Upload 3 files
Browse files- mnist.ipynb +139 -0
- mnist_test.ipynb +335 -0
- mnistmodel.pt +3 -0
mnist.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torchvision\n",
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"from torch import nn, optim\n",
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"from torch.autograd import Variable\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"mnist_data = torchvision.datasets.MNIST(\n",
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" \"mnist_data\", train=True, transform=torchvision.transforms.ToTensor(), download=True\n",
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")\n",
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"mnist_dataloader = torch.utils.data.DataLoader(mnist_data, batch_size=50)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Mnet(nn.Module):\n",
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" def __init__(self):\n",
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" super(Mnet, self).__init__()\n",
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" self.linear1 = nn.Linear(28 * 28, 400)\n",
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" self.linear2 = nn.Linear(400, 200)\n",
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" self.linear3 = nn.Linear(200, 100)\n",
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" self.linear4 = nn.Linear(100, 50)\n",
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" self.linear5 = nn.Linear(50, 25)\n",
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" self.final_linear = nn.Linear(25, 10)\n",
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"\n",
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" self.relu = nn.ReLU()\n",
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"\n",
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" def forward(self, images):\n",
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" x = images.view(-1, 28 * 28)\n",
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" x = self.relu(self.linear1(x))\n",
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" x = self.relu(self.linear2(x))\n",
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" x = self.relu(self.linear3(x))\n",
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" x = self.relu(self.linear4(x))\n",
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" x = self.relu(self.linear5(x))\n",
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" x = self.final_linear(x)\n",
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" return x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 50/50 [21:18<00:00, 25.57s/it]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"final loss: 1.1586851087486139e-06\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"from tqdm import tqdm\n",
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"model = Mnet()\n",
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"cec_loss = nn.CrossEntropyLoss()\n",
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"params = model.parameters()\n",
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"optimizer = optim.Adam(params=params, lr=0.001)\n",
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"\n",
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"n_epochs = 50\n",
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"n_iterations = 0\n",
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"\n",
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"for e in tqdm(range(n_epochs)):\n",
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" for i, (images, labels) in enumerate(mnist_dataloader):\n",
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" output = model(images)\n",
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"\n",
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" model.zero_grad()\n",
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" loss = cec_loss(output, labels)\n",
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" loss.backward()\n",
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"\n",
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" optimizer.step()\n",
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" n_iterations+=1\n",
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"\n",
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"print(f'final loss: {loss.item()}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"torch.save(model, \"mnistmodel.pt\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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mnist_test.ipynb
ADDED
@@ -0,0 +1,335 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch, torchvision\n",
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"from torch import nn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Mnet(nn.Module):\n",
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" def __init__(self):\n",
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" super(Mnet, self).__init__()\n",
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+
" self.linear1 = nn.Linear(28 * 28, 400)\n",
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+
" self.linear2 = nn.Linear(400, 200)\n",
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" self.linear3 = nn.Linear(200, 100)\n",
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+
" self.linear4 = nn.Linear(100, 50)\n",
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" self.linear5 = nn.Linear(50, 25)\n",
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" self.final_linear = nn.Linear(25, 10)\n",
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"\n",
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" self.relu = nn.ReLU()\n",
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"\n",
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" def forward(self, images):\n",
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" x = images.view(-1, 28 * 28)\n",
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" x = self.relu(self.linear1(x))\n",
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" x = self.relu(self.linear2(x))\n",
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" x = self.relu(self.linear3(x))\n",
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" x = self.relu(self.linear4(x))\n",
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" x = self.relu(self.linear5(x))\n",
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" x = self.final_linear(x)\n",
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" return x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = torch.load(\"mnistmodel.pt\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"T = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\n",
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"test_data = torchvision.datasets.MNIST(\"mnist_data\", train=False, transform=T, download=True)\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"#image, label = test_data[9016]\n",
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"#print(label)\n",
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"#plt.imshow(image[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"wrong answer 149\n",
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"wrong answer 151\n",
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"wrong answer 247\n",
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"wrong answer 259\n",
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"wrong answer 268\n",
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"wrong answer 340\n",
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"wrong answer 445\n",
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"wrong answer 495\n",
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"wrong answer 582\n",
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"wrong answer 684\n",
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"wrong answer 720\n",
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"wrong answer 844\n",
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"wrong answer 938\n",
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"wrong answer 947\n",
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"wrong answer 1014\n",
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"wrong answer 1039\n",
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"wrong answer 1200\n",
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"wrong answer 1226\n",
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"wrong answer 1232\n",
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"wrong answer 1242\n",
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"wrong answer 1247\n",
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"wrong answer 1260\n",
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"wrong answer 1289\n",
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"wrong answer 1319\n",
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"wrong answer 1328\n",
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"wrong answer 1393\n",
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"wrong answer 1414\n",
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"wrong answer 1425\n",
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"wrong answer 1530\n",
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"wrong answer 1553\n",
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"wrong answer 1569\n",
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"wrong answer 1681\n",
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"wrong answer 1717\n",
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"wrong answer 1751\n",
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"wrong answer 1754\n",
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"wrong answer 1790\n",
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"wrong answer 1800\n",
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"wrong answer 1878\n",
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"wrong answer 2024\n",
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"wrong answer 2109\n",
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"wrong answer 2293\n",
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"wrong answer 2369\n",
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"wrong answer 2387\n",
|
127 |
+
"wrong answer 2406\n",
|
128 |
+
"wrong answer 2414\n",
|
129 |
+
"wrong answer 2422\n",
|
130 |
+
"wrong answer 2488\n",
|
131 |
+
"wrong answer 2582\n",
|
132 |
+
"wrong answer 2597\n",
|
133 |
+
"wrong answer 2648\n",
|
134 |
+
"wrong answer 2654\n",
|
135 |
+
"wrong answer 2720\n",
|
136 |
+
"wrong answer 2863\n",
|
137 |
+
"wrong answer 2877\n",
|
138 |
+
"wrong answer 2896\n",
|
139 |
+
"wrong answer 2921\n",
|
140 |
+
"wrong answer 2927\n",
|
141 |
+
"wrong answer 2939\n",
|
142 |
+
"wrong answer 2953\n",
|
143 |
+
"wrong answer 2979\n",
|
144 |
+
"wrong answer 3060\n",
|
145 |
+
"wrong answer 3073\n",
|
146 |
+
"wrong answer 3117\n",
|
147 |
+
"wrong answer 3263\n",
|
148 |
+
"wrong answer 3284\n",
|
149 |
+
"wrong answer 3394\n",
|
150 |
+
"wrong answer 3422\n",
|
151 |
+
"wrong answer 3475\n",
|
152 |
+
"wrong answer 3503\n",
|
153 |
+
"wrong answer 3520\n",
|
154 |
+
"wrong answer 3558\n",
|
155 |
+
"wrong answer 3565\n",
|
156 |
+
"wrong answer 3567\n",
|
157 |
+
"wrong answer 3597\n",
|
158 |
+
"wrong answer 3727\n",
|
159 |
+
"wrong answer 3767\n",
|
160 |
+
"wrong answer 3776\n",
|
161 |
+
"wrong answer 3796\n",
|
162 |
+
"wrong answer 3808\n",
|
163 |
+
"wrong answer 3811\n",
|
164 |
+
"wrong answer 3817\n",
|
165 |
+
"wrong answer 3818\n",
|
166 |
+
"wrong answer 3869\n",
|
167 |
+
"wrong answer 3893\n",
|
168 |
+
"wrong answer 3906\n",
|
169 |
+
"wrong answer 3941\n",
|
170 |
+
"wrong answer 3943\n",
|
171 |
+
"wrong answer 3970\n",
|
172 |
+
"wrong answer 3985\n",
|
173 |
+
"wrong answer 4000\n",
|
174 |
+
"wrong answer 4065\n",
|
175 |
+
"wrong answer 4075\n",
|
176 |
+
"wrong answer 4140\n",
|
177 |
+
"wrong answer 4163\n",
|
178 |
+
"wrong answer 4176\n",
|
179 |
+
"wrong answer 4199\n",
|
180 |
+
"wrong answer 4224\n",
|
181 |
+
"wrong answer 4248\n",
|
182 |
+
"wrong answer 4289\n",
|
183 |
+
"wrong answer 4350\n",
|
184 |
+
"wrong answer 4369\n",
|
185 |
+
"wrong answer 4437\n",
|
186 |
+
"wrong answer 4497\n",
|
187 |
+
"wrong answer 4504\n",
|
188 |
+
"wrong answer 4536\n",
|
189 |
+
"wrong answer 4547\n",
|
190 |
+
"wrong answer 4571\n",
|
191 |
+
"wrong answer 4601\n",
|
192 |
+
"wrong answer 4731\n",
|
193 |
+
"wrong answer 4740\n",
|
194 |
+
"wrong answer 4761\n",
|
195 |
+
"wrong answer 4807\n",
|
196 |
+
"wrong answer 4823\n",
|
197 |
+
"wrong answer 4833\n",
|
198 |
+
"wrong answer 4956\n",
|
199 |
+
"wrong answer 4966\n",
|
200 |
+
"wrong answer 5078\n",
|
201 |
+
"wrong answer 5265\n",
|
202 |
+
"wrong answer 5331\n",
|
203 |
+
"wrong answer 5457\n",
|
204 |
+
"wrong answer 5586\n",
|
205 |
+
"wrong answer 5676\n",
|
206 |
+
"wrong answer 5734\n",
|
207 |
+
"wrong answer 5749\n",
|
208 |
+
"wrong answer 5887\n",
|
209 |
+
"wrong answer 5888\n",
|
210 |
+
"wrong answer 5955\n",
|
211 |
+
"wrong answer 5973\n",
|
212 |
+
"wrong answer 6011\n",
|
213 |
+
"wrong answer 6059\n",
|
214 |
+
"wrong answer 6555\n",
|
215 |
+
"wrong answer 6571\n",
|
216 |
+
"wrong answer 6597\n",
|
217 |
+
"wrong answer 6603\n",
|
218 |
+
"wrong answer 6625\n",
|
219 |
+
"wrong answer 6641\n",
|
220 |
+
"wrong answer 6651\n",
|
221 |
+
"wrong answer 6755\n",
|
222 |
+
"wrong answer 6783\n",
|
223 |
+
"wrong answer 6847\n",
|
224 |
+
"wrong answer 7434\n",
|
225 |
+
"wrong answer 7921\n",
|
226 |
+
"wrong answer 8094\n",
|
227 |
+
"wrong answer 8246\n",
|
228 |
+
"wrong answer 8311\n",
|
229 |
+
"wrong answer 8382\n",
|
230 |
+
"wrong answer 8408\n",
|
231 |
+
"wrong answer 8456\n",
|
232 |
+
"wrong answer 8522\n",
|
233 |
+
"wrong answer 8527\n",
|
234 |
+
"wrong answer 9009\n",
|
235 |
+
"wrong answer 9015\n",
|
236 |
+
"wrong answer 9024\n",
|
237 |
+
"wrong answer 9280\n",
|
238 |
+
"wrong answer 9587\n",
|
239 |
+
"wrong answer 9634\n",
|
240 |
+
"wrong answer 9664\n",
|
241 |
+
"wrong answer 9669\n",
|
242 |
+
"wrong answer 9679\n",
|
243 |
+
"wrong answer 9729\n",
|
244 |
+
"wrong answer 9745\n",
|
245 |
+
"wrong answer 9749\n",
|
246 |
+
"wrong answer 9768\n",
|
247 |
+
"wrong answer 9770\n",
|
248 |
+
"wrong answer 9792\n",
|
249 |
+
"wrong answer 9808\n",
|
250 |
+
"wrong answer 9858\n",
|
251 |
+
"9825 10000\n"
|
252 |
+
]
|
253 |
+
}
|
254 |
+
],
|
255 |
+
"source": [
|
256 |
+
"#정답률\n",
|
257 |
+
"\n",
|
258 |
+
"total_test = len(test_data)\n",
|
259 |
+
"correct_answer = 0\n",
|
260 |
+
"\n",
|
261 |
+
"for i, (image, label) in enumerate(test_data):\n",
|
262 |
+
" output = model(image)\n",
|
263 |
+
" s = nn.Softmax(dim=1)\n",
|
264 |
+
" output = s(output)\n",
|
265 |
+
" a = torch.argmax(output)\n",
|
266 |
+
" if label == a.item():\n",
|
267 |
+
" correct_answer+=1\n",
|
268 |
+
" else:\n",
|
269 |
+
" print('wrong answer', i)\n",
|
270 |
+
"\n",
|
271 |
+
"print(correct_answer, total_test)"
|
272 |
+
]
|
273 |
+
},
|
274 |
+
{
|
275 |
+
"cell_type": "code",
|
276 |
+
"execution_count": 32,
|
277 |
+
"metadata": {},
|
278 |
+
"outputs": [
|
279 |
+
{
|
280 |
+
"name": "stdout",
|
281 |
+
"output_type": "stream",
|
282 |
+
"text": [
|
283 |
+
"computer's guess: 3, answer: 3\n"
|
284 |
+
]
|
285 |
+
},
|
286 |
+
{
|
287 |
+
"data": {
|
288 |
+
"image/png": 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",
|
289 |
+
"text/plain": [
|
290 |
+
"<Figure size 640x480 with 1 Axes>"
|
291 |
+
]
|
292 |
+
},
|
293 |
+
"metadata": {},
|
294 |
+
"output_type": "display_data"
|
295 |
+
}
|
296 |
+
],
|
297 |
+
"source": [
|
298 |
+
"#틀린 1문제\n",
|
299 |
+
"\n",
|
300 |
+
"def testexam(i: int):\n",
|
301 |
+
" image, label = test_data[i]\n",
|
302 |
+
" output = model(image)\n",
|
303 |
+
" s = nn.Softmax(dim=1)\n",
|
304 |
+
" output = s(output)\n",
|
305 |
+
" a = torch.argmax(output)\n",
|
306 |
+
" print(f\"computer's guess: {a.item()}, answer: {label}\")\n",
|
307 |
+
" plt.imshow(image[0])\n",
|
308 |
+
"\n",
|
309 |
+
"\n",
|
310 |
+
"testexam(9975)"
|
311 |
+
]
|
312 |
+
}
|
313 |
+
],
|
314 |
+
"metadata": {
|
315 |
+
"kernelspec": {
|
316 |
+
"display_name": ".venv",
|
317 |
+
"language": "python",
|
318 |
+
"name": "python3"
|
319 |
+
},
|
320 |
+
"language_info": {
|
321 |
+
"codemirror_mode": {
|
322 |
+
"name": "ipython",
|
323 |
+
"version": 3
|
324 |
+
},
|
325 |
+
"file_extension": ".py",
|
326 |
+
"mimetype": "text/x-python",
|
327 |
+
"name": "python",
|
328 |
+
"nbconvert_exporter": "python",
|
329 |
+
"pygments_lexer": "ipython3",
|
330 |
+
"version": "3.10.10"
|
331 |
+
}
|
332 |
+
},
|
333 |
+
"nbformat": 4,
|
334 |
+
"nbformat_minor": 2
|
335 |
+
}
|
mnistmodel.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d66f60842989d3986d6616676cfd4f2ac19b31a60f34d150bf59bd78a8b3cee2
|
3 |
+
size 1689506
|