8] Loss_D: 0.7768 Loss_G: 1.9015 D(x): 0.6201 D(G(z)): 0.0798 / 0.0755\n", + "[1352/1600][4/8] Loss_D: 0.7213 Loss_G: 1.9729 D(x): 0.5975 D(G(z)): 0.0654 / 0.0674\n", + "[1352/1600][6/8] Loss_D: 0.7473 Loss_G: 1.7749 D(x): 0.6077 D(G(z)): 0.0872 / 0.0901\n", + "[1353/1600][0/8] Loss_D: 0.7088 Loss_G: 1.7842 D(x): 0.6791 D(G(z)): 0.0882 / 0.0894\n", + "[1353/1600][2/8] Loss_D: 0.8028 Loss_G: 1.8339 D(x): 0.6355 D(G(z)): 0.0882 / 0.0824\n", + "[1353/1600][4/8] Loss_D: 0.6910 Loss_G: 1.7725 D(x): 0.6273 D(G(z)): 0.0877 / 0.0896\n", + "[1353/1600][6/8] Loss_D: 0.7710 Loss_G: 1.8729 D(x): 0.6554 D(G(z)): 0.0819 / 0.0783\n", + "[1354/1600][0/8] Loss_D: 0.6768 Loss_G: 1.7527 D(x): 0.6495 D(G(z)): 0.0916 / 0.0942\n", + "[1354/1600][2/8] Loss_D: 0.7436 Loss_G: 1.8309 D(x): 0.5896 D(G(z)): 0.0810 / 0.0821\n", + "[1354/1600][4/8] Loss_D: 0.7378 Loss_G: 1.7728 D(x): 0.6772 D(G(z)): 0.0914 / 0.0909\n", + "[1354/1600][6/8] Loss_D: 0.7967 Loss_G: 1.7705 D(x): 0.6631 D(G(z)): 0.0931 / 0.0892\n", + "[1355/1600][0/8] Loss_D: 0.7917 Loss_G: 1.8925 D(x): 0.5966 D(G(z)): 0.0797 / 0.0755\n", + "[1355/1600][2/8] Loss_D: 0.7700 Loss_G: 1.9170 D(x): 0.5992 D(G(z)): 0.0750 / 0.0738\n", + "[1355/1600][4/8] Loss_D: 0.7010 Loss_G: 1.9410 D(x): 0.6356 D(G(z)): 0.0713 / 0.0713\n", + "[1355/1600][6/8] Loss_D: 0.7869 Loss_G: 1.8622 D(x): 0.5640 D(G(z)): 0.0814 / 0.0801\n", + "[1356/1600][0/8] Loss_D: 0.7010 Loss_G: 1.8846 D(x): 0.6338 D(G(z)): 0.0751 / 0.0762\n", + "[1356/1600][2/8] Loss_D: 0.7775 Loss_G: 1.9051 D(x): 0.6038 D(G(z)): 0.0752 / 0.0747\n", + "[1356/1600][4/8] Loss_D: 0.7252 Loss_G: 1.8872 D(x): 0.5928 D(G(z)): 0.0737 / 0.0748\n", + "[1356/1600][6/8] Loss_D: 0.7603 Loss_G: 1.8617 D(x): 0.6139 D(G(z)): 0.0792 / 0.0795\n", + "[1357/1600][0/8] Loss_D: 0.7237 Loss_G: 1.8644 D(x): 0.6575 D(G(z)): 0.0840 / 0.0810\n", + "[1357/1600][2/8] Loss_D: 0.7812 Loss_G: 1.9340 D(x): 0.6124 D(G(z)): 0.0772 / 0.0731\n", + "[1357/1600][4/8] Loss_D: 0.7381 Loss_G: 1.8579 D(x): 0.5663 D(G(z)): 0.0752 / 0.0797\n", + "[1357/1600][6/8] Loss_D: 0.7367 Loss_G: 1.7942 D(x): 0.6636 D(G(z)): 0.0876 / 0.0880\n", + "[1358/1600][0/8] Loss_D: 0.6761 Loss_G: 1.8004 D(x): 0.6746 D(G(z)): 0.0853 / 0.0859\n", + "[1358/1600][2/8] Loss_D: 0.7772 Loss_G: 1.7842 D(x): 0.6505 D(G(z)): 0.0908 / 0.0890\n", + "[1358/1600][4/8] Loss_D: 0.7429 Loss_G: 1.7784 D(x): 0.6536 D(G(z)): 0.0924 / 0.0894\n", + "[1358/1600][6/8] Loss_D: 0.7515 Loss_G: 1.9426 D(x): 0.5893 D(G(z)): 0.0708 / 0.0693\n", + "[1359/1600][0/8] Loss_D: 0.7219 Loss_G: 1.8765 D(x): 0.6124 D(G(z)): 0.0771 / 0.0779\n", + "[1359/1600][2/8] Loss_D: 0.7436 Loss_G: 1.8291 D(x): 0.6425 D(G(z)): 0.0840 / 0.0840\n", + "[1359/1600][4/8] Loss_D: 0.7594 Loss_G: 1.8690 D(x): 0.5874 D(G(z)): 0.0787 / 0.0784\n", + "[1359/1600][6/8] Loss_D: 0.7928 Loss_G: 1.8472 D(x): 0.5915 D(G(z)): 0.0844 / 0.0810\n", + "[1360/1600][0/8] Loss_D: 0.7501 Loss_G: 1.9049 D(x): 0.6365 D(G(z)): 0.0750 / 0.0729\n", + "[1360/1600][2/8] Loss_D: 0.7759 Loss_G: 1.9625 D(x): 0.5726 D(G(z)): 0.0711 / 0.0690\n", + "[1360/1600][4/8] Loss_D: 0.7206 Loss_G: 1.9738 D(x): 0.5850 D(G(z)): 0.0662 / 0.0674\n", + "[1360/1600][6/8] Loss_D: 0.7410 Loss_G: 1.8539 D(x): 0.5636 D(G(z)): 0.0747 / 0.0794\n", + "[1361/1600][0/8] Loss_D: 0.6789 Loss_G: 1.8819 D(x): 0.6262 D(G(z)): 0.0745 / 0.0777\n", + "[1361/1600][2/8] Loss_D: 0.7210 Loss_G: 1.8293 D(x): 0.6585 D(G(z)): 0.0812 / 0.0820\n", + "[1361/1600][4/8] Loss_D: 0.7675 Loss_G: 1.7377 D(x): 0.6239 D(G(z)): 0.0942 / 0.0944\n", + "[1361/1600][6/8] Loss_D: 0.7320 Loss_G: 1.8358 D(x): 0.6056 D(G(z)): 0.0821 / 0.0825\n", + "[1362/1600][0/8] Loss_D: 0.6970 Loss_G: 1.8302 D(x): 0.6240 D(G(z)): 0.0821 / 0.0823\n", + "[1362/1600][2/8] Loss_D: 0.7610 Loss_G: 1.8077 D(x): 0.6130 D(G(z)): 0.0875 / 0.0852\n", + "[1362/1600][4/8] Loss_D: 0.7110 Loss_G: 1.8137 D(x): 0.6318 D(G(z)): 0.0819 / 0.0840\n", + "[1362/1600][6/8] Loss_D: 0.7186 Loss_G: 1.7717 D(x): 0.6325 D(G(z)): 0.0878 / 0.0887\n", + "[1363/1600][0/8] Loss_D: 0.7694 Loss_G: 1.8155 D(x): 0.6264 D(G(z)): 0.0865 / 0.0846\n", + "[1363/1600][2/8] Loss_D: 0.7902 Loss_G: 1.8305 D(x): 0.6246 D(G(z)): 0.0850 / 0.0821\n", + "[1363/1600][4/8] Loss_D: 0.7442 Loss_G: 1.8524 D(x): 0.6105 D(G(z)): 0.0800 / 0.0805\n", + "[1363/1600][6/8] Loss_D: 0.7871 Loss_G: 1.7911 D(x): 0.6372 D(G(z)): 0.0925 / 0.0881\n", + "[1364/1600][0/8] Loss_D: 0.7808 Loss_G: 1.8964 D(x): 0.6215 D(G(z)): 0.0803 / 0.0750\n", + "[1364/1600][2/8] Loss_D: 0.7580 Loss_G: 1.9316 D(x): 0.5784 D(G(z)): 0.0732 / 0.0711\n", + "[1364/1600][4/8] Loss_D: 0.7661 Loss_G: 2.0670 D(x): 0.5385 D(G(z)): 0.0574 / 0.0582\n", + "[1364/1600][6/8] Loss_D: 0.7915 Loss_G: 1.8693 D(x): 0.5624 D(G(z)): 0.0792 / 0.0806\n", + "[1365/1600][0/8] Loss_D: 0.7693 Loss_G: 1.8794 D(x): 0.6050 D(G(z)): 0.0772 / 0.0768\n", + "[1365/1600][2/8] Loss_D: 0.7483 Loss_G: 1.9175 D(x): 0.5608 D(G(z)): 0.0681 / 0.0716\n", + "[1365/1600][4/8] Loss_D: 0.7500 Loss_G: 1.8559 D(x): 0.6310 D(G(z)): 0.0795 / 0.0795\n", + "[1365/1600][6/8] Loss_D: 0.7344 Loss_G: 1.7644 D(x): 0.6094 D(G(z)): 0.0893 / 0.0926\n", + "[1366/1600][0/8] Loss_D: 0.7618 Loss_G: 1.7381 D(x): 0.6541 D(G(z)): 0.0981 / 0.0949\n", + "[1366/1600][2/8] Loss_D: 0.7117 Loss_G: 1.8045 D(x): 0.6185 D(G(z)): 0.0845 / 0.0847\n", + "[1366/1600][4/8] Loss_D: 0.7950 Loss_G: 1.8759 D(x): 0.6672 D(G(z)): 0.0833 / 0.0784\n", + "[1366/1600][6/8] Loss_D: 0.7644 Loss_G: 1.8605 D(x): 0.5530 D(G(z)): 0.0784 / 0.0788\n", + "[1367/1600][0/8] Loss_D: 0.7737 Loss_G: 1.8830 D(x): 0.5801 D(G(z)): 0.0765 / 0.0770\n", + "[1367/1600][2/8] Loss_D: 0.7145 Loss_G: 1.8754 D(x): 0.6244 D(G(z)): 0.0760 / 0.0763\n", + "[1367/1600][4/8] Loss_D: 0.7382 Loss_G: 1.8694 D(x): 0.5976 D(G(z)): 0.0767 / 0.0775\n", + "[1367/1600][6/8] Loss_D: 0.7483 Loss_G: 1.7982 D(x): 0.6501 D(G(z)): 0.0887 / 0.0878\n", + "[1368/1600][0/8] Loss_D: 0.7390 Loss_G: 1.8945 D(x): 0.6151 D(G(z)): 0.0768 / 0.0766\n", + "[1368/1600][2/8] Loss_D: 0.7182 Loss_G: 1.8587 D(x): 0.6125 D(G(z)): 0.0775 / 0.0790\n", + "[1368/1600][4/8] Loss_D: 0.7328 Loss_G: 1.7786 D(x): 0.6557 D(G(z)): 0.0894 / 0.0902\n", + "[1368/1600][6/8] Loss_D: 0.8002 Loss_G: 1.7015 D(x): 0.6302 D(G(z)): 0.1026 / 0.0986\n", + "[1369/1600][0/8] Loss_D: 0.7145 Loss_G: 1.7630 D(x): 0.6204 D(G(z)): 0.0899 / 0.0913\n", + "[1369/1600][2/8] Loss_D: 0.6691 Loss_G: 1.7594 D(x): 0.6545 D(G(z)): 0.0866 / 0.0896\n", + "[1369/1600][4/8] Loss_D: 0.8060 Loss_G: 1.7534 D(x): 0.6347 D(G(z)): 0.0992 / 0.0941\n", + "[1369/1600][6/8] Loss_D: 0.7468 Loss_G: 1.8241 D(x): 0.6290 D(G(z)): 0.0869 / 0.0843\n", + "[1370/1600][0/8] Loss_D: 0.7117 Loss_G: 1.8172 D(x): 0.6399 D(G(z)): 0.0842 / 0.0849\n", + "[1370/1600][2/8] Loss_D: 0.7683 Loss_G: 1.8037 D(x): 0.5873 D(G(z)): 0.0868 / 0.0858\n", + "[1370/1600][4/8] Loss_D: 0.7093 Loss_G: 1.7741 D(x): 0.6201 D(G(z)): 0.0878 / 0.0893\n", + "[1370/1600][6/8] Loss_D: 0.7280 Loss_G: 1.7454 D(x): 0.6303 D(G(z)): 0.0922 / 0.0940\n", + "[1371/1600][0/8] Loss_D: 0.7866 Loss_G: 1.7840 D(x): 0.6247 D(G(z)): 0.0914 / 0.0884\n", + "[1371/1600][2/8] Loss_D: 0.7200 Loss_G: 1.7927 D(x): 0.6319 D(G(z)): 0.0894 / 0.0879\n", + "[1371/1600][4/8] Loss_D: 0.7565 Loss_G: 1.8222 D(x): 0.6050 D(G(z)): 0.0851 / 0.0833\n", + "[1371/1600][6/8] Loss_D: 0.7976 Loss_G: 1.8440 D(x): 0.5321 D(G(z)): 0.0827 / 0.0820\n", + "[1372/1600][0/8] Loss_D: 0.7618 Loss_G: 1.7507 D(x): 0.5993 D(G(z)): 0.0944 / 0.0951\n", + "[1372/1600][2/8] Loss_D: 0.7869 Loss_G: 1.8285 D(x): 0.6471 D(G(z)): 0.0857 / 0.0828\n", + "[1372/1600][4/8] Loss_D: 0.7241 Loss_G: 1.8141 D(x): 0.6050 D(G(z)): 0.0830 / 0.0844\n", + "[1372/1600][6/8] Loss_D: 0.7325 Loss_G: 1.7672 D(x): 0.6392 D(G(z)): 0.0891 / 0.0912\n", + "[1373/1600][0/8] Loss_D: 0.7319 Loss_G: 1.7865 D(x): 0.6315 D(G(z)): 0.0878 / 0.0872\n", + "[1373/1600][2/8] Loss_D: 0.6705 Loss_G: 1.7813 D(x): 0.6810 D(G(z)): 0.0902 / 0.0899\n", + "[1373/1600][4/8] Loss_D: 0.7984 Loss_G: 1.8096 D(x): 0.6090 D(G(z)): 0.0874 / 0.0847\n", + "[1373/1600][6/8] Loss_D: 0.7348 Loss_G: 1.8425 D(x): 0.5844 D(G(z)): 0.0798 / 0.0807\n", + "[1374/1600][0/8] Loss_D: 0.6833 Loss_G: 1.8167 D(x): 0.6446 D(G(z)): 0.0822 / 0.0836\n", + "[1374/1600][2/8] Loss_D: 0.6989 Loss_G: 1.7799 D(x): 0.6273 D(G(z)): 0.0862 / 0.0884\n", + "[1374/1600][4/8] Loss_D: 0.7810 Loss_G: 1.7734 D(x): 0.6349 D(G(z)): 0.0918 / 0.0893\n", + "[1374/1600][6/8] Loss_D: 0.7752 Loss_G: 1.7720 D(x): 0.6043 D(G(z)): 0.0910 / 0.0895\n", + "[1375/1600][0/8] Loss_D: 0.7640 Loss_G: 1.8100 D(x): 0.6263 D(G(z)): 0.0891 / 0.0859\n", + "[1375/1600][2/8] Loss_D: 0.7483 Loss_G: 1.8159 D(x): 0.6246 D(G(z)): 0.0846 / 0.0839\n", + "[1375/1600][4/8] Loss_D: 0.7423 Loss_G: 1.8532 D(x): 0.6501 D(G(z)): 0.0813 / 0.0801\n", + "[1375/1600][6/8] Loss_D: 0.7196 Loss_G: 1.8304 D(x): 0.6230 D(G(z)): 0.0833 / 0.0823\n", + "[1376/1600][0/8] Loss_D: 0.7198 Loss_G: 1.7425 D(x): 0.6291 D(G(z)): 0.0909 / 0.0933\n", + "[1376/1600][2/8] Loss_D: 0.7249 Loss_G: 1.7998 D(x): 0.6317 D(G(z)): 0.0861 / 0.0859\n", + "[1376/1600][4/8] Loss_D: 0.7649 Loss_G: 1.7193 D(x): 0.5851 D(G(z)): 0.0959 / 0.0971\n", + "[1376/1600][6/8] Loss_D: 0.7552 Loss_G: 1.6820 D(x): 0.6211 D(G(z)): 0.1012 / 0.1022\n", + "[1377/1600][0/8] Loss_D: 0.6975 Loss_G: 1.7878 D(x): 0.6610 D(G(z)): 0.0906 / 0.0889\n", + "[1377/1600][2/8] Loss_D: 0.7224 Loss_G: 1.7319 D(x): 0.6219 D(G(z)): 0.0930 / 0.0950\n", + "[1377/1600][4/8] Loss_D: 0.7816 Loss_G: 1.6859 D(x): 0.6220 D(G(z)): 0.1044 / 0.1013\n", + "[1377/1600][6/8] Loss_D: 0.7018 Loss_G: 1.8132 D(x): 0.6418 D(G(z)): 0.0874 / 0.0861\n", + "[1378/1600][0/8] Loss_D: 0.7374 Loss_G: 1.8505 D(x): 0.5982 D(G(z)): 0.0800 / 0.0801\n", + "[1378/1600][2/8] Loss_D: 0.7357 Loss_G: 1.7964 D(x): 0.6305 D(G(z)): 0.0860 / 0.0868\n", + "[1378/1600][4/8] Loss_D: 0.7253 Loss_G: 1.7557 D(x): 0.6204 D(G(z)): 0.0922 / 0.0925\n", + "[1378/1600][6/8] Loss_D: 0.7183 Loss_G: 1.7875 D(x): 0.6211 D(G(z)): 0.0869 / 0.0878\n", + "[1379/1600][0/8] Loss_D: 0.7101 Loss_G: 1.8421 D(x): 0.5952 D(G(z)): 0.0793 / 0.0814\n", + "[1379/1600][2/8] Loss_D: 0.7558 Loss_G: 1.7459 D(x): 0.6360 D(G(z)): 0.0941 / 0.0934\n", + "[1379/1600][4/8] Loss_D: 0.7746 Loss_G: 1.7901 D(x): 0.6121 D(G(z)): 0.0903 / 0.0881\n", + "[1379/1600][6/8] Loss_D: 0.7487 Loss_G: 1.7955 D(x): 0.6425 D(G(z)): 0.0887 / 0.0863\n", + "[1380/1600][0/8] Loss_D: 0.7567 Loss_G: 1.7445 D(x): 0.5764 D(G(z)): 0.0928 / 0.0935\n", + "[1380/1600][2/8] Loss_D: 0.6879 Loss_G: 1.7984 D(x): 0.6393 D(G(z)): 0.0855 / 0.0887\n", + "[1380/1600][4/8] Loss_D: 0.7809 Loss_G: 1.8363 D(x): 0.6694 D(G(z)): 0.0856 / 0.0811\n", + "[1380/1600][6/8] Loss_D: 0.7591 Loss_G: 1.8888 D(x): 0.5958 D(G(z)): 0.0771 / 0.0767\n", + "[1381/1600][0/8] Loss_D: 0.6828 Loss_G: 1.8293 D(x): 0.6516 D(G(z)): 0.0813 / 0.0828\n", + "[1381/1600][2/8] Loss_D: 0.7725 Loss_G: 1.8498 D(x): 0.6674 D(G(z)): 0.0832 / 0.0802\n", + "[1381/1600][4/8] Loss_D: 0.7332 Loss_G: 1.8518 D(x): 0.6469 D(G(z)): 0.0827 / 0.0802\n", + "[1381/1600][6/8] Loss_D: 0.7650 Loss_G: 1.8135 D(x): 0.5929 D(G(z)): 0.0849 / 0.0843\n", + "[1382/1600][0/8] Loss_D: 0.7487 Loss_G: 1.8458 D(x): 0.5841 D(G(z)): 0.0777 / 0.0808\n", + "[1382/1600][2/8] Loss_D: 0.7981 Loss_G: 1.7894 D(x): 0.6131 D(G(z)): 0.0914 / 0.0889\n", + "[1382/1600][4/8] Loss_D: 0.7763 Loss_G: 1.9117 D(x): 0.6569 D(G(z)): 0.0784 / 0.0733\n", + "[1382/1600][6/8] Loss_D: 0.7376 Loss_G: 1.8672 D(x): 0.5863 D(G(z)): 0.0793 / 0.0787\n", + "[1383/1600][0/8] Loss_D: 0.7714 Loss_G: 1.8778 D(x): 0.5533 D(G(z)): 0.0737 / 0.0750\n", + "[1383/1600][2/8] Loss_D: 0.7145 Loss_G: 1.7824 D(x): 0.6128 D(G(z)): 0.0832 / 0.0887\n", + "[1383/1600][4/8] Loss_D: 0.6916 Loss_G: 1.7812 D(x): 0.6470 D(G(z)): 0.0838 / 0.0880\n", + "[1383/1600][6/8] Loss_D: 0.6995 Loss_G: 1.7376 D(x): 0.6839 D(G(z)): 0.0950 / 0.0955\n", + "[1384/1600][0/8] Loss_D: 0.7615 Loss_G: 1.7765 D(x): 0.6259 D(G(z)): 0.0923 / 0.0889\n", + "[1384/1600][2/8] Loss_D: 0.7536 Loss_G: 1.7795 D(x): 0.6491 D(G(z)): 0.0905 / 0.0881\n", + "[1384/1600][4/8] Loss_D: 0.7833 Loss_G: 1.7987 D(x): 0.6440 D(G(z)): 0.0929 / 0.0875\n", + "[1384/1600][6/8] Loss_D: 0.7675 Loss_G: 1.8455 D(x): 0.5689 D(G(z)): 0.0829 / 0.0833\n", + "[1385/1600][0/8] Loss_D: 0.6711 Loss_G: 1.8814 D(x): 0.6310 D(G(z)): 0.0744 / 0.0758\n", + "[1385/1600][2/8] Loss_D: 0.7384 Loss_G: 1.8135 D(x): 0.5833 D(G(z)): 0.0827 / 0.0855\n", + "[1385/1600][4/8] Loss_D: 0.7193 Loss_G: 1.7791 D(x): 0.5911 D(G(z)): 0.0866 / 0.0916\n", + "[1385/1600][6/8] Loss_D: 0.7127 Loss_G: 1.7169 D(x): 0.6564 D(G(z)): 0.0965 / 0.0976\n", + "[1386/1600][0/8] Loss_D: 0.7728 Loss_G: 1.7340 D(x): 0.5911 D(G(z)): 0.0977 / 0.0959\n", + "[1386/1600][2/8] Loss_D: 0.7824 Loss_G: 1.7575 D(x): 0.6514 D(G(z)): 0.0984 / 0.0924\n", + "[1386/1600][4/8] Loss_D: 0.7842 Loss_G: 1.8559 D(x): 0.6132 D(G(z)): 0.0848 / 0.0796\n", + "[1386/1600][6/8] Loss_D: 0.7725 Loss_G: 2.0187 D(x): 0.5569 D(G(z)): 0.0633 / 0.0619\n", + "[1387/1600][0/8] Loss_D: 0.7596 Loss_G: 1.8913 D(x): 0.6070 D(G(z)): 0.0761 / 0.0763\n", + "[1387/1600][2/8] Loss_D: 0.7393 Loss_G: 1.8369 D(x): 0.5494 D(G(z)): 0.0762 / 0.0829\n", + "[1387/1600][4/8] Loss_D: 0.6888 Loss_G: 1.8235 D(x): 0.6416 D(G(z)): 0.0806 / 0.0836\n", + "[1387/1600][6/8] Loss_D: 0.6990 Loss_G: 1.7308 D(x): 0.6395 D(G(z)): 0.0939 / 0.0956\n", + "[1388/1600][0/8] Loss_D: 0.7983 Loss_G: 1.7047 D(x): 0.5933 D(G(z)): 0.1022 / 0.0980\n", + "[1388/1600][2/8] Loss_D: 0.7829 Loss_G: 1.8430 D(x): 0.6225 D(G(z)): 0.0881 / 0.0826\n", + "[1388/1600][4/8] Loss_D: 0.7797 Loss_G: 1.8903 D(x): 0.5937 D(G(z)): 0.0799 / 0.0768\n", + "[1388/1600][6/8] Loss_D: 0.7239 Loss_G: 1.9111 D(x): 0.6105 D(G(z)): 0.0732 / 0.0743\n", + "[1389/1600][0/8] Loss_D: 0.7725 Loss_G: 1.9346 D(x): 0.5263 D(G(z)): 0.0677 / 0.0708\n", + "[1389/1600][2/8] Loss_D: 0.7707 Loss_G: 1.9015 D(x): 0.6236 D(G(z)): 0.0785 / 0.0767\n", + "[1389/1600][4/8] Loss_D: 0.7913 Loss_G: 1.8307 D(x): 0.6287 D(G(z)): 0.0847 / 0.0825\n", + "[1389/1600][6/8] Loss_D: 0.7677 Loss_G: 1.8316 D(x): 0.6040 D(G(z)): 0.0842 / 0.0831\n", + "[1390/1600][0/8] Loss_D: 0.7483 Loss_G: 1.8870 D(x): 0.6045 D(G(z)): 0.0769 / 0.0761\n", + "[1390/1600][2/8] Loss_D: 0.7422 Loss_G: 1.8622 D(x): 0.6002 D(G(z)): 0.0778 / 0.0803\n", + "[1390/1600][4/8] Loss_D: 0.7739 Loss_G: 1.8155 D(x): 0.6217 D(G(z)): 0.0887 / 0.0852\n", + "[1390/1600][6/8] Loss_D: 0.6906 Loss_G: 1.9530 D(x): 0.6257 D(G(z)): 0.0692 / 0.0695\n", + "[1391/1600][0/8] Loss_D: 0.7333 Loss_G: 1.8259 D(x): 0.5753 D(G(z)): 0.0778 / 0.0828\n", + "[1391/1600][2/8] Loss_D: 0.7373 Loss_G: 1.6831 D(x): 0.6069 D(G(z)): 0.0978 / 0.1025\n", + "[1391/1600][4/8] Loss_D: 0.7080 Loss_G: 1.6775 D(x): 0.6508 D(G(z)): 0.1027 / 0.1040\n", + "[1391/1600][6/8] Loss_D: 0.7277 Loss_G: 1.7409 D(x): 0.6704 D(G(z)): 0.0967 / 0.0945\n", + "[1392/1600][0/8] Loss_D: 0.7187 Loss_G: 1.8213 D(x): 0.6175 D(G(z)): 0.0849 / 0.0834\n", + "[1392/1600][2/8] Loss_D: 0.6958 Loss_G: 1.8194 D(x): 0.6467 D(G(z)): 0.0888 / 0.0870\n", + "[1392/1600][4/8] Loss_D: 0.7358 Loss_G: 1.8607 D(x): 0.5879 D(G(z)): 0.0784 / 0.0795\n", + "[1392/1600][6/8] Loss_D: 0.7559 Loss_G: 1.7515 D(x): 0.6272 D(G(z)): 0.0934 / 0.0941\n", + "[1393/1600][0/8] Loss_D: 0.7396 Loss_G: 1.7307 D(x): 0.6380 D(G(z)): 0.0964 / 0.0973\n", + "[1393/1600][2/8] Loss_D: 0.7778 Loss_G: 1.7251 D(x): 0.6380 D(G(z)): 0.0990 / 0.0976\n", + "[1393/1600][4/8] Loss_D: 0.7222 Loss_G: 1.7886 D(x): 0.6906 D(G(z)): 0.0916 / 0.0889\n", + "[1393/1600][6/8] Loss_D: 0.7075 Loss_G: 1.6982 D(x): 0.6566 D(G(z)): 0.1019 / 0.1012\n", + "[1394/1600][0/8] Loss_D: 0.7253 Loss_G: 1.7641 D(x): 0.6110 D(G(z)): 0.0898 / 0.0924\n", + "[1394/1600][2/8] Loss_D: 0.7418 Loss_G: 1.7918 D(x): 0.6495 D(G(z)): 0.0898 / 0.0867\n", + "[1394/1600][4/8] Loss_D: 0.7481 Loss_G: 1.7558 D(x): 0.5883 D(G(z)): 0.0930 / 0.0928\n", + "[1394/1600][6/8] Loss_D: 0.7696 Loss_G: 1.8358 D(x): 0.6233 D(G(z)): 0.0849 / 0.0822\n", + "[1395/1600][0/8] Loss_D: 0.7427 Loss_G: 1.9258 D(x): 0.6113 D(G(z)): 0.0724 / 0.0717\n", + "[1395/1600][2/8] Loss_D: 0.7925 Loss_G: 1.7278 D(x): 0.5228 D(G(z)): 0.0884 / 0.0961\n", + "[1395/1600][4/8] Loss_D: 0.7875 Loss_G: 1.8402 D(x): 0.6493 D(G(z)): 0.0842 / 0.0814\n", + "[1395/1600][6/8] Loss_D: 0.7294 Loss_G: 1.8008 D(x): 0.6054 D(G(z)): 0.0866 / 0.0891\n", + "[1396/1600][0/8] Loss_D: 0.7433 Loss_G: 1.7363 D(x): 0.6666 D(G(z)): 0.0959 / 0.0935\n", + "[1396/1600][2/8] Loss_D: 0.7487 Loss_G: 1.7366 D(x): 0.6328 D(G(z)): 0.0998 / 0.0970\n", + "[1396/1600][4/8] Loss_D: 0.7966 Loss_G: 1.8434 D(x): 0.6351 D(G(z)): 0.0875 / 0.0828\n", + "[1396/1600][6/8] Loss_D: 0.7631 Loss_G: 1.7792 D(x): 0.6186 D(G(z)): 0.0903 / 0.0895\n", + "[1397/1600][0/8] Loss_D: 0.7093 Loss_G: 1.8150 D(x): 0.6117 D(G(z)): 0.0814 / 0.0849\n", + "[1397/1600][2/8] Loss_D: 0.7708 Loss_G: 1.7772 D(x): 0.5941 D(G(z)): 0.0879 / 0.0896\n", + "[1397/1600][4/8] Loss_D: 0.7936 Loss_G: 1.7070 D(x): 0.6105 D(G(z)): 0.1027 / 0.0992\n", + "[1397/1600][6/8] Loss_D: 0.7707 Loss_G: 1.8316 D(x): 0.5987 D(G(z)): 0.0883 / 0.0846\n", + "[1398/1600][0/8] Loss_D: 0.6984 Loss_G: 1.7904 D(x): 0.6371 D(G(z)): 0.0904 / 0.0899\n", + "[1398/1600][2/8] Loss_D: 0.7730 Loss_G: 1.8719 D(x): 0.5404 D(G(z)): 0.0746 / 0.0774\n", + "[1398/1600][4/8] Loss_D: 0.7187 Loss_G: 1.8128 D(x): 0.6483 D(G(z)): 0.0846 / 0.0864\n", + "[1398/1600][6/8] Loss_D: 0.7073 Loss_G: 1.7569 D(x): 0.6805 D(G(z)): 0.0908 / 0.0909\n", + "[1399/1600][0/8] Loss_D: 0.7146 Loss_G: 1.7352 D(x): 0.6298 D(G(z)): 0.0960 / 0.0966\n", + "[1399/1600][2/8] Loss_D: 0.7500 Loss_G: 1.8395 D(x): 0.6064 D(G(z)): 0.0839 / 0.0811\n", + "[1399/1600][4/8] Loss_D: 0.7930 Loss_G: 1.8908 D(x): 0.6093 D(G(z)): 0.0801 / 0.0777\n", + "[1399/1600][6/8] Loss_D: 0.7504 Loss_G: 1.8163 D(x): 0.6209 D(G(z)): 0.0847 / 0.0839\n", + "[1400/1600][0/8] Loss_D: 0.7902 Loss_G: 1.8046 D(x): 0.6123 D(G(z)): 0.0871 / 0.0837\n", + "[1400/1600][2/8] Loss_D: 0.7023 Loss_G: 1.7682 D(x): 0.6116 D(G(z)): 0.0875 / 0.0906\n", + "[1400/1600][4/8] Loss_D: 0.7308 Loss_G: 1.9130 D(x): 0.6309 D(G(z)): 0.0767 / 0.0754\n", + "[1400/1600][6/8] Loss_D: 0.7126 Loss_G: 1.8345 D(x): 0.5913 D(G(z)): 0.0779 / 0.0813\n", + "[1401/1600][0/8] Loss_D: 0.7843 Loss_G: 1.7689 D(x): 0.6222 D(G(z)): 0.0903 / 0.0895\n", + "[1401/1600][2/8] Loss_D: 0.7739 Loss_G: 1.8370 D(x): 0.5813 D(G(z)): 0.0838 / 0.0824\n", + "[1401/1600][4/8] Loss_D: 0.7776 Loss_G: 1.9023 D(x): 0.6030 D(G(z)): 0.0775 / 0.0750\n", + "[1401/1600][6/8] Loss_D: 0.7114 Loss_G: 1.8073 D(x): 0.6051 D(G(z)): 0.0826 / 0.0849\n" + ] + }, + { + "data": { + "image/png": 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"[1426/1600][0/8] Loss_D: 0.8368 Loss_G: 1.6909 D(x): 0.6450 D(G(z)): 0.1114 / 0.1055\n", + "[1426/1600][2/8] Loss_D: 0.7097 Loss_G: 1.7132 D(x): 0.6278 D(G(z)): 0.0982 / 0.0986\n", + "[1426/1600][4/8] Loss_D: 0.7595 Loss_G: 1.8632 D(x): 0.6530 D(G(z)): 0.0813 / 0.0787\n", + "[1426/1600][6/8] Loss_D: 0.8059 Loss_G: 1.7620 D(x): 0.5740 D(G(z)): 0.0934 / 0.0903\n", + "[1427/1600][0/8] Loss_D: 0.7517 Loss_G: 1.9240 D(x): 0.5780 D(G(z)): 0.0707 / 0.0714\n", + "[1427/1600][2/8] Loss_D: 0.7774 Loss_G: 1.8363 D(x): 0.6157 D(G(z)): 0.0827 / 0.0816\n", + "[1427/1600][4/8] Loss_D: 0.7721 Loss_G: 1.8840 D(x): 0.6601 D(G(z)): 0.0788 / 0.0755\n", + "[1427/1600][6/8] Loss_D: 0.7852 Loss_G: 1.8612 D(x): 0.5533 D(G(z)): 0.0788 / 0.0787\n", + "[1428/1600][0/8] Loss_D: 0.7663 Loss_G: 1.8019 D(x): 0.5992 D(G(z)): 0.0860 / 0.0874\n", + "[1428/1600][2/8] Loss_D: 0.7835 Loss_G: 1.9024 D(x): 0.6129 D(G(z)): 0.0783 / 0.0743\n", + "[1428/1600][4/8] Loss_D: 0.6861 Loss_G: 1.8925 D(x): 0.6125 D(G(z)): 0.0722 / 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D(G(z)): 0.0853 / 0.0863\n", + "[1431/1600][4/8] Loss_D: 0.7086 Loss_G: 1.7642 D(x): 0.6763 D(G(z)): 0.0958 / 0.0945\n", + "[1431/1600][6/8] Loss_D: 0.7471 Loss_G: 1.8146 D(x): 0.5944 D(G(z)): 0.0832 / 0.0850\n", + "[1432/1600][0/8] Loss_D: 0.7457 Loss_G: 1.6360 D(x): 0.6416 D(G(z)): 0.1107 / 0.1105\n", + "[1432/1600][2/8] Loss_D: 0.7885 Loss_G: 1.8573 D(x): 0.5993 D(G(z)): 0.0848 / 0.0801\n", + "[1432/1600][4/8] Loss_D: 0.7686 Loss_G: 1.8312 D(x): 0.6565 D(G(z)): 0.0858 / 0.0821\n", + "[1432/1600][6/8] Loss_D: 0.7241 Loss_G: 1.9138 D(x): 0.5821 D(G(z)): 0.0716 / 0.0730\n", + "[1433/1600][0/8] Loss_D: 0.6970 Loss_G: 1.8583 D(x): 0.6479 D(G(z)): 0.0786 / 0.0797\n", + "[1433/1600][2/8] Loss_D: 0.6620 Loss_G: 1.8367 D(x): 0.6531 D(G(z)): 0.0787 / 0.0812\n", + "[1433/1600][4/8] Loss_D: 0.6974 Loss_G: 1.8179 D(x): 0.6306 D(G(z)): 0.0844 / 0.0863\n", + "[1433/1600][6/8] Loss_D: 0.7804 Loss_G: 1.7920 D(x): 0.6138 D(G(z)): 0.0909 / 0.0869\n", + "[1434/1600][0/8] Loss_D: 0.7391 Loss_G: 1.8505 D(x): 0.5960 D(G(z)): 0.0815 / 0.0810\n", + "[1434/1600][2/8] Loss_D: 0.7127 Loss_G: 1.8771 D(x): 0.6020 D(G(z)): 0.0757 / 0.0786\n", + "[1434/1600][4/8] Loss_D: 0.7908 Loss_G: 1.8491 D(x): 0.5746 D(G(z)): 0.0808 / 0.0814\n", + "[1434/1600][6/8] Loss_D: 0.7656 Loss_G: 1.8452 D(x): 0.6407 D(G(z)): 0.0839 / 0.0803\n", + "[1435/1600][0/8] Loss_D: 0.7468 Loss_G: 1.9136 D(x): 0.6372 D(G(z)): 0.0752 / 0.0724\n", + "[1435/1600][2/8] Loss_D: 0.7621 Loss_G: 1.8734 D(x): 0.5938 D(G(z)): 0.0809 / 0.0803\n", + "[1435/1600][4/8] Loss_D: 0.7441 Loss_G: 1.8579 D(x): 0.5916 D(G(z)): 0.0789 / 0.0808\n", + "[1435/1600][6/8] Loss_D: 0.7828 Loss_G: 1.8790 D(x): 0.5501 D(G(z)): 0.0783 / 0.0781\n", + "[1436/1600][0/8] Loss_D: 0.7449 Loss_G: 1.9515 D(x): 0.6239 D(G(z)): 0.0716 / 0.0701\n", + "[1436/1600][2/8] Loss_D: 0.7824 Loss_G: 1.9028 D(x): 0.6193 D(G(z)): 0.0777 / 0.0762\n", + "[1436/1600][4/8] Loss_D: 0.7770 Loss_G: 1.8341 D(x): 0.5964 D(G(z)): 0.0836 / 0.0829\n", + "[1436/1600][6/8] Loss_D: 0.7927 Loss_G: 1.8555 D(x): 0.5627 D(G(z)): 0.0833 / 0.0813\n", + "[1437/1600][0/8] Loss_D: 0.6997 Loss_G: 1.9185 D(x): 0.6212 D(G(z)): 0.0712 / 0.0731\n", + "[1437/1600][2/8] Loss_D: 0.7365 Loss_G: 1.7760 D(x): 0.5942 D(G(z)): 0.0862 / 0.0897\n", + "[1437/1600][4/8] Loss_D: 0.7907 Loss_G: 1.7964 D(x): 0.6174 D(G(z)): 0.0882 / 0.0878\n", + "[1437/1600][6/8] Loss_D: 0.7497 Loss_G: 1.8365 D(x): 0.6542 D(G(z)): 0.0886 / 0.0849\n", + "[1438/1600][0/8] Loss_D: 0.7872 Loss_G: 1.9180 D(x): 0.5962 D(G(z)): 0.0767 / 0.0726\n", + "[1438/1600][2/8] Loss_D: 0.7215 Loss_G: 1.9651 D(x): 0.5788 D(G(z)): 0.0657 / 0.0676\n", + "[1438/1600][4/8] Loss_D: 0.7533 Loss_G: 1.8636 D(x): 0.5671 D(G(z)): 0.0754 / 0.0786\n", + "[1438/1600][6/8] Loss_D: 0.7363 Loss_G: 1.7428 D(x): 0.5985 D(G(z)): 0.0940 / 0.0954\n", + "[1439/1600][0/8] Loss_D: 0.7603 Loss_G: 1.8399 D(x): 0.5874 D(G(z)): 0.0819 / 0.0814\n", + "[1439/1600][2/8] Loss_D: 0.7684 Loss_G: 1.7688 D(x): 0.6399 D(G(z)): 0.0920 / 0.0915\n", + "[1439/1600][4/8] Loss_D: 0.7022 Loss_G: 1.8264 D(x): 0.6396 D(G(z)): 0.0833 / 0.0832\n", + "[1439/1600][6/8] Loss_D: 0.7231 Loss_G: 1.8960 D(x): 0.6023 D(G(z)): 0.0757 / 0.0759\n", + "[1440/1600][0/8] Loss_D: 0.7661 Loss_G: 1.8442 D(x): 0.6059 D(G(z)): 0.0841 / 0.0831\n", + "[1440/1600][2/8] Loss_D: 0.7991 Loss_G: 1.8867 D(x): 0.5940 D(G(z)): 0.0802 / 0.0781\n", + "[1440/1600][4/8] Loss_D: 0.7699 Loss_G: 1.9244 D(x): 0.5961 D(G(z)): 0.0762 / 0.0737\n", + "[1440/1600][6/8] Loss_D: 0.7514 Loss_G: 1.8881 D(x): 0.5597 D(G(z)): 0.0754 / 0.0772\n", + "[1441/1600][0/8] Loss_D: 0.6587 Loss_G: 2.0047 D(x): 0.6331 D(G(z)): 0.0620 / 0.0646\n", + "[1441/1600][2/8] Loss_D: 0.7629 Loss_G: 1.8821 D(x): 0.5514 D(G(z)): 0.0734 / 0.0771\n", + "[1441/1600][4/8] Loss_D: 0.7540 Loss_G: 1.8414 D(x): 0.6067 D(G(z)): 0.0831 / 0.0835\n", + "[1441/1600][6/8] Loss_D: 0.7307 Loss_G: 1.8252 D(x): 0.6283 D(G(z)): 0.0855 / 0.0857\n", + "[1442/1600][0/8] Loss_D: 0.7068 Loss_G: 1.7997 D(x): 0.6210 D(G(z)): 0.0878 / 0.0882\n", + "[1442/1600][2/8] Loss_D: 0.7611 Loss_G: 1.8412 D(x): 0.5695 D(G(z)): 0.0802 / 0.0808\n", + "[1442/1600][4/8] Loss_D: 0.7860 Loss_G: 1.8680 D(x): 0.6089 D(G(z)): 0.0810 / 0.0780\n", + "[1442/1600][6/8] Loss_D: 0.7058 Loss_G: 1.8318 D(x): 0.6156 D(G(z)): 0.0788 / 0.0818\n", + "[1443/1600][0/8] Loss_D: 0.7724 Loss_G: 1.7861 D(x): 0.5929 D(G(z)): 0.0902 / 0.0904\n", + "[1443/1600][2/8] Loss_D: 0.7343 Loss_G: 1.7929 D(x): 0.6650 D(G(z)): 0.0887 / 0.0865\n", + "[1443/1600][4/8] Loss_D: 0.7507 Loss_G: 1.7992 D(x): 0.5984 D(G(z)): 0.0863 / 0.0878\n", + "[1443/1600][6/8] Loss_D: 0.7537 Loss_G: 1.7175 D(x): 0.6161 D(G(z)): 0.0956 / 0.0969\n", + "[1444/1600][0/8] Loss_D: 0.7103 Loss_G: 1.7714 D(x): 0.6430 D(G(z)): 0.0907 / 0.0901\n", + "[1444/1600][2/8] Loss_D: 0.7608 Loss_G: 1.7266 D(x): 0.6716 D(G(z)): 0.1026 / 0.0997\n", + "[1444/1600][4/8] Loss_D: 0.7503 Loss_G: 1.8565 D(x): 0.6003 D(G(z)): 0.0829 / 0.0804\n", + "[1444/1600][6/8] Loss_D: 0.7162 Loss_G: 1.8577 D(x): 0.6189 D(G(z)): 0.0798 / 0.0806\n", + "[1445/1600][0/8] Loss_D: 0.7677 Loss_G: 1.7758 D(x): 0.6219 D(G(z)): 0.0890 / 0.0894\n", + "[1445/1600][2/8] Loss_D: 0.7148 Loss_G: 1.7389 D(x): 0.6551 D(G(z)): 0.0954 / 0.0958\n", + "[1445/1600][4/8] Loss_D: 0.7132 Loss_G: 1.7483 D(x): 0.6266 D(G(z)): 0.0937 / 0.0935\n", + "[1445/1600][6/8] Loss_D: 0.7259 Loss_G: 1.8103 D(x): 0.6701 D(G(z)): 0.0860 / 0.0854\n", + "[1446/1600][0/8] Loss_D: 0.7292 Loss_G: 1.7131 D(x): 0.6218 D(G(z)): 0.0979 / 0.0998\n", + "[1446/1600][2/8] Loss_D: 0.8052 Loss_G: 1.8071 D(x): 0.6159 D(G(z)): 0.0925 / 0.0865\n", + "[1446/1600][4/8] Loss_D: 0.7200 Loss_G: 1.9162 D(x): 0.6257 D(G(z)): 0.0777 / 0.0759\n", + "[1446/1600][6/8] Loss_D: 0.7736 Loss_G: 1.8923 D(x): 0.6100 D(G(z)): 0.0772 / 0.0761\n", + "[1447/1600][0/8] Loss_D: 0.7844 Loss_G: 1.7912 D(x): 0.6294 D(G(z)): 0.0909 / 0.0888\n", + "[1447/1600][2/8] Loss_D: 0.7608 Loss_G: 1.8833 D(x): 0.6148 D(G(z)): 0.0790 / 0.0772\n", + "[1447/1600][4/8] Loss_D: 0.7472 Loss_G: 1.7968 D(x): 0.5715 D(G(z)): 0.0836 / 0.0874\n", + "[1447/1600][6/8] Loss_D: 0.6946 Loss_G: 1.8311 D(x): 0.6226 D(G(z)): 0.0818 / 0.0849\n", + "[1448/1600][0/8] Loss_D: 0.7433 Loss_G: 1.7910 D(x): 0.6591 D(G(z)): 0.0895 / 0.0891\n", + "[1448/1600][2/8] Loss_D: 0.7475 Loss_G: 1.7835 D(x): 0.6571 D(G(z)): 0.0907 / 0.0885\n", + "[1448/1600][4/8] Loss_D: 0.6760 Loss_G: 1.8866 D(x): 0.6410 D(G(z)): 0.0756 / 0.0769\n", + "[1448/1600][6/8] Loss_D: 0.6762 Loss_G: 1.8178 D(x): 0.6692 D(G(z)): 0.0830 / 0.0841\n", + "[1449/1600][0/8] Loss_D: 0.7871 Loss_G: 1.8355 D(x): 0.6696 D(G(z)): 0.0871 / 0.0837\n", + "[1449/1600][2/8] Loss_D: 0.7261 Loss_G: 1.7668 D(x): 0.6389 D(G(z)): 0.0919 / 0.0913\n", + "[1449/1600][4/8] Loss_D: 0.7840 Loss_G: 1.7049 D(x): 0.5485 D(G(z)): 0.0972 / 0.1012\n", + "[1449/1600][6/8] Loss_D: 0.7339 Loss_G: 1.7234 D(x): 0.6578 D(G(z)): 0.0965 / 0.0961\n", + "[1450/1600][0/8] Loss_D: 0.7930 Loss_G: 1.8469 D(x): 0.6153 D(G(z)): 0.0879 / 0.0827\n", + "[1450/1600][2/8] Loss_D: 0.7903 Loss_G: 1.8192 D(x): 0.6114 D(G(z)): 0.0875 / 0.0843\n", + "[1450/1600][4/8] Loss_D: 0.7623 Loss_G: 1.8480 D(x): 0.6243 D(G(z)): 0.0856 / 0.0834\n", + "[1450/1600][6/8] Loss_D: 0.7549 Loss_G: 1.8324 D(x): 0.6527 D(G(z)): 0.0861 / 0.0830\n", + "[1451/1600][0/8] Loss_D: 0.7420 Loss_G: 1.8606 D(x): 0.5752 D(G(z)): 0.0762 / 0.0786\n", + "[1451/1600][2/8] Loss_D: 0.7191 Loss_G: 1.8154 D(x): 0.6198 D(G(z)): 0.0812 / 0.0840\n", + "[1451/1600][4/8] Loss_D: 0.7741 Loss_G: 1.7518 D(x): 0.5901 D(G(z)): 0.0933 / 0.0925\n", + "[1451/1600][6/8] Loss_D: 0.7230 Loss_G: 1.9097 D(x): 0.6282 D(G(z)): 0.0755 / 0.0742\n", + "[1452/1600][0/8] Loss_D: 0.7328 Loss_G: 1.8571 D(x): 0.6062 D(G(z)): 0.0780 / 0.0790\n", + "[1452/1600][2/8] Loss_D: 0.7588 Loss_G: 1.8785 D(x): 0.5943 D(G(z)): 0.0805 / 0.0791\n", + "[1452/1600][4/8] Loss_D: 0.7615 Loss_G: 1.9076 D(x): 0.6270 D(G(z)): 0.0763 / 0.0732\n", + "[1452/1600][6/8] Loss_D: 0.7764 Loss_G: 1.9719 D(x): 0.5859 D(G(z)): 0.0694 / 0.0692\n", + "[1453/1600][0/8] Loss_D: 0.7467 Loss_G: 1.8663 D(x): 0.5834 D(G(z)): 0.0771 / 0.0786\n", + "[1453/1600][2/8] Loss_D: 0.7606 Loss_G: 1.7971 D(x): 0.6153 D(G(z)): 0.0868 / 0.0885\n", + "[1453/1600][4/8] Loss_D: 0.7256 Loss_G: 1.8992 D(x): 0.6013 D(G(z)): 0.0748 / 0.0763\n", + "[1453/1600][6/8] Loss_D: 0.7134 Loss_G: 1.8289 D(x): 0.6378 D(G(z)): 0.0820 / 0.0828\n", + "[1454/1600][0/8] Loss_D: 0.6861 Loss_G: 1.8124 D(x): 0.6589 D(G(z)): 0.0853 / 0.0850\n", + "[1454/1600][2/8] Loss_D: 0.7010 Loss_G: 1.7798 D(x): 0.6195 D(G(z)): 0.0843 / 0.0888\n", + "[1454/1600][4/8] Loss_D: 0.7751 Loss_G: 1.8385 D(x): 0.6219 D(G(z)): 0.0868 / 0.0826\n", + "[1454/1600][6/8] Loss_D: 0.7035 Loss_G: 1.9371 D(x): 0.5854 D(G(z)): 0.0702 / 0.0717\n", + "[1455/1600][0/8] Loss_D: 0.7385 Loss_G: 1.8295 D(x): 0.5949 D(G(z)): 0.0813 / 0.0836\n", + "[1455/1600][2/8] Loss_D: 0.7132 Loss_G: 1.8804 D(x): 0.6532 D(G(z)): 0.0780 / 0.0773\n", + "[1455/1600][4/8] Loss_D: 0.7579 Loss_G: 1.8115 D(x): 0.5959 D(G(z)): 0.0871 / 0.0871\n", + "[1455/1600][6/8] Loss_D: 0.7831 Loss_G: 1.8901 D(x): 0.6377 D(G(z)): 0.0823 / 0.0775\n", + "[1456/1600][0/8] Loss_D: 0.7630 Loss_G: 1.9543 D(x): 0.5750 D(G(z)): 0.0714 / 0.0711\n", + "[1456/1600][2/8] Loss_D: 0.7469 Loss_G: 1.8681 D(x): 0.5549 D(G(z)): 0.0714 / 0.0782\n", + "[1456/1600][4/8] Loss_D: 0.6430 Loss_G: 1.8961 D(x): 0.6946 D(G(z)): 0.0767 / 0.0769\n", + "[1456/1600][6/8] Loss_D: 0.7446 Loss_G: 1.8312 D(x): 0.5990 D(G(z)): 0.0825 / 0.0840\n", + "[1457/1600][0/8] Loss_D: 0.7484 Loss_G: 1.7695 D(x): 0.5985 D(G(z)): 0.0913 / 0.0936\n", + "[1457/1600][2/8] Loss_D: 0.8038 Loss_G: 1.8415 D(x): 0.6766 D(G(z)): 0.0899 / 0.0831\n", + "[1457/1600][4/8] Loss_D: 0.7239 Loss_G: 1.9153 D(x): 0.6027 D(G(z)): 0.0737 / 0.0731\n", + "[1457/1600][6/8] Loss_D: 0.7370 Loss_G: 1.7765 D(x): 0.6517 D(G(z)): 0.0887 / 0.0888\n", + "[1458/1600][0/8] Loss_D: 0.7889 Loss_G: 1.8737 D(x): 0.6100 D(G(z)): 0.0847 / 0.0799\n", + "[1458/1600][2/8] Loss_D: 0.7115 Loss_G: 1.9193 D(x): 0.6161 D(G(z)): 0.0726 / 0.0722\n", + "[1458/1600][4/8] Loss_D: 0.7361 Loss_G: 1.8606 D(x): 0.6013 D(G(z)): 0.0766 / 0.0784\n", + "[1458/1600][6/8] Loss_D: 0.7764 Loss_G: 1.9506 D(x): 0.5800 D(G(z)): 0.0709 / 0.0702\n", + "[1459/1600][0/8] Loss_D: 0.7185 Loss_G: 1.8464 D(x): 0.6333 D(G(z)): 0.0811 / 0.0822\n", + "[1459/1600][2/8] Loss_D: 0.7015 Loss_G: 1.8344 D(x): 0.6621 D(G(z)): 0.0837 / 0.0835\n", + "[1459/1600][4/8] Loss_D: 0.7583 Loss_G: 1.7984 D(x): 0.6115 D(G(z)): 0.0876 / 0.0882\n", + "[1459/1600][6/8] Loss_D: 0.7836 Loss_G: 1.8627 D(x): 0.6152 D(G(z)): 0.0809 / 0.0790\n", + "[1460/1600][0/8] Loss_D: 0.6974 Loss_G: 1.8324 D(x): 0.6399 D(G(z)): 0.0807 / 0.0828\n", + "[1460/1600][2/8] Loss_D: 0.7544 Loss_G: 1.8633 D(x): 0.6453 D(G(z)): 0.0819 / 0.0791\n", + "[1460/1600][4/8] Loss_D: 0.7612 Loss_G: 1.8426 D(x): 0.5957 D(G(z)): 0.0839 / 0.0826\n", + "[1460/1600][6/8] Loss_D: 0.7982 Loss_G: 1.8989 D(x): 0.6169 D(G(z)): 0.0802 / 0.0757\n", + "[1461/1600][0/8] Loss_D: 0.7316 Loss_G: 1.8360 D(x): 0.5983 D(G(z)): 0.0800 / 0.0825\n", + "[1461/1600][2/8] Loss_D: 0.6956 Loss_G: 1.8584 D(x): 0.6343 D(G(z)): 0.0786 / 0.0791\n", + "[1461/1600][4/8] Loss_D: 0.7804 Loss_G: 1.9364 D(x): 0.5672 D(G(z)): 0.0735 / 0.0720\n", + "[1461/1600][6/8] Loss_D: 0.7717 Loss_G: 1.9235 D(x): 0.5774 D(G(z)): 0.0750 / 0.0729\n", + "[1462/1600][0/8] Loss_D: 0.7768 Loss_G: 1.9592 D(x): 0.5922 D(G(z)): 0.0717 / 0.0692\n", + "[1462/1600][2/8] Loss_D: 0.7582 Loss_G: 1.9328 D(x): 0.5391 D(G(z)): 0.0674 / 0.0721\n", + "[1462/1600][4/8] Loss_D: 0.6995 Loss_G: 1.8532 D(x): 0.6118 D(G(z)): 0.0739 / 0.0800\n", + "[1462/1600][6/8] Loss_D: 0.7464 Loss_G: 1.7559 D(x): 0.6591 D(G(z)): 0.0946 / 0.0922\n", + "[1463/1600][0/8] Loss_D: 0.7297 Loss_G: 1.8537 D(x): 0.5967 D(G(z)): 0.0800 / 0.0818\n", + "[1463/1600][2/8] Loss_D: 0.7497 Loss_G: 1.8550 D(x): 0.6578 D(G(z)): 0.0818 / 0.0812\n", + "[1463/1600][4/8] Loss_D: 0.7548 Loss_G: 1.7585 D(x): 0.6638 D(G(z)): 0.0978 / 0.0933\n", + "[1463/1600][6/8] Loss_D: 0.7797 Loss_G: 1.8971 D(x): 0.5382 D(G(z)): 0.0736 / 0.0751\n", + "[1464/1600][0/8] Loss_D: 0.7709 Loss_G: 1.7956 D(x): 0.5944 D(G(z)): 0.0864 / 0.0861\n", + "[1464/1600][2/8] Loss_D: 0.7213 Loss_G: 1.7714 D(x): 0.6133 D(G(z)): 0.0905 / 0.0910\n", + "[1464/1600][4/8] Loss_D: 0.7035 Loss_G: 1.8699 D(x): 0.6202 D(G(z)): 0.0762 / 0.0786\n", + "[1464/1600][6/8] Loss_D: 0.7606 Loss_G: 1.7621 D(x): 0.6156 D(G(z)): 0.0916 / 0.0917\n", + "[1465/1600][0/8] Loss_D: 0.7449 Loss_G: 1.7611 D(x): 0.5949 D(G(z)): 0.0934 / 0.0947\n", + "[1465/1600][2/8] Loss_D: 0.7930 Loss_G: 1.8418 D(x): 0.6155 D(G(z)): 0.0879 / 0.0831\n", + "[1465/1600][4/8] Loss_D: 0.6985 Loss_G: 1.8821 D(x): 0.6377 D(G(z)): 0.0761 / 0.0764\n", + "[1465/1600][6/8] Loss_D: 0.7795 Loss_G: 1.9442 D(x): 0.5431 D(G(z)): 0.0704 / 0.0707\n", + "[1466/1600][0/8] Loss_D: 0.6918 Loss_G: 1.7813 D(x): 0.6322 D(G(z)): 0.0885 / 0.0894\n", + "[1466/1600][2/8] Loss_D: 0.7283 Loss_G: 1.8664 D(x): 0.5913 D(G(z)): 0.0750 / 0.0792\n", + "[1466/1600][4/8] Loss_D: 0.7111 Loss_G: 1.7987 D(x): 0.6111 D(G(z)): 0.0829 / 0.0862\n", + "[1466/1600][6/8] Loss_D: 0.8053 Loss_G: 1.8148 D(x): 0.6077 D(G(z)): 0.0912 / 0.0858\n", + "[1467/1600][0/8] Loss_D: 0.7331 Loss_G: 1.7818 D(x): 0.6255 D(G(z)): 0.0895 / 0.0897\n", + "[1467/1600][2/8] Loss_D: 0.7125 Loss_G: 1.7380 D(x): 0.6301 D(G(z)): 0.0942 / 0.0939\n", + "[1467/1600][4/8] Loss_D: 0.7777 Loss_G: 1.9032 D(x): 0.5469 D(G(z)): 0.0771 / 0.0758\n", + "[1467/1600][6/8] Loss_D: 0.7124 Loss_G: 1.8238 D(x): 0.6100 D(G(z)): 0.0811 / 0.0841\n", + "[1468/1600][0/8] Loss_D: 0.7223 Loss_G: 1.8148 D(x): 0.5990 D(G(z)): 0.0809 / 0.0843\n", + "[1468/1600][2/8] Loss_D: 0.7910 Loss_G: 1.7926 D(x): 0.5970 D(G(z)): 0.0905 / 0.0889\n", + "[1468/1600][4/8] Loss_D: 0.7927 Loss_G: 1.9032 D(x): 0.5886 D(G(z)): 0.0782 / 0.0754\n", + "[1468/1600][6/8] Loss_D: 0.7732 Loss_G: 1.8215 D(x): 0.6254 D(G(z)): 0.0878 / 0.0855\n", + "[1469/1600][0/8] Loss_D: 0.7391 Loss_G: 1.8658 D(x): 0.5776 D(G(z)): 0.0786 / 0.0806\n", + "[1469/1600][2/8] Loss_D: 0.7064 Loss_G: 1.8320 D(x): 0.6027 D(G(z)): 0.0792 / 0.0835\n", + "[1469/1600][4/8] Loss_D: 0.7068 Loss_G: 1.7703 D(x): 0.6844 D(G(z)): 0.0891 / 0.0907\n", + "[1469/1600][6/8] Loss_D: 0.7060 Loss_G: 1.7705 D(x): 0.6545 D(G(z)): 0.0916 / 0.0923\n", + "[1470/1600][0/8] Loss_D: 0.8118 Loss_G: 1.7766 D(x): 0.6838 D(G(z)): 0.0949 / 0.0887\n", + "[1470/1600][2/8] Loss_D: 0.7379 Loss_G: 1.8016 D(x): 0.6247 D(G(z)): 0.0864 / 0.0866\n", + "[1470/1600][4/8] Loss_D: 0.7438 Loss_G: 1.8167 D(x): 0.6322 D(G(z)): 0.0880 / 0.0846\n", + "[1470/1600][6/8] Loss_D: 0.7310 Loss_G: 1.9373 D(x): 0.6060 D(G(z)): 0.0733 / 0.0718\n", + "[1471/1600][0/8] Loss_D: 0.7281 Loss_G: 1.9740 D(x): 0.6048 D(G(z)): 0.0671 / 0.0682\n", + "[1471/1600][2/8] Loss_D: 0.7950 Loss_G: 1.8111 D(x): 0.6213 D(G(z)): 0.0889 / 0.0873\n", + "[1471/1600][4/8] Loss_D: 0.7697 Loss_G: 1.7952 D(x): 0.6291 D(G(z)): 0.0908 / 0.0888\n", + "[1471/1600][6/8] Loss_D: 0.7723 Loss_G: 1.9664 D(x): 0.5727 D(G(z)): 0.0687 / 0.0676\n", + "[1472/1600][0/8] Loss_D: 0.7618 Loss_G: 1.9687 D(x): 0.6006 D(G(z)): 0.0719 / 0.0691\n", + "[1472/1600][2/8] Loss_D: 0.7161 Loss_G: 1.9204 D(x): 0.5973 D(G(z)): 0.0735 / 0.0743\n", + "[1472/1600][4/8] Loss_D: 0.7297 Loss_G: 1.9248 D(x): 0.5726 D(G(z)): 0.0687 / 0.0734\n", + "[1472/1600][6/8] Loss_D: 0.7381 Loss_G: 1.8186 D(x): 0.5645 D(G(z)): 0.0800 / 0.0871\n", + "[1473/1600][0/8] Loss_D: 0.7639 Loss_G: 1.7847 D(x): 0.6307 D(G(z)): 0.0893 / 0.0904\n", + "[1473/1600][2/8] Loss_D: 0.7404 Loss_G: 1.8121 D(x): 0.6649 D(G(z)): 0.0912 / 0.0872\n", + "[1473/1600][4/8] Loss_D: 0.6609 Loss_G: 1.8316 D(x): 0.6678 D(G(z)): 0.0836 / 0.0840\n", + "[1473/1600][6/8] Loss_D: 0.7943 Loss_G: 1.8137 D(x): 0.5498 D(G(z)): 0.0848 / 0.0849\n", + "[1474/1600][0/8] Loss_D: 0.7265 Loss_G: 1.7638 D(x): 0.6349 D(G(z)): 0.0878 / 0.0900\n", + "[1474/1600][2/8] Loss_D: 0.7031 Loss_G: 1.7718 D(x): 0.6210 D(G(z)): 0.0904 / 0.0927\n", + "[1474/1600][4/8] Loss_D: 0.7930 Loss_G: 1.7366 D(x): 0.6627 D(G(z)): 0.1002 / 0.0965\n", + "[1474/1600][6/8] Loss_D: 0.8243 Loss_G: 1.6954 D(x): 0.6465 D(G(z)): 0.1068 / 0.1002\n", + "[1475/1600][0/8] Loss_D: 0.7290 Loss_G: 1.8126 D(x): 0.6360 D(G(z)): 0.0873 / 0.0865\n", + "[1475/1600][2/8] Loss_D: 0.7196 Loss_G: 1.8110 D(x): 0.6100 D(G(z)): 0.0868 / 0.0896\n", + "[1475/1600][4/8] Loss_D: 0.7712 Loss_G: 1.7947 D(x): 0.6072 D(G(z)): 0.0878 / 0.0863\n", + "[1475/1600][6/8] Loss_D: 0.8089 Loss_G: 1.7907 D(x): 0.6272 D(G(z)): 0.0944 / 0.0895\n", + "[1476/1600][0/8] Loss_D: 0.7317 Loss_G: 1.9075 D(x): 0.6138 D(G(z)): 0.0759 / 0.0738\n", + "[1476/1600][2/8] Loss_D: 0.7805 Loss_G: 1.8884 D(x): 0.6161 D(G(z)): 0.0823 / 0.0783\n", + "[1476/1600][4/8] Loss_D: 0.7970 Loss_G: 1.9088 D(x): 0.5066 D(G(z)): 0.0707 / 0.0764\n", + "[1476/1600][6/8] Loss_D: 0.7262 Loss_G: 1.9462 D(x): 0.5928 D(G(z)): 0.0688 / 0.0704\n", + "[1477/1600][0/8] Loss_D: 0.8181 Loss_G: 1.7198 D(x): 0.5471 D(G(z)): 0.0994 / 0.1001\n", + "[1477/1600][2/8] Loss_D: 0.6899 Loss_G: 1.8319 D(x): 0.6385 D(G(z)): 0.0813 / 0.0832\n", + "[1477/1600][4/8] Loss_D: 0.7640 Loss_G: 1.8308 D(x): 0.6699 D(G(z)): 0.0890 / 0.0849\n", + "[1477/1600][6/8] Loss_D: 0.7408 Loss_G: 1.9414 D(x): 0.6123 D(G(z)): 0.0711 / 0.0704\n", + "[1478/1600][0/8] Loss_D: 0.7480 Loss_G: 1.8911 D(x): 0.6136 D(G(z)): 0.0756 / 0.0762\n", + "[1478/1600][2/8] Loss_D: 0.6939 Loss_G: 1.8198 D(x): 0.6235 D(G(z)): 0.0813 / 0.0857\n", + "[1478/1600][4/8] Loss_D: 0.7791 Loss_G: 1.8320 D(x): 0.6012 D(G(z)): 0.0842 / 0.0822\n", + "[1478/1600][6/8] Loss_D: 0.6794 Loss_G: 1.8543 D(x): 0.6371 D(G(z)): 0.0797 / 0.0796\n", + "[1479/1600][0/8] Loss_D: 0.7360 Loss_G: 1.8482 D(x): 0.6342 D(G(z)): 0.0799 / 0.0795\n", + "[1479/1600][2/8] Loss_D: 0.7578 Loss_G: 1.7741 D(x): 0.6158 D(G(z)): 0.0936 / 0.0942\n", + "[1479/1600][4/8] Loss_D: 0.7871 Loss_G: 1.7973 D(x): 0.6150 D(G(z)): 0.0909 / 0.0882\n", + "[1479/1600][6/8] Loss_D: 0.7671 Loss_G: 1.8432 D(x): 0.5986 D(G(z)): 0.0828 / 0.0824\n", + "[1480/1600][0/8] Loss_D: 0.7481 Loss_G: 1.8144 D(x): 0.6001 D(G(z)): 0.0840 / 0.0865\n", + "[1480/1600][2/8] Loss_D: 0.8099 Loss_G: 1.8293 D(x): 0.6597 D(G(z)): 0.0872 / 0.0819\n", + "[1480/1600][4/8] Loss_D: 0.7084 Loss_G: 1.8690 D(x): 0.6437 D(G(z)): 0.0810 / 0.0804\n", + "[1480/1600][6/8] Loss_D: 0.7793 Loss_G: 1.7502 D(x): 0.5969 D(G(z)): 0.0946 / 0.0925\n", + "[1481/1600][0/8] Loss_D: 0.6854 Loss_G: 1.7869 D(x): 0.6273 D(G(z)): 0.0838 / 0.0878\n", + "[1481/1600][2/8] Loss_D: 0.7740 Loss_G: 1.8537 D(x): 0.6846 D(G(z)): 0.0839 / 0.0800\n", + "[1481/1600][4/8] Loss_D: 0.7971 Loss_G: 1.9024 D(x): 0.5858 D(G(z)): 0.0790 / 0.0746\n", + "[1481/1600][6/8] Loss_D: 0.7470 Loss_G: 1.8963 D(x): 0.5870 D(G(z)): 0.0766 / 0.0778\n", + "[1482/1600][0/8] Loss_D: 0.7085 Loss_G: 1.7976 D(x): 0.6383 D(G(z)): 0.0831 / 0.0860\n", + "[1482/1600][2/8] Loss_D: 0.6875 Loss_G: 1.7915 D(x): 0.6502 D(G(z)): 0.0834 / 0.0864\n", + "[1482/1600][4/8] Loss_D: 0.6910 Loss_G: 1.7662 D(x): 0.6524 D(G(z)): 0.0914 / 0.0945\n", + "[1482/1600][6/8] Loss_D: 0.8059 Loss_G: 1.8366 D(x): 0.6921 D(G(z)): 0.0888 / 0.0814\n", + "[1483/1600][0/8] Loss_D: 0.7308 Loss_G: 1.8837 D(x): 0.6417 D(G(z)): 0.0783 / 0.0764\n", + "[1483/1600][2/8] Loss_D: 0.7001 Loss_G: 1.8163 D(x): 0.6330 D(G(z)): 0.0826 / 0.0839\n", + "[1483/1600][4/8] Loss_D: 0.7287 Loss_G: 1.7780 D(x): 0.6237 D(G(z)): 0.0910 / 0.0911\n", + "[1483/1600][6/8] Loss_D: 0.7694 Loss_G: 1.8414 D(x): 0.6078 D(G(z)): 0.0860 / 0.0837\n", + "[1484/1600][0/8] Loss_D: 0.7723 Loss_G: 1.8697 D(x): 0.5827 D(G(z)): 0.0786 / 0.0785\n", + "[1484/1600][2/8] Loss_D: 0.7709 Loss_G: 1.8289 D(x): 0.5898 D(G(z)): 0.0825 / 0.0840\n", + "[1484/1600][4/8] Loss_D: 0.7090 Loss_G: 1.7463 D(x): 0.6260 D(G(z)): 0.0916 / 0.0954\n", + "[1484/1600][6/8] Loss_D: 0.7491 Loss_G: 1.8404 D(x): 0.6263 D(G(z)): 0.0834 / 0.0827\n", + "[1485/1600][0/8] Loss_D: 0.7911 Loss_G: 1.8768 D(x): 0.6267 D(G(z)): 0.0843 / 0.0795\n", + "[1485/1600][2/8] Loss_D: 0.7041 Loss_G: 1.9487 D(x): 0.5913 D(G(z)): 0.0686 / 0.0699\n", + "[1485/1600][4/8] Loss_D: 0.7110 Loss_G: 1.7478 D(x): 0.6299 D(G(z)): 0.0902 / 0.0938\n", + "[1485/1600][6/8] Loss_D: 0.7730 Loss_G: 1.9531 D(x): 0.5718 D(G(z)): 0.0736 / 0.0728\n", + "[1486/1600][0/8] Loss_D: 0.7587 Loss_G: 1.9375 D(x): 0.6308 D(G(z)): 0.0738 / 0.0716\n", + "[1486/1600][2/8] Loss_D: 0.7808 Loss_G: 1.8862 D(x): 0.5648 D(G(z)): 0.0766 / 0.0770\n", + "[1486/1600][4/8] Loss_D: 0.6996 Loss_G: 1.7940 D(x): 0.6369 D(G(z)): 0.0862 / 0.0894\n", + "[1486/1600][6/8] Loss_D: 0.7248 Loss_G: 1.8942 D(x): 0.6163 D(G(z)): 0.0751 / 0.0751\n", + "[1487/1600][0/8] Loss_D: 0.7329 Loss_G: 1.7670 D(x): 0.6071 D(G(z)): 0.0903 / 0.0923\n", + "[1487/1600][2/8] Loss_D: 0.7328 Loss_G: 1.8046 D(x): 0.6193 D(G(z)): 0.0881 / 0.0884\n", + "[1487/1600][4/8] Loss_D: 0.7645 Loss_G: 1.8688 D(x): 0.6595 D(G(z)): 0.0820 / 0.0796\n", + "[1487/1600][6/8] Loss_D: 0.7828 Loss_G: 1.8610 D(x): 0.6578 D(G(z)): 0.0860 / 0.0808\n", + "[1488/1600][0/8] Loss_D: 0.7511 Loss_G: 1.8923 D(x): 0.6185 D(G(z)): 0.0772 / 0.0747\n", + "[1488/1600][2/8] Loss_D: 0.7405 Loss_G: 1.9703 D(x): 0.6318 D(G(z)): 0.0693 / 0.0677\n", + "[1488/1600][4/8] Loss_D: 0.7566 Loss_G: 1.9042 D(x): 0.5963 D(G(z)): 0.0750 / 0.0759\n", + "[1488/1600][6/8] Loss_D: 0.7746 Loss_G: 1.8089 D(x): 0.6210 D(G(z)): 0.0879 / 0.0859\n", + "[1489/1600][0/8] Loss_D: 0.7495 Loss_G: 1.9208 D(x): 0.6281 D(G(z)): 0.0759 / 0.0755\n", + "[1489/1600][2/8] Loss_D: 0.7546 Loss_G: 1.8880 D(x): 0.5787 D(G(z)): 0.0751 / 0.0760\n", + "[1489/1600][4/8] Loss_D: 0.7612 Loss_G: 1.9064 D(x): 0.5978 D(G(z)): 0.0756 / 0.0754\n", + "[1489/1600][6/8] Loss_D: 0.7463 Loss_G: 1.8877 D(x): 0.6399 D(G(z)): 0.0786 / 0.0781\n", + "[1490/1600][0/8] Loss_D: 0.7886 Loss_G: 1.9412 D(x): 0.6256 D(G(z)): 0.0733 / 0.0713\n", + "[1490/1600][2/8] Loss_D: 0.6994 Loss_G: 1.8181 D(x): 0.6417 D(G(z)): 0.0816 / 0.0831\n", + "[1490/1600][4/8] Loss_D: 0.7378 Loss_G: 1.8379 D(x): 0.6791 D(G(z)): 0.0835 / 0.0817\n", + "[1490/1600][6/8] Loss_D: 0.7711 Loss_G: 1.9550 D(x): 0.5706 D(G(z)): 0.0710 / 0.0703\n", + "[1491/1600][0/8] Loss_D: 0.6974 Loss_G: 1.7497 D(x): 0.6484 D(G(z)): 0.0915 / 0.0944\n", + "[1491/1600][2/8] Loss_D: 0.7719 Loss_G: 1.8322 D(x): 0.6055 D(G(z)): 0.0839 / 0.0840\n", + "[1491/1600][4/8] Loss_D: 0.7108 Loss_G: 1.8507 D(x): 0.6061 D(G(z)): 0.0797 / 0.0811\n", + "[1491/1600][6/8] Loss_D: 0.7459 Loss_G: 1.8046 D(x): 0.6973 D(G(z)): 0.0926 / 0.0891\n", + "[1492/1600][0/8] Loss_D: 0.7050 Loss_G: 1.8834 D(x): 0.6205 D(G(z)): 0.0767 / 0.0768\n", + "[1492/1600][2/8] Loss_D: 0.7889 Loss_G: 1.9347 D(x): 0.6463 D(G(z)): 0.0739 / 0.0699\n", + "[1492/1600][4/8] Loss_D: 0.6997 Loss_G: 1.8715 D(x): 0.6448 D(G(z)): 0.0785 / 0.0797\n", + "[1492/1600][6/8] Loss_D: 0.7853 Loss_G: 1.9124 D(x): 0.5857 D(G(z)): 0.0749 / 0.0730\n", + "[1493/1600][0/8] Loss_D: 0.7553 Loss_G: 1.9353 D(x): 0.5538 D(G(z)): 0.0704 / 0.0733\n", + "[1493/1600][2/8] Loss_D: 0.7703 Loss_G: 1.7685 D(x): 0.6080 D(G(z)): 0.0911 / 0.0913\n", + "[1493/1600][4/8] Loss_D: 0.7334 Loss_G: 1.8796 D(x): 0.6069 D(G(z)): 0.0766 / 0.0779\n", + "[1493/1600][6/8] Loss_D: 0.7810 Loss_G: 1.8795 D(x): 0.6292 D(G(z)): 0.0827 / 0.0794\n", + "[1494/1600][0/8] Loss_D: 0.7411 Loss_G: 1.8842 D(x): 0.5954 D(G(z)): 0.0780 / 0.0781\n", + "[1494/1600][2/8] Loss_D: 0.7058 Loss_G: 1.9220 D(x): 0.6273 D(G(z)): 0.0698 / 0.0720\n", + "[1494/1600][4/8] Loss_D: 0.7521 Loss_G: 1.7414 D(x): 0.6022 D(G(z)): 0.0952 / 0.0960\n", + "[1494/1600][6/8] Loss_D: 0.7112 Loss_G: 1.7946 D(x): 0.6657 D(G(z)): 0.0881 / 0.0872\n", + "[1495/1600][0/8] Loss_D: 0.7624 Loss_G: 1.8433 D(x): 0.6038 D(G(z)): 0.0846 / 0.0812\n", + "[1495/1600][2/8] Loss_D: 0.7402 Loss_G: 1.8511 D(x): 0.5898 D(G(z)): 0.0794 / 0.0804\n", + "[1495/1600][4/8] Loss_D: 0.7933 Loss_G: 1.8713 D(x): 0.6638 D(G(z)): 0.0804 / 0.0789\n", + "[1495/1600][6/8] Loss_D: 0.7461 Loss_G: 1.8232 D(x): 0.6159 D(G(z)): 0.0843 / 0.0829\n", + "[1496/1600][0/8] Loss_D: 0.7243 Loss_G: 1.9480 D(x): 0.5772 D(G(z)): 0.0691 / 0.0706\n", + "[1496/1600][2/8] Loss_D: 0.7411 Loss_G: 1.8775 D(x): 0.6054 D(G(z)): 0.0770 / 0.0778\n", + "[1496/1600][4/8] Loss_D: 0.7800 Loss_G: 1.9146 D(x): 0.5951 D(G(z)): 0.0772 / 0.0742\n", + "[1496/1600][6/8] Loss_D: 0.7807 Loss_G: 1.9820 D(x): 0.5589 D(G(z)): 0.0703 / 0.0682\n", + "[1497/1600][0/8] Loss_D: 0.7568 Loss_G: 2.0379 D(x): 0.5537 D(G(z)): 0.0606 / 0.0620\n", + "[1497/1600][2/8] Loss_D: 0.7460 Loss_G: 1.9291 D(x): 0.5618 D(G(z)): 0.0708 / 0.0738\n", + "[1497/1600][4/8] Loss_D: 0.7836 Loss_G: 1.9640 D(x): 0.6010 D(G(z)): 0.0713 / 0.0696\n", + "[1497/1600][6/8] Loss_D: 0.7558 Loss_G: 1.9296 D(x): 0.5514 D(G(z)): 0.0709 / 0.0746\n", + "[1498/1600][0/8] Loss_D: 0.7579 Loss_G: 1.8652 D(x): 0.5828 D(G(z)): 0.0789 / 0.0826\n", + "[1498/1600][2/8] Loss_D: 0.7308 Loss_G: 1.8769 D(x): 0.6204 D(G(z)): 0.0763 / 0.0775\n", + "[1498/1600][4/8] Loss_D: 0.7344 Loss_G: 1.7519 D(x): 0.6583 D(G(z)): 0.0968 / 0.0954\n", + "[1498/1600][6/8] Loss_D: 0.7189 Loss_G: 1.7668 D(x): 0.6158 D(G(z)): 0.0904 / 0.0922\n", + "[1499/1600][0/8] Loss_D: 0.7239 Loss_G: 1.8329 D(x): 0.6430 D(G(z)): 0.0860 / 0.0847\n", + "[1499/1600][2/8] Loss_D: 0.7535 Loss_G: 1.8883 D(x): 0.6501 D(G(z)): 0.0790 / 0.0773\n", + "[1499/1600][4/8] Loss_D: 0.7656 Loss_G: 1.8478 D(x): 0.6422 D(G(z)): 0.0856 / 0.0821\n", + "[1499/1600][6/8] Loss_D: 0.7918 Loss_G: 1.8627 D(x): 0.5967 D(G(z)): 0.0828 / 0.0804\n", + "[1500/1600][0/8] Loss_D: 0.7885 Loss_G: 1.8126 D(x): 0.6193 D(G(z)): 0.0888 / 0.0865\n", + "[1500/1600][2/8] Loss_D: 0.7523 Loss_G: 1.9395 D(x): 0.6204 D(G(z)): 0.0736 / 0.0716\n", + "[1500/1600][4/8] Loss_D: 0.7634 Loss_G: 2.0097 D(x): 0.6026 D(G(z)): 0.0674 / 0.0655\n", + "[1500/1600][6/8] Loss_D: 0.7505 Loss_G: 1.9683 D(x): 0.5699 D(G(z)): 0.0664 / 0.0671\n", + "[1501/1600][0/8] Loss_D: 0.7208 Loss_G: 1.8909 D(x): 0.5957 D(G(z)): 0.0749 / 0.0774\n", + "[1501/1600][2/8] Loss_D: 0.7068 Loss_G: 1.9423 D(x): 0.5843 D(G(z)): 0.0677 / 0.0711\n", + "[1501/1600][4/8] Loss_D: 0.7570 Loss_G: 1.8798 D(x): 0.6045 D(G(z)): 0.0771 / 0.0780\n", + "[1501/1600][6/8] Loss_D: 0.7880 Loss_G: 1.8917 D(x): 0.5785 D(G(z)): 0.0779 / 0.0762\n", + "[1502/1600][0/8] Loss_D: 0.7762 Loss_G: 1.8891 D(x): 0.6597 D(G(z)): 0.0798 / 0.0766\n", + "[1502/1600][2/8] Loss_D: 0.7731 Loss_G: 1.9073 D(x): 0.5459 D(G(z)): 0.0745 / 0.0747\n", + "[1502/1600][4/8] Loss_D: 0.7607 Loss_G: 1.8280 D(x): 0.6095 D(G(z)): 0.0819 / 0.0828\n", + "[1502/1600][6/8] Loss_D: 0.7867 Loss_G: 1.7593 D(x): 0.6404 D(G(z)): 0.0954 / 0.0938\n", + "[1503/1600][0/8] Loss_D: 0.7862 Loss_G: 1.8650 D(x): 0.5976 D(G(z)): 0.0818 / 0.0806\n", + "[1503/1600][2/8] Loss_D: 0.7918 Loss_G: 1.8767 D(x): 0.6002 D(G(z)): 0.0834 / 0.0790\n", + "[1503/1600][4/8] Loss_D: 0.7506 Loss_G: 1.9189 D(x): 0.5950 D(G(z)): 0.0750 / 0.0749\n", + "[1503/1600][6/8] Loss_D: 0.7712 Loss_G: 1.7974 D(x): 0.5842 D(G(z)): 0.0861 / 0.0880\n", + "[1504/1600][0/8] Loss_D: 0.7475 Loss_G: 1.8794 D(x): 0.6255 D(G(z)): 0.0804 / 0.0789\n", + "[1504/1600][2/8] Loss_D: 0.7690 Loss_G: 1.8115 D(x): 0.5626 D(G(z)): 0.0852 / 0.0857\n", + "[1504/1600][4/8] Loss_D: 0.7393 Loss_G: 1.9023 D(x): 0.6038 D(G(z)): 0.0773 / 0.0774\n", + "[1504/1600][6/8] Loss_D: 0.7376 Loss_G: 1.7988 D(x): 0.5935 D(G(z)): 0.0840 / 0.0867\n", + "[1505/1600][0/8] Loss_D: 0.7831 Loss_G: 1.8679 D(x): 0.6265 D(G(z)): 0.0798 / 0.0782\n", + "[1505/1600][2/8] Loss_D: 0.7100 Loss_G: 1.8627 D(x): 0.6037 D(G(z)): 0.0786 / 0.0804\n", + "[1505/1600][4/8] Loss_D: 0.7546 Loss_G: 1.7937 D(x): 0.6196 D(G(z)): 0.0870 / 0.0883\n", + "[1505/1600][6/8] Loss_D: 0.7830 Loss_G: 1.8092 D(x): 0.6413 D(G(z)): 0.0893 / 0.0868\n", + "[1506/1600][0/8] Loss_D: 0.7318 Loss_G: 1.8009 D(x): 0.5905 D(G(z)): 0.0826 / 0.0854\n", + "[1506/1600][2/8] Loss_D: 0.6940 Loss_G: 1.7118 D(x): 0.6385 D(G(z)): 0.0976 / 0.0983\n", + "[1506/1600][4/8] Loss_D: 0.7754 Loss_G: 1.8689 D(x): 0.6112 D(G(z)): 0.0829 / 0.0791\n", + "[1506/1600][6/8] Loss_D: 0.7638 Loss_G: 1.9463 D(x): 0.6095 D(G(z)): 0.0715 / 0.0690\n", + "[1507/1600][0/8] Loss_D: 0.7505 Loss_G: 1.8882 D(x): 0.5848 D(G(z)): 0.0750 / 0.0772\n", + "[1507/1600][2/8] Loss_D: 0.7285 Loss_G: 1.8777 D(x): 0.6140 D(G(z)): 0.0774 / 0.0789\n", + "[1507/1600][4/8] Loss_D: 0.7524 Loss_G: 1.8905 D(x): 0.6164 D(G(z)): 0.0794 / 0.0770\n", + "[1507/1600][6/8] Loss_D: 0.7733 Loss_G: 1.9412 D(x): 0.6097 D(G(z)): 0.0737 / 0.0723\n", + "[1508/1600][0/8] Loss_D: 0.7392 Loss_G: 1.8970 D(x): 0.6311 D(G(z)): 0.0761 / 0.0764\n", + "[1508/1600][2/8] Loss_D: 0.7761 Loss_G: 1.8747 D(x): 0.6277 D(G(z)): 0.0839 / 0.0808\n", + "[1508/1600][4/8] Loss_D: 0.7848 Loss_G: 1.8325 D(x): 0.5739 D(G(z)): 0.0823 / 0.0830\n", + "[1508/1600][6/8] Loss_D: 0.7717 Loss_G: 1.8730 D(x): 0.5878 D(G(z)): 0.0771 / 0.0772\n", + "[1509/1600][0/8] Loss_D: 0.7411 Loss_G: 1.8499 D(x): 0.6062 D(G(z)): 0.0803 / 0.0811\n", + "[1509/1600][2/8] Loss_D: 0.7871 Loss_G: 1.8624 D(x): 0.5978 D(G(z)): 0.0818 / 0.0786\n", + "[1509/1600][4/8] Loss_D: 0.7450 Loss_G: 1.8802 D(x): 0.6044 D(G(z)): 0.0796 / 0.0787\n", + "[1509/1600][6/8] Loss_D: 0.7368 Loss_G: 1.8521 D(x): 0.6007 D(G(z)): 0.0775 / 0.0804\n", + "[1510/1600][0/8] Loss_D: 0.7813 Loss_G: 1.8672 D(x): 0.6095 D(G(z)): 0.0809 / 0.0791\n", + "[1510/1600][2/8] Loss_D: 0.7988 Loss_G: 1.8348 D(x): 0.5706 D(G(z)): 0.0846 / 0.0837\n", + "[1510/1600][4/8] Loss_D: 0.7813 Loss_G: 1.8234 D(x): 0.6478 D(G(z)): 0.0884 / 0.0847\n", + "[1510/1600][6/8] Loss_D: 0.7245 Loss_G: 1.9382 D(x): 0.6266 D(G(z)): 0.0710 / 0.0716\n", + "[1511/1600][0/8] Loss_D: 0.6873 Loss_G: 1.8542 D(x): 0.6167 D(G(z)): 0.0756 / 0.0806\n", + "[1511/1600][2/8] Loss_D: 0.7166 Loss_G: 1.8271 D(x): 0.6878 D(G(z)): 0.0831 / 0.0830\n", + "[1511/1600][4/8] Loss_D: 0.7059 Loss_G: 1.6732 D(x): 0.6302 D(G(z)): 0.1023 / 0.1053\n", + "[1511/1600][6/8] Loss_D: 0.7742 Loss_G: 1.7083 D(x): 0.6610 D(G(z)): 0.1046 / 0.0992\n", + "[1512/1600][0/8] Loss_D: 0.7287 Loss_G: 1.7857 D(x): 0.6530 D(G(z)): 0.0911 / 0.0896\n", + "[1512/1600][2/8] Loss_D: 0.7721 Loss_G: 1.7911 D(x): 0.6161 D(G(z)): 0.0911 / 0.0894\n", + "[1512/1600][4/8] Loss_D: 0.7747 Loss_G: 1.7325 D(x): 0.6366 D(G(z)): 0.0995 / 0.0959\n", + "[1512/1600][6/8] Loss_D: 0.7354 Loss_G: 1.8549 D(x): 0.5839 D(G(z)): 0.0827 / 0.0830\n", + "[1513/1600][0/8] Loss_D: 0.7623 Loss_G: 1.8280 D(x): 0.5919 D(G(z)): 0.0839 / 0.0838\n", + "[1513/1600][2/8] Loss_D: 0.7290 Loss_G: 1.8757 D(x): 0.5991 D(G(z)): 0.0762 / 0.0794\n", + "[1513/1600][4/8] Loss_D: 0.7596 Loss_G: 1.8054 D(x): 0.5537 D(G(z)): 0.0851 / 0.0877\n", + "[1513/1600][6/8] Loss_D: 0.7500 Loss_G: 1.7627 D(x): 0.6611 D(G(z)): 0.0937 / 0.0934\n", + "[1514/1600][0/8] Loss_D: 0.7910 Loss_G: 1.8290 D(x): 0.6167 D(G(z)): 0.0892 / 0.0839\n", + "[1514/1600][2/8] Loss_D: 0.7366 Loss_G: 1.8105 D(x): 0.6024 D(G(z)): 0.0838 / 0.0855\n", + "[1514/1600][4/8] Loss_D: 0.7536 Loss_G: 1.7330 D(x): 0.5957 D(G(z)): 0.0941 / 0.0959\n", + "[1514/1600][6/8] Loss_D: 0.7001 Loss_G: 1.7926 D(x): 0.6427 D(G(z)): 0.0857 / 0.0878\n", + "[1515/1600][0/8] Loss_D: 0.7457 Loss_G: 1.8016 D(x): 0.6664 D(G(z)): 0.0918 / 0.0880\n", + "[1515/1600][2/8] Loss_D: 0.7794 Loss_G: 1.8287 D(x): 0.5819 D(G(z)): 0.0857 / 0.0838\n", + "[1515/1600][4/8] Loss_D: 0.7848 Loss_G: 1.8679 D(x): 0.5866 D(G(z)): 0.0814 / 0.0789\n", + "[1515/1600][6/8] Loss_D: 0.7193 Loss_G: 1.8732 D(x): 0.6034 D(G(z)): 0.0782 / 0.0792\n", + "[1516/1600][0/8] Loss_D: 0.7466 Loss_G: 1.8804 D(x): 0.6232 D(G(z)): 0.0761 / 0.0778\n", + "[1516/1600][2/8] Loss_D: 0.7555 Loss_G: 1.8019 D(x): 0.6349 D(G(z)): 0.0876 / 0.0864\n", + "[1516/1600][4/8] Loss_D: 0.7567 Loss_G: 1.9268 D(x): 0.6120 D(G(z)): 0.0740 / 0.0729\n", + "[1516/1600][6/8] Loss_D: 0.7562 Loss_G: 1.8895 D(x): 0.6071 D(G(z)): 0.0765 / 0.0760\n", + "[1517/1600][0/8] Loss_D: 0.7204 Loss_G: 1.8392 D(x): 0.6184 D(G(z)): 0.0806 / 0.0824\n", + "[1517/1600][2/8] Loss_D: 0.7169 Loss_G: 1.8574 D(x): 0.6693 D(G(z)): 0.0797 / 0.0798\n", + "[1517/1600][4/8] Loss_D: 0.7197 Loss_G: 1.7446 D(x): 0.6073 D(G(z)): 0.0931 / 0.0950\n", + "[1517/1600][6/8] Loss_D: 0.7982 Loss_G: 1.7495 D(x): 0.6498 D(G(z)): 0.0976 / 0.0952\n", + "[1518/1600][0/8] Loss_D: 0.7368 Loss_G: 1.8194 D(x): 0.6490 D(G(z)): 0.0880 / 0.0860\n", + "[1518/1600][2/8] Loss_D: 0.7488 Loss_G: 1.8159 D(x): 0.6403 D(G(z)): 0.0862 / 0.0840\n", + "[1518/1600][4/8] Loss_D: 0.7851 Loss_G: 1.8894 D(x): 0.6149 D(G(z)): 0.0808 / 0.0767\n", + "[1518/1600][6/8] Loss_D: 0.7708 Loss_G: 1.8748 D(x): 0.5649 D(G(z)): 0.0785 / 0.0790\n", + "[1519/1600][0/8] Loss_D: 0.7615 Loss_G: 1.8942 D(x): 0.6049 D(G(z)): 0.0782 / 0.0772\n", + "[1519/1600][2/8] Loss_D: 0.7265 Loss_G: 1.9050 D(x): 0.6187 D(G(z)): 0.0739 / 0.0751\n", + "[1519/1600][4/8] Loss_D: 0.7521 Loss_G: 1.8818 D(x): 0.5905 D(G(z)): 0.0774 / 0.0776\n", + "[1519/1600][6/8] Loss_D: 0.6886 Loss_G: 1.8693 D(x): 0.6204 D(G(z)): 0.0746 / 0.0787\n", + "[1520/1600][0/8] Loss_D: 0.7561 Loss_G: 1.7183 D(x): 0.6100 D(G(z)): 0.0961 / 0.0986\n", + "[1520/1600][2/8] Loss_D: 0.7065 Loss_G: 1.8894 D(x): 0.6267 D(G(z)): 0.0771 / 0.0766\n", + "[1520/1600][4/8] Loss_D: 0.7452 Loss_G: 1.8205 D(x): 0.6653 D(G(z)): 0.0876 / 0.0858\n", + "[1520/1600][6/8] Loss_D: 0.7531 Loss_G: 1.8826 D(x): 0.5677 D(G(z)): 0.0769 / 0.0781\n", + "[1521/1600][0/8] Loss_D: 0.7480 Loss_G: 1.8335 D(x): 0.6021 D(G(z)): 0.0872 / 0.0854\n", + "[1521/1600][2/8] Loss_D: 0.7437 Loss_G: 1.9270 D(x): 0.5680 D(G(z)): 0.0721 / 0.0737\n", + "[1521/1600][4/8] Loss_D: 0.7168 Loss_G: 1.8227 D(x): 0.6506 D(G(z)): 0.0860 / 0.0869\n", + "[1521/1600][6/8] Loss_D: 0.7098 Loss_G: 1.8410 D(x): 0.6239 D(G(z)): 0.0808 / 0.0813\n", + "[1522/1600][0/8] Loss_D: 0.7811 Loss_G: 1.7970 D(x): 0.6014 D(G(z)): 0.0880 / 0.0877\n", + "[1522/1600][2/8] Loss_D: 0.6945 Loss_G: 1.7955 D(x): 0.6614 D(G(z)): 0.0877 / 0.0878\n", + "[1522/1600][4/8] Loss_D: 0.7291 Loss_G: 1.7735 D(x): 0.6453 D(G(z)): 0.0907 / 0.0910\n", + "[1522/1600][6/8] Loss_D: 0.7134 Loss_G: 1.8148 D(x): 0.6577 D(G(z)): 0.0854 / 0.0864\n", + "[1523/1600][0/8] Loss_D: 0.6986 Loss_G: 1.7839 D(x): 0.6693 D(G(z)): 0.0916 / 0.0898\n", + "[1523/1600][2/8] Loss_D: 0.7246 Loss_G: 1.8589 D(x): 0.6159 D(G(z)): 0.0805 / 0.0799\n", + "[1523/1600][4/8] Loss_D: 0.7581 Loss_G: 1.7428 D(x): 0.5689 D(G(z)): 0.0920 / 0.0968\n", + "[1523/1600][6/8] Loss_D: 0.7245 Loss_G: 1.7673 D(x): 0.6791 D(G(z)): 0.0903 / 0.0906\n", + "[1524/1600][0/8] Loss_D: 0.8053 Loss_G: 1.8278 D(x): 0.6054 D(G(z)): 0.0898 / 0.0843\n", + "[1524/1600][2/8] Loss_D: 0.7813 Loss_G: 1.8865 D(x): 0.6224 D(G(z)): 0.0790 / 0.0757\n", + "[1524/1600][4/8] Loss_D: 0.7250 Loss_G: 1.8252 D(x): 0.6407 D(G(z)): 0.0841 / 0.0843\n", + "[1524/1600][6/8] Loss_D: 0.7719 Loss_G: 1.8093 D(x): 0.6513 D(G(z)): 0.0908 / 0.0867\n", + "[1525/1600][0/8] Loss_D: 0.7342 Loss_G: 1.8481 D(x): 0.5897 D(G(z)): 0.0794 / 0.0805\n", + "[1525/1600][2/8] Loss_D: 0.6553 Loss_G: 1.8152 D(x): 0.6694 D(G(z)): 0.0830 / 0.0859\n", + "[1525/1600][4/8] Loss_D: 0.7652 Loss_G: 1.7168 D(x): 0.5732 D(G(z)): 0.0934 / 0.0971\n", + "[1525/1600][6/8] Loss_D: 0.8089 Loss_G: 1.8319 D(x): 0.6964 D(G(z)): 0.0898 / 0.0838\n", + "[1526/1600][0/8] Loss_D: 0.7192 Loss_G: 1.8575 D(x): 0.6100 D(G(z)): 0.0815 / 0.0807\n", + "[1526/1600][2/8] Loss_D: 0.7068 Loss_G: 1.8814 D(x): 0.6305 D(G(z)): 0.0765 / 0.0774\n", + "[1526/1600][4/8] Loss_D: 0.7369 Loss_G: 1.8682 D(x): 0.6344 D(G(z)): 0.0793 / 0.0799\n", + "[1526/1600][6/8] Loss_D: 0.7345 Loss_G: 1.7426 D(x): 0.6001 D(G(z)): 0.0931 / 0.0960\n", + "[1527/1600][0/8] Loss_D: 0.7553 Loss_G: 1.6946 D(x): 0.6465 D(G(z)): 0.1016 / 0.1018\n", + "[1527/1600][2/8] Loss_D: 0.7232 Loss_G: 1.7159 D(x): 0.6089 D(G(z)): 0.0941 / 0.0988\n", + "[1527/1600][4/8] Loss_D: 0.7510 Loss_G: 1.6161 D(x): 0.6652 D(G(z)): 0.1150 / 0.1125\n", + "[1527/1600][6/8] Loss_D: 0.7117 Loss_G: 1.7495 D(x): 0.6951 D(G(z)): 0.0997 / 0.0954\n", + "[1528/1600][0/8] Loss_D: 0.7892 Loss_G: 1.8392 D(x): 0.6521 D(G(z)): 0.0871 / 0.0820\n", + "[1528/1600][2/8] Loss_D: 0.7528 Loss_G: 1.8501 D(x): 0.6271 D(G(z)): 0.0855 / 0.0830\n", + "[1528/1600][4/8] Loss_D: 0.7650 Loss_G: 1.8977 D(x): 0.5932 D(G(z)): 0.0796 / 0.0765\n", + "[1528/1600][6/8] Loss_D: 0.7881 Loss_G: 1.9362 D(x): 0.5268 D(G(z)): 0.0714 / 0.0720\n", + "[1529/1600][0/8] Loss_D: 0.7824 Loss_G: 1.9719 D(x): 0.5918 D(G(z)): 0.0701 / 0.0690\n", + "[1529/1600][2/8] Loss_D: 0.7747 Loss_G: 1.8908 D(x): 0.6013 D(G(z)): 0.0791 / 0.0787\n", + "[1529/1600][4/8] Loss_D: 0.7664 Loss_G: 1.8757 D(x): 0.5414 D(G(z)): 0.0737 / 0.0773\n", + "[1529/1600][6/8] Loss_D: 0.7202 Loss_G: 1.8657 D(x): 0.6246 D(G(z)): 0.0787 / 0.0795\n", + "[1530/1600][0/8] Loss_D: 0.7426 Loss_G: 1.9540 D(x): 0.6294 D(G(z)): 0.0730 / 0.0713\n", + "[1530/1600][2/8] Loss_D: 0.7495 Loss_G: 1.8699 D(x): 0.5814 D(G(z)): 0.0773 / 0.0789\n", + "[1530/1600][4/8] Loss_D: 0.7245 Loss_G: 1.8763 D(x): 0.5753 D(G(z)): 0.0736 / 0.0781\n", + "[1530/1600][6/8] Loss_D: 0.7161 Loss_G: 1.7058 D(x): 0.6595 D(G(z)): 0.0979 / 0.0996\n", + "[1531/1600][0/8] Loss_D: 0.7043 Loss_G: 1.8256 D(x): 0.6127 D(G(z)): 0.0810 / 0.0836\n", + "[1531/1600][2/8] Loss_D: 0.7329 Loss_G: 1.6717 D(x): 0.6097 D(G(z)): 0.1025 / 0.1045\n", + "[1531/1600][4/8] Loss_D: 0.7931 Loss_G: 1.7916 D(x): 0.6833 D(G(z)): 0.0958 / 0.0895\n", + "[1531/1600][6/8] Loss_D: 0.7893 Loss_G: 1.9438 D(x): 0.6338 D(G(z)): 0.0773 / 0.0723\n", + "[1532/1600][0/8] Loss_D: 0.7732 Loss_G: 1.9216 D(x): 0.6067 D(G(z)): 0.0764 / 0.0727\n", + "[1532/1600][2/8] Loss_D: 0.7683 Loss_G: 1.9554 D(x): 0.5517 D(G(z)): 0.0681 / 0.0689\n", + "[1532/1600][4/8] Loss_D: 0.7066 Loss_G: 1.8582 D(x): 0.5957 D(G(z)): 0.0754 / 0.0802\n", + "[1532/1600][6/8] Loss_D: 0.7493 Loss_G: 1.7494 D(x): 0.5757 D(G(z)): 0.0923 / 0.0959\n", + "[1533/1600][0/8] Loss_D: 0.7868 Loss_G: 1.7799 D(x): 0.5837 D(G(z)): 0.0907 / 0.0903\n", + "[1533/1600][2/8] Loss_D: 0.7427 Loss_G: 1.8137 D(x): 0.6633 D(G(z)): 0.0897 / 0.0873\n", + "[1533/1600][4/8] Loss_D: 0.7323 Loss_G: 1.8072 D(x): 0.5971 D(G(z)): 0.0836 / 0.0861\n", + "[1533/1600][6/8] Loss_D: 0.6892 Loss_G: 1.8237 D(x): 0.6246 D(G(z)): 0.0811 / 0.0846\n", + "[1534/1600][0/8] Loss_D: 0.7850 Loss_G: 1.8394 D(x): 0.6516 D(G(z)): 0.0870 / 0.0821\n", + "[1534/1600][2/8] Loss_D: 0.7519 Loss_G: 1.8813 D(x): 0.6227 D(G(z)): 0.0825 / 0.0801\n", + "[1534/1600][4/8] Loss_D: 0.7584 Loss_G: 1.9491 D(x): 0.5712 D(G(z)): 0.0720 / 0.0718\n", + "[1534/1600][6/8] Loss_D: 0.7410 Loss_G: 1.9992 D(x): 0.6162 D(G(z)): 0.0663 / 0.0650\n", + "[1535/1600][0/8] Loss_D: 0.7540 Loss_G: 1.9933 D(x): 0.6013 D(G(z)): 0.0685 / 0.0673\n", + "[1535/1600][2/8] Loss_D: 0.6768 Loss_G: 1.9590 D(x): 0.6283 D(G(z)): 0.0675 / 0.0698\n", + "[1535/1600][4/8] Loss_D: 0.7495 Loss_G: 1.8817 D(x): 0.5722 D(G(z)): 0.0765 / 0.0786\n", + "[1535/1600][6/8] Loss_D: 0.7727 Loss_G: 1.9629 D(x): 0.5785 D(G(z)): 0.0676 / 0.0684\n", + "[1536/1600][0/8] Loss_D: 0.7409 Loss_G: 1.8125 D(x): 0.6232 D(G(z)): 0.0844 / 0.0864\n", + "[1536/1600][2/8] Loss_D: 0.6958 Loss_G: 1.8721 D(x): 0.6345 D(G(z)): 0.0773 / 0.0787\n", + "[1536/1600][4/8] Loss_D: 0.7395 Loss_G: 1.7690 D(x): 0.5836 D(G(z)): 0.0894 / 0.0932\n", + "[1536/1600][6/8] Loss_D: 0.7571 Loss_G: 1.8116 D(x): 0.6448 D(G(z)): 0.0889 / 0.0859\n", + "[1537/1600][0/8] Loss_D: 0.7509 Loss_G: 1.9084 D(x): 0.6333 D(G(z)): 0.0776 / 0.0753\n", + "[1537/1600][2/8] Loss_D: 0.7442 Loss_G: 1.8972 D(x): 0.5998 D(G(z)): 0.0784 / 0.0772\n", + "[1537/1600][4/8] Loss_D: 0.7636 Loss_G: 1.9363 D(x): 0.6116 D(G(z)): 0.0738 / 0.0727\n", + "[1537/1600][6/8] Loss_D: 0.7389 Loss_G: 1.9058 D(x): 0.5988 D(G(z)): 0.0747 / 0.0748\n", + "[1538/1600][0/8] Loss_D: 0.7086 Loss_G: 1.7868 D(x): 0.6134 D(G(z)): 0.0863 / 0.0900\n", + "[1538/1600][2/8] Loss_D: 0.7342 Loss_G: 1.9272 D(x): 0.6359 D(G(z)): 0.0750 / 0.0736\n", + "[1538/1600][4/8] Loss_D: 0.7222 Loss_G: 1.8855 D(x): 0.6351 D(G(z)): 0.0769 / 0.0768\n", + "[1538/1600][6/8] Loss_D: 0.7512 Loss_G: 1.8065 D(x): 0.5619 D(G(z)): 0.0815 / 0.0864\n", + "[1539/1600][0/8] Loss_D: 0.7490 Loss_G: 1.7920 D(x): 0.6307 D(G(z)): 0.0889 / 0.0894\n", + "[1539/1600][2/8] Loss_D: 0.7421 Loss_G: 1.8017 D(x): 0.6249 D(G(z)): 0.0863 / 0.0859\n", + "[1539/1600][4/8] Loss_D: 0.7763 Loss_G: 1.8301 D(x): 0.6233 D(G(z)): 0.0859 / 0.0831\n", + "[1539/1600][6/8] Loss_D: 0.7932 Loss_G: 1.8516 D(x): 0.6012 D(G(z)): 0.0857 / 0.0813\n", + "[1540/1600][0/8] Loss_D: 0.7760 Loss_G: 1.9340 D(x): 0.5852 D(G(z)): 0.0752 / 0.0726\n", + "[1540/1600][2/8] Loss_D: 0.7512 Loss_G: 1.9542 D(x): 0.5362 D(G(z)): 0.0647 / 0.0691\n", + "[1540/1600][4/8] Loss_D: 0.7503 Loss_G: 1.8225 D(x): 0.6206 D(G(z)): 0.0824 / 0.0835\n", + "[1540/1600][6/8] Loss_D: 0.7838 Loss_G: 1.8819 D(x): 0.6311 D(G(z)): 0.0800 / 0.0775\n", + "[1541/1600][0/8] Loss_D: 0.7829 Loss_G: 1.8857 D(x): 0.6157 D(G(z)): 0.0774 / 0.0753\n", + "[1541/1600][2/8] Loss_D: 0.7680 Loss_G: 1.8655 D(x): 0.5654 D(G(z)): 0.0807 / 0.0801\n", + "[1541/1600][4/8] Loss_D: 0.7176 Loss_G: 1.8410 D(x): 0.6489 D(G(z)): 0.0809 / 0.0817\n", + "[1541/1600][6/8] Loss_D: 0.7832 Loss_G: 1.8533 D(x): 0.5867 D(G(z)): 0.0830 / 0.0808\n", + "[1542/1600][0/8] Loss_D: 0.7079 Loss_G: 1.8543 D(x): 0.5867 D(G(z)): 0.0746 / 0.0792\n", + "[1542/1600][2/8] Loss_D: 0.7454 Loss_G: 1.8504 D(x): 0.5907 D(G(z)): 0.0816 / 0.0823\n", + "[1542/1600][4/8] Loss_D: 0.7426 Loss_G: 1.8515 D(x): 0.6246 D(G(z)): 0.0813 / 0.0814\n", + "[1542/1600][6/8] Loss_D: 0.7701 Loss_G: 1.7918 D(x): 0.6180 D(G(z)): 0.0908 / 0.0897\n", + "[1543/1600][0/8] Loss_D: 0.7405 Loss_G: 1.8806 D(x): 0.6247 D(G(z)): 0.0815 / 0.0792\n", + "[1543/1600][2/8] Loss_D: 0.7672 Loss_G: 1.8458 D(x): 0.5768 D(G(z)): 0.0832 / 0.0833\n", + "[1543/1600][4/8] Loss_D: 0.7617 Loss_G: 1.9129 D(x): 0.5806 D(G(z)): 0.0741 / 0.0745\n", + "[1543/1600][6/8] Loss_D: 0.7777 Loss_G: 1.8688 D(x): 0.5978 D(G(z)): 0.0786 / 0.0792\n", + "[1544/1600][0/8] Loss_D: 0.7055 Loss_G: 1.8107 D(x): 0.6342 D(G(z)): 0.0860 / 0.0867\n", + "[1544/1600][2/8] Loss_D: 0.7708 Loss_G: 1.8490 D(x): 0.6225 D(G(z)): 0.0847 / 0.0821\n", + "[1544/1600][4/8] Loss_D: 0.7398 Loss_G: 1.8546 D(x): 0.5835 D(G(z)): 0.0791 / 0.0808\n", + "[1544/1600][6/8] Loss_D: 0.7260 Loss_G: 1.7779 D(x): 0.6252 D(G(z)): 0.0887 / 0.0916\n", + "[1545/1600][0/8] Loss_D: 0.7265 Loss_G: 1.7743 D(x): 0.6245 D(G(z)): 0.0860 / 0.0885\n", + "[1545/1600][2/8] Loss_D: 0.7445 Loss_G: 1.7031 D(x): 0.6419 D(G(z)): 0.1008 / 0.1010\n", + "[1545/1600][4/8] Loss_D: 0.7433 Loss_G: 1.7780 D(x): 0.6408 D(G(z)): 0.0925 / 0.0896\n", + "[1545/1600][6/8] Loss_D: 0.7558 Loss_G: 1.8778 D(x): 0.6896 D(G(z)): 0.0845 / 0.0789\n", + "[1546/1600][0/8] Loss_D: 0.7655 Loss_G: 1.7893 D(x): 0.5941 D(G(z)): 0.0913 / 0.0905\n", + "[1546/1600][2/8] Loss_D: 0.6872 Loss_G: 1.8354 D(x): 0.6316 D(G(z)): 0.0815 / 0.0831\n", + "[1546/1600][4/8] Loss_D: 0.7406 Loss_G: 1.7367 D(x): 0.5832 D(G(z)): 0.0897 / 0.0958\n", + "[1546/1600][6/8] Loss_D: 0.7579 Loss_G: 1.7534 D(x): 0.6251 D(G(z)): 0.0940 / 0.0939\n", + "[1547/1600][0/8] Loss_D: 0.7413 Loss_G: 1.8260 D(x): 0.6317 D(G(z)): 0.0859 / 0.0854\n", + "[1547/1600][2/8] Loss_D: 0.7688 Loss_G: 1.7992 D(x): 0.5913 D(G(z)): 0.0885 / 0.0873\n", + "[1547/1600][4/8] Loss_D: 0.7271 Loss_G: 1.8199 D(x): 0.6199 D(G(z)): 0.0852 / 0.0841\n", + "[1547/1600][6/8] Loss_D: 0.7292 Loss_G: 1.8705 D(x): 0.6060 D(G(z)): 0.0793 / 0.0782\n", + "[1548/1600][0/8] Loss_D: 0.7581 Loss_G: 1.8531 D(x): 0.6125 D(G(z)): 0.0816 / 0.0810\n", + "[1548/1600][2/8] Loss_D: 0.7755 Loss_G: 1.8488 D(x): 0.6335 D(G(z)): 0.0838 / 0.0820\n", + "[1548/1600][4/8] Loss_D: 0.7599 Loss_G: 1.9554 D(x): 0.5952 D(G(z)): 0.0722 / 0.0696\n", + "[1548/1600][6/8] Loss_D: 0.7226 Loss_G: 1.9292 D(x): 0.5936 D(G(z)): 0.0693 / 0.0709\n", + "[1549/1600][0/8] Loss_D: 0.7565 Loss_G: 1.9836 D(x): 0.5825 D(G(z)): 0.0678 / 0.0681\n", + "[1549/1600][2/8] Loss_D: 0.7407 Loss_G: 1.8847 D(x): 0.6077 D(G(z)): 0.0760 / 0.0770\n", + "[1549/1600][4/8] Loss_D: 0.7358 Loss_G: 1.9285 D(x): 0.5814 D(G(z)): 0.0717 / 0.0733\n", + "[1549/1600][6/8] Loss_D: 0.7177 Loss_G: 1.8424 D(x): 0.6519 D(G(z)): 0.0802 / 0.0816\n", + "[1550/1600][0/8] Loss_D: 0.7804 Loss_G: 1.7652 D(x): 0.6380 D(G(z)): 0.0948 / 0.0927\n", + "[1550/1600][2/8] Loss_D: 0.7714 Loss_G: 1.8056 D(x): 0.5920 D(G(z)): 0.0867 / 0.0862\n", + "[1550/1600][4/8] Loss_D: 0.7286 Loss_G: 1.8523 D(x): 0.5966 D(G(z)): 0.0816 / 0.0829\n", + "[1550/1600][6/8] Loss_D: 0.7217 Loss_G: 1.7848 D(x): 0.6029 D(G(z)): 0.0851 / 0.0890\n", + "[1551/1600][0/8] Loss_D: 0.7354 Loss_G: 1.8115 D(x): 0.6530 D(G(z)): 0.0847 / 0.0846\n", + "[1551/1600][2/8] Loss_D: 0.7257 Loss_G: 1.7483 D(x): 0.6479 D(G(z)): 0.0937 / 0.0937\n", + "[1551/1600][4/8] Loss_D: 0.7734 Loss_G: 1.7457 D(x): 0.6605 D(G(z)): 0.0966 / 0.0932\n", + "[1551/1600][6/8] Loss_D: 0.7676 Loss_G: 1.8571 D(x): 0.5965 D(G(z)): 0.0836 / 0.0802\n", + "[1552/1600][0/8] Loss_D: 0.7214 Loss_G: 1.8370 D(x): 0.6537 D(G(z)): 0.0841 / 0.0829\n", + "[1552/1600][2/8] Loss_D: 0.7737 Loss_G: 1.8563 D(x): 0.5946 D(G(z)): 0.0833 / 0.0829\n", + "[1552/1600][4/8] Loss_D: 0.7146 Loss_G: 1.8510 D(x): 0.6455 D(G(z)): 0.0803 / 0.0806\n", + "[1552/1600][6/8] Loss_D: 0.7007 Loss_G: 1.8352 D(x): 0.6070 D(G(z)): 0.0802 / 0.0841\n", + "[1553/1600][0/8] Loss_D: 0.7185 Loss_G: 1.8290 D(x): 0.5973 D(G(z)): 0.0825 / 0.0844\n", + "[1553/1600][2/8] Loss_D: 0.7327 Loss_G: 1.7971 D(x): 0.6289 D(G(z)): 0.0875 / 0.0891\n", + "[1553/1600][4/8] Loss_D: 0.7757 Loss_G: 1.7600 D(x): 0.6798 D(G(z)): 0.0974 / 0.0930\n", + "[1553/1600][6/8] Loss_D: 0.7982 Loss_G: 1.8502 D(x): 0.6212 D(G(z)): 0.0860 / 0.0820\n", + "[1554/1600][0/8] Loss_D: 0.7996 Loss_G: 1.8849 D(x): 0.6257 D(G(z)): 0.0813 / 0.0774\n", + "[1554/1600][2/8] Loss_D: 0.7298 Loss_G: 1.8669 D(x): 0.6258 D(G(z)): 0.0774 / 0.0782\n", + "[1554/1600][4/8] Loss_D: 0.6955 Loss_G: 1.8406 D(x): 0.6343 D(G(z)): 0.0824 / 0.0828\n", + "[1554/1600][6/8] Loss_D: 0.7760 Loss_G: 1.8945 D(x): 0.5879 D(G(z)): 0.0779 / 0.0762\n", + "[1555/1600][0/8] Loss_D: 0.7755 Loss_G: 1.8340 D(x): 0.5975 D(G(z)): 0.0854 / 0.0840\n", + "[1555/1600][2/8] Loss_D: 0.7836 Loss_G: 1.7902 D(x): 0.6064 D(G(z)): 0.0908 / 0.0887\n", + "[1555/1600][4/8] Loss_D: 0.7886 Loss_G: 1.8621 D(x): 0.6330 D(G(z)): 0.0827 / 0.0799\n", + "[1555/1600][6/8] Loss_D: 0.7056 Loss_G: 1.9237 D(x): 0.5858 D(G(z)): 0.0696 / 0.0719\n", + "[1556/1600][0/8] Loss_D: 0.7274 Loss_G: 1.8341 D(x): 0.6044 D(G(z)): 0.0816 / 0.0849\n", + "[1556/1600][2/8] Loss_D: 0.6949 Loss_G: 1.8346 D(x): 0.6543 D(G(z)): 0.0836 / 0.0833\n", + "[1556/1600][4/8] Loss_D: 0.7047 Loss_G: 1.8366 D(x): 0.6090 D(G(z)): 0.0786 / 0.0820\n", + "[1556/1600][6/8] Loss_D: 0.7880 Loss_G: 1.8474 D(x): 0.6518 D(G(z)): 0.0859 / 0.0839\n", + "[1557/1600][0/8] Loss_D: 0.7296 Loss_G: 1.8747 D(x): 0.6296 D(G(z)): 0.0793 / 0.0773\n", + "[1557/1600][2/8] Loss_D: 0.7307 Loss_G: 1.8574 D(x): 0.5884 D(G(z)): 0.0803 / 0.0810\n", + "[1557/1600][4/8] Loss_D: 0.7004 Loss_G: 1.8930 D(x): 0.6118 D(G(z)): 0.0756 / 0.0774\n", + "[1557/1600][6/8] Loss_D: 0.7565 Loss_G: 1.8270 D(x): 0.5982 D(G(z)): 0.0831 / 0.0862\n", + "[1558/1600][0/8] Loss_D: 0.7924 Loss_G: 1.7749 D(x): 0.6335 D(G(z)): 0.0929 / 0.0902\n", + "[1558/1600][2/8] Loss_D: 0.7463 Loss_G: 1.8192 D(x): 0.6011 D(G(z)): 0.0841 / 0.0849\n", + "[1558/1600][4/8] Loss_D: 0.6749 Loss_G: 1.7466 D(x): 0.6577 D(G(z)): 0.0910 / 0.0935\n", + "[1558/1600][6/8] Loss_D: 0.8052 Loss_G: 1.8438 D(x): 0.6466 D(G(z)): 0.0892 / 0.0823\n", + "[1559/1600][0/8] Loss_D: 0.7583 Loss_G: 1.9317 D(x): 0.5625 D(G(z)): 0.0720 / 0.0721\n", + "[1559/1600][2/8] Loss_D: 0.7701 Loss_G: 1.8426 D(x): 0.6119 D(G(z)): 0.0838 / 0.0829\n", + "[1559/1600][4/8] Loss_D: 0.7241 Loss_G: 1.8780 D(x): 0.6065 D(G(z)): 0.0789 / 0.0795\n", + "[1559/1600][6/8] Loss_D: 0.7688 Loss_G: 1.9830 D(x): 0.6217 D(G(z)): 0.0705 / 0.0675\n", + "[1560/1600][0/8] Loss_D: 0.7549 Loss_G: 1.9190 D(x): 0.5992 D(G(z)): 0.0731 / 0.0726\n", + "[1560/1600][2/8] Loss_D: 0.7576 Loss_G: 2.0522 D(x): 0.6313 D(G(z)): 0.0621 / 0.0597\n", + "[1560/1600][4/8] Loss_D: 0.8000 Loss_G: 1.9481 D(x): 0.5165 D(G(z)): 0.0706 / 0.0718\n", + "[1560/1600][6/8] Loss_D: 0.7165 Loss_G: 1.9624 D(x): 0.6084 D(G(z)): 0.0648 / 0.0683\n", + "[1561/1600][0/8] Loss_D: 0.7832 Loss_G: 1.8796 D(x): 0.5944 D(G(z)): 0.0763 / 0.0763\n", + "[1561/1600][2/8] Loss_D: 0.7763 Loss_G: 1.8526 D(x): 0.5645 D(G(z)): 0.0801 / 0.0807\n", + "[1561/1600][4/8] Loss_D: 0.7537 Loss_G: 1.8400 D(x): 0.6333 D(G(z)): 0.0825 / 0.0806\n", + "[1561/1600][6/8] Loss_D: 0.6845 Loss_G: 1.9518 D(x): 0.6347 D(G(z)): 0.0685 / 0.0698\n", + "[1562/1600][0/8] Loss_D: 0.7904 Loss_G: 1.8695 D(x): 0.6468 D(G(z)): 0.0812 / 0.0795\n", + "[1562/1600][2/8] Loss_D: 0.7085 Loss_G: 1.9300 D(x): 0.6543 D(G(z)): 0.0718 / 0.0707\n", + "[1562/1600][4/8] Loss_D: 0.7877 Loss_G: 1.8661 D(x): 0.5672 D(G(z)): 0.0801 / 0.0808\n", + "[1562/1600][6/8] Loss_D: 0.7142 Loss_G: 1.8343 D(x): 0.5994 D(G(z)): 0.0787 / 0.0819\n", + "[1563/1600][0/8] Loss_D: 0.7073 Loss_G: 1.8054 D(x): 0.6150 D(G(z)): 0.0844 / 0.0874\n", + "[1563/1600][2/8] Loss_D: 0.7216 Loss_G: 1.7950 D(x): 0.6224 D(G(z)): 0.0874 / 0.0872\n", + "[1563/1600][4/8] Loss_D: 0.7878 Loss_G: 1.8265 D(x): 0.6481 D(G(z)): 0.0872 / 0.0828\n", + "[1563/1600][6/8] Loss_D: 0.7756 Loss_G: 1.7506 D(x): 0.5927 D(G(z)): 0.0940 / 0.0948\n", + "[1564/1600][0/8] Loss_D: 0.7625 Loss_G: 1.8146 D(x): 0.6441 D(G(z)): 0.0887 / 0.0848\n", + "[1564/1600][2/8] Loss_D: 0.7128 Loss_G: 1.8827 D(x): 0.6316 D(G(z)): 0.0781 / 0.0799\n", + "[1564/1600][4/8] Loss_D: 0.6887 Loss_G: 1.8133 D(x): 0.6462 D(G(z)): 0.0834 / 0.0837\n", + "[1564/1600][6/8] Loss_D: 0.7962 Loss_G: 1.7716 D(x): 0.6250 D(G(z)): 0.0950 / 0.0922\n", + "[1565/1600][0/8] Loss_D: 0.7641 Loss_G: 1.8826 D(x): 0.6239 D(G(z)): 0.0790 / 0.0770\n", + "[1565/1600][2/8] Loss_D: 0.7806 Loss_G: 1.8960 D(x): 0.6170 D(G(z)): 0.0802 / 0.0772\n", + "[1565/1600][4/8] Loss_D: 0.7391 Loss_G: 1.8705 D(x): 0.5908 D(G(z)): 0.0784 / 0.0796\n", + "[1565/1600][6/8] Loss_D: 0.7776 Loss_G: 1.8279 D(x): 0.5871 D(G(z)): 0.0818 / 0.0826\n", + "[1566/1600][0/8] Loss_D: 0.6752 Loss_G: 1.7631 D(x): 0.6640 D(G(z)): 0.0920 / 0.0942\n", + "[1566/1600][2/8] Loss_D: 0.7827 Loss_G: 1.8969 D(x): 0.5935 D(G(z)): 0.0771 / 0.0755\n", + "[1566/1600][4/8] Loss_D: 0.7856 Loss_G: 1.8605 D(x): 0.5611 D(G(z)): 0.0812 / 0.0799\n", + "[1566/1600][6/8] Loss_D: 0.7766 Loss_G: 1.8110 D(x): 0.5710 D(G(z)): 0.0873 / 0.0871\n", + "[1567/1600][0/8] Loss_D: 0.6913 Loss_G: 1.7903 D(x): 0.6287 D(G(z)): 0.0843 / 0.0882\n", + "[1567/1600][2/8] Loss_D: 0.7956 Loss_G: 1.8441 D(x): 0.6367 D(G(z)): 0.0877 / 0.0840\n", + "[1567/1600][4/8] Loss_D: 0.7517 Loss_G: 1.8163 D(x): 0.5973 D(G(z)): 0.0842 / 0.0855\n", + "[1567/1600][6/8] Loss_D: 0.6775 Loss_G: 1.8806 D(x): 0.6525 D(G(z)): 0.0760 / 0.0764\n", + "[1568/1600][0/8] Loss_D: 0.6755 Loss_G: 1.8335 D(x): 0.6471 D(G(z)): 0.0838 / 0.0843\n", + "[1568/1600][2/8] Loss_D: 0.7107 Loss_G: 1.7674 D(x): 0.6145 D(G(z)): 0.0883 / 0.0925\n", + "[1568/1600][4/8] Loss_D: 0.7081 Loss_G: 1.7270 D(x): 0.6249 D(G(z)): 0.0960 / 0.0980\n", + "[1568/1600][6/8] Loss_D: 0.7494 Loss_G: 1.7382 D(x): 0.6247 D(G(z)): 0.0990 / 0.0990\n", + "[1569/1600][0/8] Loss_D: 0.7295 Loss_G: 1.7082 D(x): 0.6742 D(G(z)): 0.1035 / 0.1008\n", + "[1569/1600][2/8] Loss_D: 0.7785 Loss_G: 1.7892 D(x): 0.6005 D(G(z)): 0.0946 / 0.0923\n", + "[1569/1600][4/8] Loss_D: 0.7397 Loss_G: 1.8986 D(x): 0.6083 D(G(z)): 0.0781 / 0.0774\n", + "[1569/1600][6/8] Loss_D: 0.7986 Loss_G: 1.7650 D(x): 0.6286 D(G(z)): 0.0960 / 0.0919\n", + "[1570/1600][0/8] Loss_D: 0.7437 Loss_G: 1.8308 D(x): 0.5859 D(G(z)): 0.0828 / 0.0832\n", + "[1570/1600][2/8] Loss_D: 0.7286 Loss_G: 1.8339 D(x): 0.5811 D(G(z)): 0.0829 / 0.0857\n", + "[1570/1600][4/8] Loss_D: 0.7563 Loss_G: 1.8789 D(x): 0.6187 D(G(z)): 0.0774 / 0.0774\n", + "[1570/1600][6/8] Loss_D: 0.7985 Loss_G: 1.8177 D(x): 0.5747 D(G(z)): 0.0869 / 0.0829\n", + "[1571/1600][0/8] Loss_D: 0.7781 Loss_G: 1.9086 D(x): 0.5807 D(G(z)): 0.0755 / 0.0748\n", + "[1571/1600][2/8] Loss_D: 0.7367 Loss_G: 1.8297 D(x): 0.6359 D(G(z)): 0.0851 / 0.0859\n", + "[1571/1600][4/8] Loss_D: 0.7664 Loss_G: 1.9018 D(x): 0.6013 D(G(z)): 0.0751 / 0.0757\n", + "[1571/1600][6/8] Loss_D: 0.7218 Loss_G: 1.8820 D(x): 0.6460 D(G(z)): 0.0766 / 0.0767\n", + "[1572/1600][0/8] Loss_D: 0.7507 Loss_G: 1.8274 D(x): 0.6415 D(G(z)): 0.0887 / 0.0859\n", + "[1572/1600][2/8] Loss_D: 0.7807 Loss_G: 1.8548 D(x): 0.5968 D(G(z)): 0.0837 / 0.0807\n", + "[1572/1600][4/8] Loss_D: 0.7853 Loss_G: 1.9594 D(x): 0.5642 D(G(z)): 0.0709 / 0.0699\n", + "[1572/1600][6/8] Loss_D: 0.8036 Loss_G: 1.8932 D(x): 0.5179 D(G(z)): 0.0752 / 0.0756\n", + "[1573/1600][0/8] Loss_D: 0.7000 Loss_G: 1.9397 D(x): 0.6162 D(G(z)): 0.0695 / 0.0724\n", + "[1573/1600][2/8] Loss_D: 0.7387 Loss_G: 1.7732 D(x): 0.5921 D(G(z)): 0.0866 / 0.0904\n", + "[1573/1600][4/8] Loss_D: 0.7540 Loss_G: 1.8093 D(x): 0.6374 D(G(z)): 0.0859 / 0.0866\n", + "[1573/1600][6/8] Loss_D: 0.7282 Loss_G: 1.7710 D(x): 0.6238 D(G(z)): 0.0914 / 0.0908\n", + "[1574/1600][0/8] Loss_D: 0.6938 Loss_G: 1.8034 D(x): 0.6322 D(G(z)): 0.0847 / 0.0869\n", + "[1574/1600][2/8] Loss_D: 0.8032 Loss_G: 1.7941 D(x): 0.6394 D(G(z)): 0.0941 / 0.0891\n", + "[1574/1600][4/8] Loss_D: 0.6864 Loss_G: 1.7705 D(x): 0.6316 D(G(z)): 0.0877 / 0.0894\n", + "[1574/1600][6/8] Loss_D: 0.6753 Loss_G: 1.8585 D(x): 0.6532 D(G(z)): 0.0771 / 0.0789\n", + "[1575/1600][0/8] Loss_D: 0.7525 Loss_G: 1.8131 D(x): 0.6788 D(G(z)): 0.0886 / 0.0855\n", + "[1575/1600][2/8] Loss_D: 0.7666 Loss_G: 1.8063 D(x): 0.6263 D(G(z)): 0.0882 / 0.0862\n", + "[1575/1600][4/8] Loss_D: 0.7714 Loss_G: 1.8394 D(x): 0.6523 D(G(z)): 0.0839 / 0.0813\n", + "[1575/1600][6/8] Loss_D: 0.8054 Loss_G: 1.8067 D(x): 0.5556 D(G(z)): 0.0900 / 0.0885\n", + "[1576/1600][0/8] Loss_D: 0.7530 Loss_G: 1.8775 D(x): 0.5880 D(G(z)): 0.0777 / 0.0791\n", + "[1576/1600][2/8] Loss_D: 0.7522 Loss_G: 1.7729 D(x): 0.6329 D(G(z)): 0.0916 / 0.0911\n", + "[1576/1600][4/8] Loss_D: 0.7131 Loss_G: 1.8604 D(x): 0.6379 D(G(z)): 0.0821 / 0.0817\n", + "[1576/1600][6/8] Loss_D: 0.7770 Loss_G: 1.8001 D(x): 0.6319 D(G(z)): 0.0916 / 0.0879\n", + "[1577/1600][0/8] Loss_D: 0.7013 Loss_G: 1.9398 D(x): 0.6366 D(G(z)): 0.0732 / 0.0720\n", + "[1577/1600][2/8] Loss_D: 0.7187 Loss_G: 1.9177 D(x): 0.5802 D(G(z)): 0.0710 / 0.0743\n", + "[1577/1600][4/8] Loss_D: 0.7497 Loss_G: 1.8572 D(x): 0.5895 D(G(z)): 0.0792 / 0.0811\n", + "[1577/1600][6/8] Loss_D: 0.7216 Loss_G: 1.8574 D(x): 0.6015 D(G(z)): 0.0805 / 0.0834\n", + "[1578/1600][0/8] Loss_D: 0.7845 Loss_G: 1.8280 D(x): 0.6411 D(G(z)): 0.0855 / 0.0829\n", + "[1578/1600][2/8] Loss_D: 0.7574 Loss_G: 1.7390 D(x): 0.5962 D(G(z)): 0.0935 / 0.0947\n", + "[1578/1600][4/8] Loss_D: 0.7106 Loss_G: 1.7894 D(x): 0.6434 D(G(z)): 0.0901 / 0.0895\n", + "[1578/1600][6/8] Loss_D: 0.7896 Loss_G: 1.8708 D(x): 0.5984 D(G(z)): 0.0855 / 0.0812\n", + "[1579/1600][0/8] Loss_D: 0.7759 Loss_G: 1.9086 D(x): 0.5679 D(G(z)): 0.0764 / 0.0749\n", + "[1579/1600][2/8] Loss_D: 0.7792 Loss_G: 1.8859 D(x): 0.5417 D(G(z)): 0.0747 / 0.0769\n", + "[1579/1600][4/8] Loss_D: 0.7462 Loss_G: 1.8496 D(x): 0.5940 D(G(z)): 0.0792 / 0.0815\n", + "[1579/1600][6/8] Loss_D: 0.7156 Loss_G: 1.9038 D(x): 0.6623 D(G(z)): 0.0769 / 0.0745\n", + "[1580/1600][0/8] Loss_D: 0.7156 Loss_G: 1.9555 D(x): 0.6160 D(G(z)): 0.0691 / 0.0699\n", + "[1580/1600][2/8] Loss_D: 0.7771 Loss_G: 1.8749 D(x): 0.5828 D(G(z)): 0.0788 / 0.0786\n", + "[1580/1600][4/8] Loss_D: 0.7582 Loss_G: 1.8540 D(x): 0.6194 D(G(z)): 0.0825 / 0.0811\n", + "[1580/1600][6/8] Loss_D: 0.7587 Loss_G: 1.8983 D(x): 0.6068 D(G(z)): 0.0769 / 0.0764\n", + "[1581/1600][0/8] Loss_D: 0.7302 Loss_G: 1.8904 D(x): 0.6213 D(G(z)): 0.0768 / 0.0760\n", + "[1581/1600][2/8] Loss_D: 0.7842 Loss_G: 1.9717 D(x): 0.6035 D(G(z)): 0.0733 / 0.0701\n", + "[1581/1600][4/8] Loss_D: 0.7721 Loss_G: 1.8750 D(x): 0.5879 D(G(z)): 0.0781 / 0.0787\n", + "[1581/1600][6/8] Loss_D: 0.7225 Loss_G: 1.9799 D(x): 0.5718 D(G(z)): 0.0641 / 0.0665\n", + "[1582/1600][0/8] Loss_D: 0.6969 Loss_G: 1.8339 D(x): 0.6359 D(G(z)): 0.0808 / 0.0828\n", + "[1582/1600][2/8] Loss_D: 0.7505 Loss_G: 1.8318 D(x): 0.5873 D(G(z)): 0.0851 / 0.0866\n", + "[1582/1600][4/8] Loss_D: 0.7481 Loss_G: 1.7373 D(x): 0.5998 D(G(z)): 0.0928 / 0.0969\n", + "[1582/1600][6/8] Loss_D: 0.7526 Loss_G: 1.7696 D(x): 0.6762 D(G(z)): 0.0962 / 0.0936\n", + "[1583/1600][0/8] Loss_D: 0.7228 Loss_G: 1.7911 D(x): 0.6376 D(G(z)): 0.0874 / 0.0878\n", + "[1583/1600][2/8] Loss_D: 0.7939 Loss_G: 1.7863 D(x): 0.6516 D(G(z)): 0.0928 / 0.0887\n", + "[1583/1600][4/8] Loss_D: 0.7809 Loss_G: 1.9134 D(x): 0.5842 D(G(z)): 0.0781 / 0.0739\n", + "[1583/1600][6/8] Loss_D: 0.7602 Loss_G: 1.8983 D(x): 0.5734 D(G(z)): 0.0741 / 0.0751\n", + "[1584/1600][0/8] Loss_D: 0.8017 Loss_G: 1.8601 D(x): 0.5261 D(G(z)): 0.0822 / 0.0813\n", + "[1584/1600][2/8] Loss_D: 0.7741 Loss_G: 1.9497 D(x): 0.5873 D(G(z)): 0.0724 / 0.0708\n", + "[1584/1600][4/8] Loss_D: 0.7496 Loss_G: 1.8106 D(x): 0.5702 D(G(z)): 0.0828 / 0.0863\n", + "[1584/1600][6/8] Loss_D: 0.7673 Loss_G: 1.8536 D(x): 0.5848 D(G(z)): 0.0792 / 0.0811\n", + "[1585/1600][0/8] Loss_D: 0.7740 Loss_G: 1.8948 D(x): 0.5900 D(G(z)): 0.0779 / 0.0769\n", + "[1585/1600][2/8] Loss_D: 0.7762 Loss_G: 1.8963 D(x): 0.5731 D(G(z)): 0.0756 / 0.0758\n", + "[1585/1600][4/8] Loss_D: 0.7252 Loss_G: 1.8871 D(x): 0.6049 D(G(z)): 0.0768 / 0.0788\n", + "[1585/1600][6/8] Loss_D: 0.7826 Loss_G: 1.8686 D(x): 0.5991 D(G(z)): 0.0807 / 0.0786\n", + "[1586/1600][0/8] Loss_D: 0.7410 Loss_G: 1.9605 D(x): 0.6063 D(G(z)): 0.0701 / 0.0692\n", + "[1586/1600][2/8] Loss_D: 0.7140 Loss_G: 1.9431 D(x): 0.6049 D(G(z)): 0.0682 / 0.0700\n", + "[1586/1600][4/8] Loss_D: 0.7310 Loss_G: 1.7980 D(x): 0.6150 D(G(z)): 0.0842 / 0.0878\n", + "[1586/1600][6/8] Loss_D: 0.7713 Loss_G: 1.7054 D(x): 0.6238 D(G(z)): 0.1024 / 0.1006\n", + "[1587/1600][0/8] Loss_D: 0.7044 Loss_G: 1.9731 D(x): 0.5988 D(G(z)): 0.0681 / 0.0682\n", + "[1587/1600][2/8] Loss_D: 0.7927 Loss_G: 1.8707 D(x): 0.6073 D(G(z)): 0.0829 / 0.0799\n", + "[1587/1600][4/8] Loss_D: 0.7695 Loss_G: 1.8039 D(x): 0.5680 D(G(z)): 0.0880 / 0.0886\n", + "[1587/1600][6/8] Loss_D: 0.7326 Loss_G: 1.8834 D(x): 0.5944 D(G(z)): 0.0760 / 0.0793\n", + "[1588/1600][0/8] Loss_D: 0.7500 Loss_G: 1.8335 D(x): 0.6512 D(G(z)): 0.0864 / 0.0844\n", + "[1588/1600][2/8] Loss_D: 0.7172 Loss_G: 1.8891 D(x): 0.6360 D(G(z)): 0.0780 / 0.0771\n", + "[1588/1600][4/8] Loss_D: 0.7546 Loss_G: 1.7880 D(x): 0.6285 D(G(z)): 0.0891 / 0.0900\n", + "[1588/1600][6/8] Loss_D: 0.7174 Loss_G: 1.9466 D(x): 0.6049 D(G(z)): 0.0700 / 0.0702\n", + "[1589/1600][0/8] Loss_D: 0.7558 Loss_G: 1.8470 D(x): 0.6075 D(G(z)): 0.0832 / 0.0820\n", + "[1589/1600][2/8] Loss_D: 0.7371 Loss_G: 1.9046 D(x): 0.5736 D(G(z)): 0.0739 / 0.0756\n", + "[1589/1600][4/8] Loss_D: 0.7439 Loss_G: 1.8426 D(x): 0.6364 D(G(z)): 0.0836 / 0.0837\n", + "[1589/1600][6/8] Loss_D: 0.6805 Loss_G: 1.8996 D(x): 0.6366 D(G(z)): 0.0762 / 0.0786\n", + "[1590/1600][0/8] Loss_D: 0.6807 Loss_G: 1.7693 D(x): 0.6505 D(G(z)): 0.0880 / 0.0916\n", + "[1590/1600][2/8] Loss_D: 0.7638 Loss_G: 1.8226 D(x): 0.6384 D(G(z)): 0.0914 / 0.0870\n", + "[1590/1600][4/8] Loss_D: 0.7526 Loss_G: 1.8982 D(x): 0.5953 D(G(z)): 0.0760 / 0.0756\n", + "[1590/1600][6/8] Loss_D: 0.7703 Loss_G: 1.8480 D(x): 0.6599 D(G(z)): 0.0831 / 0.0809\n", + "[1591/1600][0/8] Loss_D: 0.7500 Loss_G: 1.8626 D(x): 0.6227 D(G(z)): 0.0815 / 0.0806\n", + "[1591/1600][2/8] Loss_D: 0.7238 Loss_G: 1.8454 D(x): 0.5811 D(G(z)): 0.0774 / 0.0807\n", + "[1591/1600][4/8] Loss_D: 0.8018 Loss_G: 1.8128 D(x): 0.6225 D(G(z)): 0.0890 / 0.0866\n", + "[1591/1600][6/8] Loss_D: 0.7602 Loss_G: 1.8130 D(x): 0.6114 D(G(z)): 0.0865 / 0.0863\n", + "[1592/1600][0/8] Loss_D: 0.7213 Loss_G: 1.8490 D(x): 0.5869 D(G(z)): 0.0782 / 0.0801\n", + "[1592/1600][2/8] Loss_D: 0.7252 Loss_G: 1.8448 D(x): 0.6118 D(G(z)): 0.0828 / 0.0832\n", + "[1592/1600][4/8] Loss_D: 0.7142 Loss_G: 1.8633 D(x): 0.6290 D(G(z)): 0.0791 / 0.0792\n", + "[1592/1600][6/8] Loss_D: 0.7391 Loss_G: 1.8619 D(x): 0.5878 D(G(z)): 0.0775 / 0.0786\n", + "[1593/1600][0/8] Loss_D: 0.7172 Loss_G: 1.8974 D(x): 0.5871 D(G(z)): 0.0732 / 0.0760\n", + "[1593/1600][2/8] Loss_D: 0.7868 Loss_G: 1.9242 D(x): 0.6606 D(G(z)): 0.0760 / 0.0722\n", + "[1593/1600][4/8] Loss_D: 0.7669 Loss_G: 1.9007 D(x): 0.6086 D(G(z)): 0.0786 / 0.0763\n", + "[1593/1600][6/8] Loss_D: 0.7756 Loss_G: 1.9531 D(x): 0.5702 D(G(z)): 0.0718 / 0.0709\n", + "[1594/1600][0/8] Loss_D: 0.7589 Loss_G: 1.8848 D(x): 0.5823 D(G(z)): 0.0758 / 0.0775\n", + "[1594/1600][2/8] Loss_D: 0.7157 Loss_G: 1.8358 D(x): 0.5901 D(G(z)): 0.0778 / 0.0823\n", + "[1594/1600][4/8] Loss_D: 0.7191 Loss_G: 1.8460 D(x): 0.6284 D(G(z)): 0.0838 / 0.0834\n", + "[1594/1600][6/8] Loss_D: 0.7399 Loss_G: 1.8132 D(x): 0.6072 D(G(z)): 0.0854 / 0.0871\n", + "[1595/1600][0/8] Loss_D: 0.7345 Loss_G: 1.7836 D(x): 0.6349 D(G(z)): 0.0877 / 0.0892\n", + "[1595/1600][2/8] Loss_D: 0.8141 Loss_G: 1.7808 D(x): 0.6499 D(G(z)): 0.0956 / 0.0909\n", + "[1595/1600][4/8] Loss_D: 0.7625 Loss_G: 1.7804 D(x): 0.5792 D(G(z)): 0.0903 / 0.0902\n", + "[1595/1600][6/8] Loss_D: 0.6976 Loss_G: 1.8304 D(x): 0.6258 D(G(z)): 0.0829 / 0.0837\n", + "[1596/1600][0/8] Loss_D: 0.7292 Loss_G: 1.8922 D(x): 0.6499 D(G(z)): 0.0801 / 0.0792\n", + "[1596/1600][2/8] Loss_D: 0.7256 Loss_G: 1.8860 D(x): 0.5915 D(G(z)): 0.0769 / 0.0783\n", + "[1596/1600][4/8] Loss_D: 0.7905 Loss_G: 1.8133 D(x): 0.6113 D(G(z)): 0.0900 / 0.0863\n", + "[1596/1600][6/8] Loss_D: 0.7683 Loss_G: 1.9694 D(x): 0.5539 D(G(z)): 0.0672 / 0.0677\n", + "[1597/1600][0/8] Loss_D: 0.7514 Loss_G: 1.8853 D(x): 0.5622 D(G(z)): 0.0735 / 0.0774\n", + "[1597/1600][2/8] Loss_D: 0.7247 Loss_G: 1.8355 D(x): 0.6254 D(G(z)): 0.0804 / 0.0830\n", + "[1597/1600][4/8] Loss_D: 0.7905 Loss_G: 1.8167 D(x): 0.6007 D(G(z)): 0.0872 / 0.0864\n", + "[1597/1600][6/8] Loss_D: 0.7013 Loss_G: 1.8336 D(x): 0.6697 D(G(z)): 0.0868 / 0.0838\n", + "[1598/1600][0/8] Loss_D: 0.7097 Loss_G: 1.8664 D(x): 0.6022 D(G(z)): 0.0800 / 0.0809\n", + "[1598/1600][2/8] Loss_D: 0.7445 Loss_G: 1.9523 D(x): 0.6133 D(G(z)): 0.0717 / 0.0708\n", + "[1598/1600][4/8] Loss_D: 0.7091 Loss_G: 1.8646 D(x): 0.6166 D(G(z)): 0.0802 / 0.0826\n", + "[1598/1600][6/8] Loss_D: 0.7772 Loss_G: 1.8266 D(x): 0.5887 D(G(z)): 0.0854 / 0.0851\n", + "[1599/1600][0/8] Loss_D: 0.7670 Loss_G: 1.9055 D(x): 0.5802 D(G(z)): 0.0753 / 0.0752\n", + "[1599/1600][2/8] Loss_D: 0.8045 Loss_G: 1.8467 D(x): 0.5182 D(G(z)): 0.0818 / 0.0832\n", + "[1599/1600][4/8] Loss_D: 0.7586 Loss_G: 1.9297 D(x): 0.6171 D(G(z)): 0.0772 / 0.0742\n", + "[1599/1600][6/8] Loss_D: 0.7844 Loss_G: 1.9092 D(x): 0.6295 D(G(z)): 0.0792 / 0.0758\n", + "[1600/1600][0/8] Loss_D: 0.7350 Loss_G: 1.9735 D(x): 0.5794 D(G(z)): 0.0667 / 0.0675\n", + "[1600/1600][2/8] Loss_D: 0.7762 Loss_G: 1.9739 D(x): 0.5800 D(G(z)): 0.0691 / 0.0682\n", + "[1600/1600][4/8] Loss_D: 0.7328 Loss_G: 1.8260 D(x): 0.5933 D(G(z)): 0.0795 / 0.0839\n", + "[1600/1600][6/8] Loss_D: 0.7830 Loss_G: 1.8034 D(x): 0.6378 D(G(z)): 0.0889 / 0.0874\n" + ] + } + ], + "source": [ + "from tqdm import tqdm_notebook as tqdm\n", + "from time import time\n", + "import torch.nn.functional as F\n", + "from scipy.stats import truncnorm\n", + "start = time()\n", + "\n", + "G_losses = []\n", + "D_losses = []\n", + "\n", + "real_chance = []\n", + "fake_chance = []\n", + "\n", + "step = 0\n", + "for epoch in range(epochs):\n", + " for ii, (real_images) in enumerate(dataloader):\n", + " end = time()\n", + " if (end -start) > 25000 :\n", + " break\n", + " ############################\n", + " # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n", + " ###########################\n", + " # train with real\n", + " real_images = real_images[0] ############################################ CAREFUL\n", + " \n", + " \n", + " netD.zero_grad()\n", + " real_images = real_images.to(device)\n", + " batch_size = real_images.size(0)\n", + " labels = torch.full((batch_size, 1), real_label, device=device) + np.random.uniform(-0.1, 0.1)\n", + "\n", + " output = netD(real_images)\n", + " errD_real = criterion(output, labels)\n", + " errD_real.backward()\n", + " D_x = output.mean().item()\n", + "\n", + " # train with fake\n", + " noise = torch.randn(batch_size, nz, 1, 1, device=device)\n", + " fake = netG(noise)\n", + " labels.fill_(fake_label) + np.random.uniform(0, 0.2)\n", + " output = netD(fake.detach())\n", + " errD_fake = criterion(output, labels)\n", + " errD_fake.backward()\n", + " D_G_z1 = output.mean().item()\n", + " errD = errD_real + errD_fake\n", + " optimizerD.step()\n", + "\n", + " ############################\n", + " # (2) Update G network: maximize log(D(G(z)))\n", + " ###########################\n", + " netG.zero_grad()\n", + " labels.fill_(real_label) # fake labels are real for generator cost\n", + " output = netD(fake)\n", + " errG = criterion(output, labels)\n", + " errG.backward()\n", + " D_G_z2 = output.mean().item()\n", + " optimizerG.step()\n", + " \n", + " G_losses.append(errG.item())\n", + " D_losses.append(errD.item())\n", + " \n", + " real_chance.append(errD_real.item())\n", + " fake_chance.append(errD_fake.item())\n", + " \n", + " if step % 2 == 0:\n", + " print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'\n", + " % (epoch + 1, epochs, ii, len(dataloader),\n", + " errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n", + " \n", + " valid_image = netG(fixed_noise)\n", + " step += 1\n", + " lr_schedulerG.step(epoch)\n", + " lr_schedulerD.step(epoch)\n", + "\n", + " if epoch % 200 == 0:\n", + " show_generated_img()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "plt.title(\"Generator and Discriminator Loss During Training\")\n", + "plt.plot(G_losses,label=\"G\")\n", + "plt.plot(D_losses,label=\"D\")\n", + "plt.xlabel(\"iterations\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "plt.title(\"Generator and Discriminator Loss During Training\")\n", + "plt.plot(real_chance,label=\"Real\")\n", + "plt.plot(fake_chance,label=\"Fake\")\n", + "plt.xlabel(\"iterations\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/kaggle/working/images.zip'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# torch.save(netG.state_dict(), 'generator.pth')\n", + "# torch.save(netD.state_dict(), 'discriminator.pth')\n", + "\n", + "def truncated_normal(size, threshold=1):\n", + " values = truncnorm.rvs(-threshold, threshold, size=size)\n", + " return values\n", + "\n", + "if not os.path.exists('../output_images'):\n", + " os.mkdir('../output_images')\n", + "im_batch_size = 100\n", + "n_images=10000\n", + "for i_batch in range(0, n_images, im_batch_size):\n", + " z = truncated_normal((im_batch_size, 100, 1, 1), threshold=1)\n", + " gen_z = torch.from_numpy(z).float().to(device) \n", + " #gen_z = torch.randn(im_batch_size, 100, 1, 1, device=device)\n", + " gen_images = netG(gen_z)\n", + " images = gen_images.to(\"cpu\").clone().detach()\n", + " images = images.numpy().transpose(0, 2, 3, 1)\n", + " for i_image in range(gen_images.size(0)): save_image((gen_images[i_image, :, :, :] +1.0)/2.0, os.path.join('../output_images', f'image_{i_batch+i_image:05d}.png'))\n", + "\n", + "\n", + "import shutil\n", + "shutil.make_archive('images', 'zip', '../output_images')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# https://www.kaggle.com/artgor/dcgan-baseline\n", + "fig = plt.figure(figsize=(25, 16))\n", + "# display 10 images from each class\n", + "for i, j in enumerate(images[:32]):\n", + " ax = fig.add_subplot(4, 8, i + 1, xticks=[], yticks=[])\n", + " plt.imshow(j)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "Introduction to Generative Adversarial Networks (GAN).ipynb", + "provenance": [], + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}