{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bacterial-agenda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, keras\n",
    "from PIL import Image\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, GlobalAveragePooling2D, GlobalMaxPooling2D\n",
    "\n",
    "# from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "# from tensorflow.keras.applications.mobilenet import MobileNet\n",
    "# from tensorflow.keras.applications.resnet_rs import ResNetRS420\n",
    "# from tensorflow.keras.applications.resnet_v2 import ResNet50V2\n",
    "# from tensorflow.keras.applications.vgg19 import VGG19\n",
    "# from tensorflow.keras.applications.nasnet import NASNetLarge\n",
    "# from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
    "# from tensorflow.keras.applications.xception import Xception\n",
    "# from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "# from tensorflow.keras.applications.densenet import DenseNet169\n",
    "# from tensorflow.keras.applications.efficientnet_v2 import EfficientNetV2S\n",
    "from tensorflow.keras.applications.convnext import ConvNeXtXLarge\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "unknown-cache",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Каталог с данными для обучения\n",
    "train_dir = 'DATASET-coco/train'\n",
    "# Каталог с данными для проверки\n",
    "val_dir = 'DATASET-coco/validation'\n",
    "# Каталог с данными для тестирования\n",
    "test_dir = 'DATASET-coco/test'\n",
    "# Размеры изображения\n",
    "img_width, img_height = 224, 224\n",
    "# Размер мини-выборки\n",
    "batch_size = 64\n",
    "# число классов\n",
    "num_classes = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "positive-multimedia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21e16b04ec0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_file_name = test_dir + '/1/137.jpg' # with watermark\n",
    "img = Image.open(image_file_name)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aa26be6-c03f-4a1d-9ef4-78f86e493464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21e0012d160>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_file_name = train_dir + '/1/52.jpeg' # with watermark\n",
    "img = Image.open(image_file_name)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "partial-continuity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 74040 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator()#(rescale=1. / 255)#, featurewise_center=True, featurewise_std_normalization=True)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "hidden-border",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24680 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_datagen = ImageDataGenerator()#(rescale=1. / 255)#, featurewise_center=True, featurewise_std_normalization=True)\n",
    "\n",
    "val_generator = test_datagen.flow_from_directory(\n",
    "    val_dir,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "separate-joyce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24700 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "reserved-showcase",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_maker(activation):\n",
    "    # base_model = ResNet50(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = MobileNet(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = ResNetRS420(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = ResNet50V2(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = VGG19(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = NASNetLarge(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = InceptionResNetV2(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = Xception(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = InceptionV3(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = DenseNet169(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    # base_model = EfficientNetV2S(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    base_model = ConvNeXtXLarge(include_top=False, input_shape = (img_width, img_height, 3))\n",
    "    \n",
    "    for layer in base_model.layers[:]:\n",
    "        layer.trainable = False # Freeze the layers\n",
    "        \n",
    "    input = Input(shape=(img_width, img_height, 3))\n",
    "    custom_model = base_model(input)\n",
    "    custom_model = GlobalAveragePooling2D()(custom_model) # GlobalMaxPooling2D()(custom_model)\n",
    "    custom_model = Dense(64, activation='relu')(custom_model)\n",
    "    custom_model = Dropout(0.5)(custom_model)\n",
    "    predictions = Dense(num_classes, activation=activation)(custom_model) # activation = 'sigmoid', activation = 'softmax'\n",
    "    return Model(inputs=input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "random-austin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Peter\\Desktop\\2class\\CLASS2\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py:174: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_5\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_5\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ convnext_xlarge (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)          │     <span style=\"color: #00af00; text-decoration-color: #00af00\">348,147,968</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ global_average_pooling2d             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)             │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">131,136</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">130</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_5 (\u001b[38;5;33mInputLayer\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ convnext_xlarge (\u001b[38;5;33mFunctional\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m)          │     \u001b[38;5;34m348,147,968\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ global_average_pooling2d             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)             │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │         \u001b[38;5;34m131,136\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)                   │             \u001b[38;5;34m130\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">348,279,234</span> (1.30 GB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m348,279,234\u001b[0m (1.30 GB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">131,266</span> (512.76 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m131,266\u001b[0m (512.76 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">348,147,968</span> (1.30 GB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m348,147,968\u001b[0m (1.30 GB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = model_maker('sigmoid') #softmax\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bfd77567-35ad-4efc-ab4f-4b3ecaa3a7ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import metrics\n",
    "\n",
    "#Binary cross entropy of each label. So no really a binary classification problem but\n",
    "#Calculating binary cross entropy for each label. \n",
    "# model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-3), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-3), loss='categorical_crossentropy', metrics=[metrics.mae, metrics.categorical_accuracy])\n",
    "# model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-3), loss='categorical_crossentropy', metrics=[metrics.categorical_accuracy])\n",
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "              loss='binary_crossentropy', # 'categorical_crossentropy',\n",
    "              metrics=['accuracy',\n",
    "                                                                                                             metrics.TruePositives(),\n",
    "                                                                                                             metrics.TrueNegatives(),\n",
    "                                                                                                            metrics.FalsePositives(),\n",
    "                                                                                                            metrics.FalseNegatives()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "typical-cologne",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Peter\\Desktop\\2class\\CLASS2\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m910s\u001b[0m 153s/step - accuracy: 0.5644 - false_negatives: 112.0000 - false_positives: 115.5714 - loss: 0.7607 - true_negatives: 131.2857 - true_positives: 134.8571 - val_accuracy: 0.5625 - val_false_negatives: 31.0000 - val_false_positives: 25.0000 - val_loss: 0.6914 - val_true_negatives: 39.0000 - val_true_positives: 33.0000\n",
      "Epoch 2/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m890s\u001b[0m 155s/step - accuracy: 0.4808 - false_negatives: 118.8571 - false_positives: 121.7143 - loss: 0.7679 - true_negatives: 125.1429 - true_positives: 128.0000 - val_accuracy: 0.5000 - val_false_negatives: 24.0000 - val_false_positives: 38.0000 - val_loss: 0.7042 - val_true_negatives: 26.0000 - val_true_positives: 40.0000\n",
      "Epoch 3/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m885s\u001b[0m 150s/step - accuracy: 0.5273 - false_negatives: 112.0000 - false_positives: 139.1429 - loss: 0.7327 - true_negatives: 107.7143 - true_positives: 134.8571 - val_accuracy: 0.5781 - val_false_negatives: 28.0000 - val_false_positives: 30.0000 - val_loss: 0.6696 - val_true_negatives: 34.0000 - val_true_positives: 36.0000\n",
      "Epoch 4/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m871s\u001b[0m 151s/step - accuracy: 0.5314 - false_negatives: 98.2857 - false_positives: 131.7143 - loss: 0.7047 - true_negatives: 115.1429 - true_positives: 148.5714 - val_accuracy: 0.6094 - val_false_negatives: 27.0000 - val_false_positives: 21.0000 - val_loss: 0.6624 - val_true_negatives: 43.0000 - val_true_positives: 37.0000\n",
      "Epoch 5/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m886s\u001b[0m 150s/step - accuracy: 0.6158 - false_negatives: 106.4286 - false_positives: 92.2857 - loss: 0.6685 - true_negatives: 154.5714 - true_positives: 140.4286 - val_accuracy: 0.6875 - val_false_negatives: 22.0000 - val_false_positives: 22.0000 - val_loss: 0.6282 - val_true_negatives: 42.0000 - val_true_positives: 42.0000\n",
      "Epoch 6/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m825s\u001b[0m 140s/step - accuracy: 0.5661 - false_negatives: 114.1429 - false_positives: 99.0000 - loss: 0.6894 - true_negatives: 147.8571 - true_positives: 132.7143 - val_accuracy: 0.5156 - val_false_negatives: 33.0000 - val_false_positives: 24.0000 - val_loss: 0.6910 - val_true_negatives: 40.0000 - val_true_positives: 31.0000\n",
      "Epoch 7/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m876s\u001b[0m 148s/step - accuracy: 0.6194 - false_negatives: 103.5714 - false_positives: 84.0000 - loss: 0.6562 - true_negatives: 162.8571 - true_positives: 143.2857 - val_accuracy: 0.6250 - val_false_negatives: 25.0000 - val_false_positives: 23.0000 - val_loss: 0.6430 - val_true_negatives: 41.0000 - val_true_positives: 39.0000\n",
      "Epoch 8/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m893s\u001b[0m 151s/step - accuracy: 0.6258 - false_negatives: 90.5714 - false_positives: 97.5714 - loss: 0.6628 - true_negatives: 149.2857 - true_positives: 156.2857 - val_accuracy: 0.6875 - val_false_negatives: 22.0000 - val_false_positives: 19.0000 - val_loss: 0.6343 - val_true_negatives: 45.0000 - val_true_positives: 42.0000\n",
      "Epoch 9/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m857s\u001b[0m 149s/step - accuracy: 0.6427 - false_negatives: 83.4286 - false_positives: 104.5714 - loss: 0.6633 - true_negatives: 142.2857 - true_positives: 163.4286 - val_accuracy: 0.5781 - val_false_negatives: 23.0000 - val_false_positives: 27.0000 - val_loss: 0.6185 - val_true_negatives: 37.0000 - val_true_positives: 41.0000\n",
      "Epoch 10/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m851s\u001b[0m 144s/step - accuracy: 0.6334 - false_negatives: 87.0000 - false_positives: 100.1429 - loss: 0.6509 - true_negatives: 146.7143 - true_positives: 159.8571 - val_accuracy: 0.6719 - val_false_negatives: 20.0000 - val_false_positives: 24.0000 - val_loss: 0.6363 - val_true_negatives: 40.0000 - val_true_positives: 44.0000\n",
      "Epoch 11/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m828s\u001b[0m 141s/step - accuracy: 0.6107 - false_negatives: 83.4286 - false_positives: 111.0000 - loss: 0.6612 - true_negatives: 135.8571 - true_positives: 163.4286 - val_accuracy: 0.6094 - val_false_negatives: 26.0000 - val_false_positives: 21.0000 - val_loss: 0.6198 - val_true_negatives: 43.0000 - val_true_positives: 38.0000\n",
      "Epoch 12/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m819s\u001b[0m 138s/step - accuracy: 0.6295 - false_negatives: 86.4286 - false_positives: 91.0000 - loss: 0.6336 - true_negatives: 155.8571 - true_positives: 160.4286 - val_accuracy: 0.6406 - val_false_negatives: 21.0000 - val_false_positives: 18.0000 - val_loss: 0.6055 - val_true_negatives: 46.0000 - val_true_positives: 43.0000\n",
      "Epoch 13/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m865s\u001b[0m 146s/step - accuracy: 0.6671 - false_negatives: 98.4286 - false_positives: 81.2857 - loss: 0.6378 - true_negatives: 165.5714 - true_positives: 148.4286 - val_accuracy: 0.6719 - val_false_negatives: 28.0000 - val_false_positives: 17.0000 - val_loss: 0.6148 - val_true_negatives: 47.0000 - val_true_positives: 36.0000\n",
      "Epoch 14/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m829s\u001b[0m 142s/step - accuracy: 0.6781 - false_negatives: 84.7143 - false_positives: 88.1429 - loss: 0.6200 - true_negatives: 158.7143 - true_positives: 162.1429 - val_accuracy: 0.6719 - val_false_negatives: 27.0000 - val_false_positives: 20.0000 - val_loss: 0.6313 - val_true_negatives: 44.0000 - val_true_positives: 37.0000\n",
      "Epoch 15/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m855s\u001b[0m 150s/step - accuracy: 0.6660 - false_negatives: 88.5714 - false_positives: 86.0000 - loss: 0.6282 - true_negatives: 160.8571 - true_positives: 158.2857 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 11.0000 - val_loss: 0.5302 - val_true_negatives: 53.0000 - val_true_positives: 54.0000\n",
      "Epoch 16/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m859s\u001b[0m 146s/step - accuracy: 0.6836 - false_negatives: 76.5714 - false_positives: 92.4286 - loss: 0.6167 - true_negatives: 154.4286 - true_positives: 170.2857 - val_accuracy: 0.7812 - val_false_negatives: 13.0000 - val_false_positives: 20.0000 - val_loss: 0.5700 - val_true_negatives: 44.0000 - val_true_positives: 51.0000\n",
      "Epoch 17/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m838s\u001b[0m 143s/step - accuracy: 0.6374 - false_negatives: 82.5714 - false_positives: 94.1429 - loss: 0.6229 - true_negatives: 152.7143 - true_positives: 164.2857 - val_accuracy: 0.7656 - val_false_negatives: 16.0000 - val_false_positives: 15.0000 - val_loss: 0.5634 - val_true_negatives: 49.0000 - val_true_positives: 48.0000\n",
      "Epoch 18/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m861s\u001b[0m 147s/step - accuracy: 0.6851 - false_negatives: 81.4286 - false_positives: 76.7143 - loss: 0.6094 - true_negatives: 170.1429 - true_positives: 165.4286 - val_accuracy: 0.7344 - val_false_negatives: 17.0000 - val_false_positives: 15.0000 - val_loss: 0.5534 - val_true_negatives: 49.0000 - val_true_positives: 47.0000\n",
      "Epoch 19/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m852s\u001b[0m 147s/step - accuracy: 0.6799 - false_negatives: 79.8571 - false_positives: 84.8571 - loss: 0.6074 - true_negatives: 162.0000 - true_positives: 167.0000 - val_accuracy: 0.7656 - val_false_negatives: 16.0000 - val_false_positives: 15.0000 - val_loss: 0.5716 - val_true_negatives: 49.0000 - val_true_positives: 48.0000\n",
      "Epoch 20/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m818s\u001b[0m 139s/step - accuracy: 0.6465 - false_negatives: 70.4286 - false_positives: 87.1429 - loss: 0.6098 - true_negatives: 159.7143 - true_positives: 176.4286 - val_accuracy: 0.6406 - val_false_negatives: 17.0000 - val_false_positives: 24.0000 - val_loss: 0.6022 - val_true_negatives: 40.0000 - val_true_positives: 47.0000\n",
      "Epoch 21/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m844s\u001b[0m 144s/step - accuracy: 0.7310 - false_negatives: 64.2857 - false_positives: 84.0000 - loss: 0.5978 - true_negatives: 162.8571 - true_positives: 182.5714 - val_accuracy: 0.7344 - val_false_negatives: 16.0000 - val_false_positives: 18.0000 - val_loss: 0.5675 - val_true_negatives: 46.0000 - val_true_positives: 48.0000\n",
      "Epoch 22/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m890s\u001b[0m 155s/step - accuracy: 0.6736 - false_negatives: 69.1429 - false_positives: 85.2857 - loss: 0.5931 - true_negatives: 161.5714 - true_positives: 177.7143 - val_accuracy: 0.8125 - val_false_negatives: 11.0000 - val_false_positives: 14.0000 - val_loss: 0.5041 - val_true_negatives: 50.0000 - val_true_positives: 53.0000\n",
      "Epoch 23/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m862s\u001b[0m 151s/step - accuracy: 0.7124 - false_negatives: 66.0000 - false_positives: 77.2857 - loss: 0.5546 - true_negatives: 169.5714 - true_positives: 180.8571 - val_accuracy: 0.7812 - val_false_negatives: 15.0000 - val_false_positives: 14.0000 - val_loss: 0.4993 - val_true_negatives: 50.0000 - val_true_positives: 49.0000\n",
      "Epoch 24/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m835s\u001b[0m 146s/step - accuracy: 0.7662 - false_negatives: 61.1429 - false_positives: 60.8571 - loss: 0.5334 - true_negatives: 186.0000 - true_positives: 185.7143 - val_accuracy: 0.7969 - val_false_negatives: 10.0000 - val_false_positives: 13.0000 - val_loss: 0.5049 - val_true_negatives: 51.0000 - val_true_positives: 54.0000\n",
      "Epoch 25/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m877s\u001b[0m 152s/step - accuracy: 0.7141 - false_negatives: 68.5714 - false_positives: 72.5714 - loss: 0.5663 - true_negatives: 174.2857 - true_positives: 178.2857 - val_accuracy: 0.7188 - val_false_negatives: 20.0000 - val_false_positives: 19.0000 - val_loss: 0.5253 - val_true_negatives: 45.0000 - val_true_positives: 44.0000\n",
      "Epoch 26/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m843s\u001b[0m 146s/step - accuracy: 0.7574 - false_negatives: 66.2857 - false_positives: 64.0000 - loss: 0.5138 - true_negatives: 182.8571 - true_positives: 180.5714 - val_accuracy: 0.6875 - val_false_negatives: 20.0000 - val_false_positives: 20.0000 - val_loss: 0.5492 - val_true_negatives: 44.0000 - val_true_positives: 44.0000\n",
      "Epoch 27/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m830s\u001b[0m 143s/step - accuracy: 0.7201 - false_negatives: 68.0000 - false_positives: 75.2857 - loss: 0.5648 - true_negatives: 171.5714 - true_positives: 178.8571 - val_accuracy: 0.7500 - val_false_negatives: 16.0000 - val_false_positives: 17.0000 - val_loss: 0.4819 - val_true_negatives: 47.0000 - val_true_positives: 48.0000\n",
      "Epoch 28/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m855s\u001b[0m 147s/step - accuracy: 0.7519 - false_negatives: 63.7143 - false_positives: 65.0000 - loss: 0.5228 - true_negatives: 181.8571 - true_positives: 183.1429 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 13.0000 - val_loss: 0.4144 - val_true_negatives: 51.0000 - val_true_positives: 51.0000\n",
      "Epoch 29/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m808s\u001b[0m 136s/step - accuracy: 0.7240 - false_negatives: 69.8571 - false_positives: 73.0000 - loss: 0.5430 - true_negatives: 173.8571 - true_positives: 177.0000 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 13.0000 - val_loss: 0.4806 - val_true_negatives: 51.0000 - val_true_positives: 53.0000\n",
      "Epoch 30/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m863s\u001b[0m 147s/step - accuracy: 0.7194 - false_negatives: 67.2857 - false_positives: 66.8571 - loss: 0.5550 - true_negatives: 180.0000 - true_positives: 179.5714 - val_accuracy: 0.7500 - val_false_negatives: 16.0000 - val_false_positives: 17.0000 - val_loss: 0.5234 - val_true_negatives: 47.0000 - val_true_positives: 48.0000\n",
      "Epoch 31/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m846s\u001b[0m 144s/step - accuracy: 0.7134 - false_negatives: 66.1429 - false_positives: 79.5714 - loss: 0.5951 - true_negatives: 167.2857 - true_positives: 180.7143 - val_accuracy: 0.8125 - val_false_negatives: 12.0000 - val_false_positives: 13.0000 - val_loss: 0.4219 - val_true_negatives: 51.0000 - val_true_positives: 52.0000\n",
      "Epoch 32/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m820s\u001b[0m 140s/step - accuracy: 0.7463 - false_negatives: 61.1429 - false_positives: 65.8571 - loss: 0.5261 - true_negatives: 181.0000 - true_positives: 185.7143 - val_accuracy: 0.7188 - val_false_negatives: 15.0000 - val_false_positives: 19.0000 - val_loss: 0.5265 - val_true_negatives: 45.0000 - val_true_positives: 49.0000\n",
      "Epoch 33/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m873s\u001b[0m 153s/step - accuracy: 0.7465 - false_negatives: 69.1429 - false_positives: 64.5714 - loss: 0.4980 - true_negatives: 182.2857 - true_positives: 177.7143 - val_accuracy: 0.6406 - val_false_negatives: 21.0000 - val_false_positives: 23.0000 - val_loss: 0.5529 - val_true_negatives: 41.0000 - val_true_positives: 43.0000\n",
      "Epoch 34/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m824s\u001b[0m 141s/step - accuracy: 0.7595 - false_negatives: 64.2857 - false_positives: 61.1429 - loss: 0.5139 - true_negatives: 185.7143 - true_positives: 182.5714 - val_accuracy: 0.7969 - val_false_negatives: 14.0000 - val_false_positives: 12.0000 - val_loss: 0.4825 - val_true_negatives: 52.0000 - val_true_positives: 50.0000\n",
      "Epoch 35/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m854s\u001b[0m 145s/step - accuracy: 0.7539 - false_negatives: 62.8571 - false_positives: 56.0000 - loss: 0.5529 - true_negatives: 190.8571 - true_positives: 184.0000 - val_accuracy: 0.7031 - val_false_negatives: 20.0000 - val_false_positives: 16.0000 - val_loss: 0.6190 - val_true_negatives: 48.0000 - val_true_positives: 44.0000\n",
      "Epoch 36/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m838s\u001b[0m 145s/step - accuracy: 0.7339 - false_negatives: 65.0000 - false_positives: 70.5714 - loss: 0.5383 - true_negatives: 176.2857 - true_positives: 181.8571 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 12.0000 - val_loss: 0.4496 - val_true_negatives: 52.0000 - val_true_positives: 54.0000\n",
      "Epoch 37/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m848s\u001b[0m 148s/step - accuracy: 0.7365 - false_negatives: 60.4286 - false_positives: 64.0000 - loss: 0.5168 - true_negatives: 182.8571 - true_positives: 186.4286 - val_accuracy: 0.7656 - val_false_negatives: 16.0000 - val_false_positives: 15.0000 - val_loss: 0.4970 - val_true_negatives: 49.0000 - val_true_positives: 48.0000\n",
      "Epoch 38/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m832s\u001b[0m 141s/step - accuracy: 0.7690 - false_negatives: 61.2857 - false_positives: 58.7143 - loss: 0.4869 - true_negatives: 188.1429 - true_positives: 185.5714 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 14.0000 - val_loss: 0.4803 - val_true_negatives: 50.0000 - val_true_positives: 51.0000\n",
      "Epoch 39/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m820s\u001b[0m 142s/step - accuracy: 0.7786 - false_negatives: 51.7143 - false_positives: 55.5714 - loss: 0.4778 - true_negatives: 191.2857 - true_positives: 195.1429 - val_accuracy: 0.7656 - val_false_negatives: 15.0000 - val_false_positives: 16.0000 - val_loss: 0.4688 - val_true_negatives: 48.0000 - val_true_positives: 49.0000\n",
      "Epoch 40/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m836s\u001b[0m 143s/step - accuracy: 0.8180 - false_negatives: 42.0000 - false_positives: 51.5714 - loss: 0.4266 - true_negatives: 195.2857 - true_positives: 204.8571 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 14.0000 - val_loss: 0.4467 - val_true_negatives: 50.0000 - val_true_positives: 51.0000\n",
      "Epoch 41/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m836s\u001b[0m 141s/step - accuracy: 0.7624 - false_negatives: 56.5714 - false_positives: 59.5714 - loss: 0.4920 - true_negatives: 187.2857 - true_positives: 190.2857 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 8.0000 - val_loss: 0.3823 - val_true_negatives: 56.0000 - val_true_positives: 53.0000\n",
      "Epoch 42/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m814s\u001b[0m 139s/step - accuracy: 0.7590 - false_negatives: 59.0000 - false_positives: 57.2857 - loss: 0.4776 - true_negatives: 189.5714 - true_positives: 187.8571 - val_accuracy: 0.7500 - val_false_negatives: 17.0000 - val_false_positives: 14.0000 - val_loss: 0.4841 - val_true_negatives: 50.0000 - val_true_positives: 47.0000\n",
      "Epoch 43/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m822s\u001b[0m 142s/step - accuracy: 0.7421 - false_negatives: 68.7143 - false_positives: 57.0000 - loss: 0.5104 - true_negatives: 189.8571 - true_positives: 178.1429 - val_accuracy: 0.8281 - val_false_negatives: 12.0000 - val_false_positives: 10.0000 - val_loss: 0.4549 - val_true_negatives: 54.0000 - val_true_positives: 52.0000\n",
      "Epoch 44/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m830s\u001b[0m 143s/step - accuracy: 0.7542 - false_negatives: 57.4286 - false_positives: 57.1429 - loss: 0.5282 - true_negatives: 189.7143 - true_positives: 189.4286 - val_accuracy: 0.7969 - val_false_negatives: 14.0000 - val_false_positives: 13.0000 - val_loss: 0.5434 - val_true_negatives: 51.0000 - val_true_positives: 50.0000\n",
      "Epoch 45/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m820s\u001b[0m 140s/step - accuracy: 0.8079 - false_negatives: 59.8571 - false_positives: 50.8571 - loss: 0.4307 - true_negatives: 196.0000 - true_positives: 187.0000 - val_accuracy: 0.7188 - val_false_negatives: 18.0000 - val_false_positives: 16.0000 - val_loss: 0.4981 - val_true_negatives: 48.0000 - val_true_positives: 46.0000\n",
      "Epoch 46/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m827s\u001b[0m 141s/step - accuracy: 0.7635 - false_negatives: 51.2857 - false_positives: 58.1429 - loss: 0.4873 - true_negatives: 188.7143 - true_positives: 195.5714 - val_accuracy: 0.8750 - val_false_negatives: 7.0000 - val_false_positives: 10.0000 - val_loss: 0.3290 - val_true_negatives: 54.0000 - val_true_positives: 57.0000\n",
      "Epoch 47/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m815s\u001b[0m 140s/step - accuracy: 0.7983 - false_negatives: 53.1429 - false_positives: 54.1429 - loss: 0.4828 - true_negatives: 192.7143 - true_positives: 193.7143 - val_accuracy: 0.7344 - val_false_negatives: 16.0000 - val_false_positives: 18.0000 - val_loss: 0.5289 - val_true_negatives: 46.0000 - val_true_positives: 48.0000\n",
      "Epoch 48/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m822s\u001b[0m 143s/step - accuracy: 0.7637 - false_negatives: 49.4286 - false_positives: 62.8571 - loss: 0.4548 - true_negatives: 184.0000 - true_positives: 197.4286 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.4426 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 49/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m825s\u001b[0m 142s/step - accuracy: 0.7983 - false_negatives: 46.5714 - false_positives: 53.2857 - loss: 0.4279 - true_negatives: 193.5714 - true_positives: 200.2857 - val_accuracy: 0.7812 - val_false_negatives: 14.0000 - val_false_positives: 15.0000 - val_loss: 0.4948 - val_true_negatives: 49.0000 - val_true_positives: 50.0000\n",
      "Epoch 50/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m828s\u001b[0m 145s/step - accuracy: 0.8121 - false_negatives: 50.5714 - false_positives: 49.5714 - loss: 0.4337 - true_negatives: 197.2857 - true_positives: 196.2857 - val_accuracy: 0.7969 - val_false_negatives: 12.0000 - val_false_positives: 14.0000 - val_loss: 0.4301 - val_true_negatives: 50.0000 - val_true_positives: 52.0000\n",
      "Epoch 51/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m805s\u001b[0m 138s/step - accuracy: 0.7515 - false_negatives: 56.7143 - false_positives: 62.5714 - loss: 0.4811 - true_negatives: 184.2857 - true_positives: 190.1429 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3662 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 52/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m797s\u001b[0m 136s/step - accuracy: 0.7615 - false_negatives: 54.0000 - false_positives: 56.7143 - loss: 0.4631 - true_negatives: 190.1429 - true_positives: 192.8571 - val_accuracy: 0.7344 - val_false_negatives: 16.0000 - val_false_positives: 17.0000 - val_loss: 0.4915 - val_true_negatives: 47.0000 - val_true_positives: 48.0000\n",
      "Epoch 53/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m788s\u001b[0m 135s/step - accuracy: 0.8045 - false_negatives: 48.5714 - false_positives: 44.8571 - loss: 0.4253 - true_negatives: 202.0000 - true_positives: 198.2857 - val_accuracy: 0.8438 - val_false_negatives: 9.0000 - val_false_positives: 10.0000 - val_loss: 0.4157 - val_true_negatives: 54.0000 - val_true_positives: 55.0000\n",
      "Epoch 54/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m789s\u001b[0m 137s/step - accuracy: 0.7512 - false_negatives: 59.8571 - false_positives: 60.0000 - loss: 0.4845 - true_negatives: 186.8571 - true_positives: 187.0000 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3818 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 55/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m785s\u001b[0m 135s/step - accuracy: 0.7946 - false_negatives: 48.0000 - false_positives: 54.1429 - loss: 0.4475 - true_negatives: 192.7143 - true_positives: 198.8571 - val_accuracy: 0.7656 - val_false_negatives: 14.0000 - val_false_positives: 15.0000 - val_loss: 0.4391 - val_true_negatives: 49.0000 - val_true_positives: 50.0000\n",
      "Epoch 56/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m857s\u001b[0m 146s/step - accuracy: 0.8207 - false_negatives: 47.2857 - false_positives: 46.4286 - loss: 0.4145 - true_negatives: 200.4286 - true_positives: 199.5714 - val_accuracy: 0.8438 - val_false_negatives: 9.0000 - val_false_positives: 13.0000 - val_loss: 0.3855 - val_true_negatives: 51.0000 - val_true_positives: 55.0000\n",
      "Epoch 57/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m786s\u001b[0m 135s/step - accuracy: 0.7716 - false_negatives: 52.7143 - false_positives: 58.7143 - loss: 0.4837 - true_negatives: 188.1429 - true_positives: 194.1429 - val_accuracy: 0.8125 - val_false_negatives: 15.0000 - val_false_positives: 12.0000 - val_loss: 0.4524 - val_true_negatives: 52.0000 - val_true_positives: 49.0000\n",
      "Epoch 58/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m753s\u001b[0m 130s/step - accuracy: 0.7988 - false_negatives: 47.2857 - false_positives: 50.4286 - loss: 0.4806 - true_negatives: 196.4286 - true_positives: 199.5714 - val_accuracy: 0.7812 - val_false_negatives: 13.0000 - val_false_positives: 14.0000 - val_loss: 0.4126 - val_true_negatives: 50.0000 - val_true_positives: 51.0000\n",
      "Epoch 59/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m802s\u001b[0m 137s/step - accuracy: 0.7802 - false_negatives: 56.8571 - false_positives: 56.2857 - loss: 0.4697 - true_negatives: 190.5714 - true_positives: 190.0000 - val_accuracy: 0.8281 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3641 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 60/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m855s\u001b[0m 146s/step - accuracy: 0.7325 - false_negatives: 64.8571 - false_positives: 62.1429 - loss: 0.5355 - true_negatives: 184.7143 - true_positives: 182.0000 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3615 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 61/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m816s\u001b[0m 141s/step - accuracy: 0.8147 - false_negatives: 48.0000 - false_positives: 46.0000 - loss: 0.4180 - true_negatives: 200.8571 - true_positives: 198.8571 - val_accuracy: 0.7969 - val_false_negatives: 16.0000 - val_false_positives: 13.0000 - val_loss: 0.4499 - val_true_negatives: 51.0000 - val_true_positives: 48.0000\n",
      "Epoch 62/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m806s\u001b[0m 141s/step - accuracy: 0.8429 - false_negatives: 44.0000 - false_positives: 42.0000 - loss: 0.3889 - true_negatives: 204.8571 - true_positives: 202.8571 - val_accuracy: 0.7969 - val_false_negatives: 14.0000 - val_false_positives: 12.0000 - val_loss: 0.4411 - val_true_negatives: 52.0000 - val_true_positives: 50.0000\n",
      "Epoch 63/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m783s\u001b[0m 136s/step - accuracy: 0.7576 - false_negatives: 58.5714 - false_positives: 60.0000 - loss: 0.4804 - true_negatives: 186.8571 - true_positives: 188.2857 - val_accuracy: 0.8750 - val_false_negatives: 10.0000 - val_false_positives: 8.0000 - val_loss: 0.3480 - val_true_negatives: 56.0000 - val_true_positives: 54.0000\n",
      "Epoch 64/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m868s\u001b[0m 148s/step - accuracy: 0.7888 - false_negatives: 55.7143 - false_positives: 46.8571 - loss: 0.4749 - true_negatives: 200.0000 - true_positives: 191.1429 - val_accuracy: 0.8438 - val_false_negatives: 9.0000 - val_false_positives: 10.0000 - val_loss: 0.3794 - val_true_negatives: 54.0000 - val_true_positives: 55.0000\n",
      "Epoch 65/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m829s\u001b[0m 142s/step - accuracy: 0.7793 - false_negatives: 54.7143 - false_positives: 51.1429 - loss: 0.4624 - true_negatives: 195.7143 - true_positives: 192.1429 - val_accuracy: 0.9062 - val_false_negatives: 7.0000 - val_false_positives: 6.0000 - val_loss: 0.3548 - val_true_negatives: 58.0000 - val_true_positives: 57.0000\n",
      "Epoch 66/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m850s\u001b[0m 145s/step - accuracy: 0.8245 - false_negatives: 42.8571 - false_positives: 46.4286 - loss: 0.4039 - true_negatives: 200.4286 - true_positives: 204.0000 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3461 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 67/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m820s\u001b[0m 142s/step - accuracy: 0.7998 - false_negatives: 52.0000 - false_positives: 48.1429 - loss: 0.4214 - true_negatives: 198.7143 - true_positives: 194.8571 - val_accuracy: 0.7812 - val_false_negatives: 14.0000 - val_false_positives: 16.0000 - val_loss: 0.4406 - val_true_negatives: 48.0000 - val_true_positives: 50.0000\n",
      "Epoch 68/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m837s\u001b[0m 144s/step - accuracy: 0.7792 - false_negatives: 51.4286 - false_positives: 52.8571 - loss: 0.4257 - true_negatives: 194.0000 - true_positives: 195.4286 - val_accuracy: 0.8594 - val_false_negatives: 11.0000 - val_false_positives: 8.0000 - val_loss: 0.3820 - val_true_negatives: 56.0000 - val_true_positives: 53.0000\n",
      "Epoch 69/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m840s\u001b[0m 142s/step - accuracy: 0.7871 - false_negatives: 51.8571 - false_positives: 50.1429 - loss: 0.4454 - true_negatives: 196.7143 - true_positives: 195.0000 - val_accuracy: 0.7344 - val_false_negatives: 17.0000 - val_false_positives: 17.0000 - val_loss: 0.5027 - val_true_negatives: 47.0000 - val_true_positives: 47.0000\n",
      "Epoch 70/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m824s\u001b[0m 139s/step - accuracy: 0.8291 - false_negatives: 43.0000 - false_positives: 42.1429 - loss: 0.4483 - true_negatives: 204.7143 - true_positives: 203.8571 - val_accuracy: 0.8594 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3342 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 71/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m836s\u001b[0m 146s/step - accuracy: 0.8159 - false_negatives: 42.0000 - false_positives: 42.5714 - loss: 0.4206 - true_negatives: 204.2857 - true_positives: 204.8571 - val_accuracy: 0.8906 - val_false_negatives: 7.0000 - val_false_positives: 7.0000 - val_loss: 0.3739 - val_true_negatives: 57.0000 - val_true_positives: 57.0000\n",
      "Epoch 72/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m814s\u001b[0m 137s/step - accuracy: 0.8047 - false_negatives: 51.1429 - false_positives: 46.0000 - loss: 0.4184 - true_negatives: 200.8571 - true_positives: 195.7143 - val_accuracy: 0.8125 - val_false_negatives: 11.0000 - val_false_positives: 12.0000 - val_loss: 0.3718 - val_true_negatives: 52.0000 - val_true_positives: 53.0000\n",
      "Epoch 73/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m848s\u001b[0m 143s/step - accuracy: 0.7705 - false_negatives: 53.4286 - false_positives: 55.0000 - loss: 0.4359 - true_negatives: 191.8571 - true_positives: 193.4286 - val_accuracy: 0.9219 - val_false_negatives: 5.0000 - val_false_positives: 5.0000 - val_loss: 0.3029 - val_true_negatives: 59.0000 - val_true_positives: 59.0000\n",
      "Epoch 74/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m808s\u001b[0m 139s/step - accuracy: 0.7925 - false_negatives: 51.7143 - false_positives: 52.0000 - loss: 0.4654 - true_negatives: 194.8571 - true_positives: 195.1429 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.3907 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 75/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m863s\u001b[0m 148s/step - accuracy: 0.8169 - false_negatives: 45.0000 - false_positives: 47.8571 - loss: 0.4293 - true_negatives: 199.0000 - true_positives: 201.8571 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.3514 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 76/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m854s\u001b[0m 148s/step - accuracy: 0.8164 - false_negatives: 43.1429 - false_positives: 45.0000 - loss: 0.3912 - true_negatives: 201.8571 - true_positives: 203.7143 - val_accuracy: 0.7812 - val_false_negatives: 15.0000 - val_false_positives: 13.0000 - val_loss: 0.4619 - val_true_negatives: 51.0000 - val_true_positives: 49.0000\n",
      "Epoch 77/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m846s\u001b[0m 145s/step - accuracy: 0.8235 - false_negatives: 46.5714 - false_positives: 45.5714 - loss: 0.4066 - true_negatives: 201.2857 - true_positives: 200.2857 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.4099 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 78/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m887s\u001b[0m 153s/step - accuracy: 0.7659 - false_negatives: 54.5714 - false_positives: 58.8571 - loss: 0.4765 - true_negatives: 188.0000 - true_positives: 192.2857 - val_accuracy: 0.7812 - val_false_negatives: 14.0000 - val_false_positives: 14.0000 - val_loss: 0.4650 - val_true_negatives: 50.0000 - val_true_positives: 50.0000\n",
      "Epoch 79/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m861s\u001b[0m 149s/step - accuracy: 0.7762 - false_negatives: 56.7143 - false_positives: 53.1429 - loss: 0.4736 - true_negatives: 193.7143 - true_positives: 190.1429 - val_accuracy: 0.7031 - val_false_negatives: 20.0000 - val_false_positives: 19.0000 - val_loss: 0.5123 - val_true_negatives: 45.0000 - val_true_positives: 44.0000\n",
      "Epoch 80/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m851s\u001b[0m 146s/step - accuracy: 0.7663 - false_negatives: 58.5714 - false_positives: 56.2857 - loss: 0.4375 - true_negatives: 190.5714 - true_positives: 188.2857 - val_accuracy: 0.7031 - val_false_negatives: 19.0000 - val_false_positives: 19.0000 - val_loss: 0.4904 - val_true_negatives: 45.0000 - val_true_positives: 45.0000\n",
      "Epoch 81/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m862s\u001b[0m 147s/step - accuracy: 0.8003 - false_negatives: 50.4286 - false_positives: 49.8571 - loss: 0.4446 - true_negatives: 197.0000 - true_positives: 196.4286 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 8.0000 - val_loss: 0.3818 - val_true_negatives: 56.0000 - val_true_positives: 55.0000\n",
      "Epoch 82/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m832s\u001b[0m 143s/step - accuracy: 0.7963 - false_negatives: 52.2857 - false_positives: 50.1429 - loss: 0.4301 - true_negatives: 196.7143 - true_positives: 194.5714 - val_accuracy: 0.8438 - val_false_negatives: 9.0000 - val_false_positives: 10.0000 - val_loss: 0.3393 - val_true_negatives: 54.0000 - val_true_positives: 55.0000\n",
      "Epoch 83/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m861s\u001b[0m 145s/step - accuracy: 0.8004 - false_negatives: 48.4286 - false_positives: 48.8571 - loss: 0.4207 - true_negatives: 198.0000 - true_positives: 198.4286 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 11.0000 - val_loss: 0.3232 - val_true_negatives: 53.0000 - val_true_positives: 54.0000\n",
      "Epoch 84/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m872s\u001b[0m 149s/step - accuracy: 0.7871 - false_negatives: 52.8571 - false_positives: 54.5714 - loss: 0.4553 - true_negatives: 192.2857 - true_positives: 194.0000 - val_accuracy: 0.7812 - val_false_negatives: 14.0000 - val_false_positives: 15.0000 - val_loss: 0.4160 - val_true_negatives: 49.0000 - val_true_positives: 50.0000\n",
      "Epoch 85/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m807s\u001b[0m 141s/step - accuracy: 0.8241 - false_negatives: 44.2857 - false_positives: 45.7143 - loss: 0.4050 - true_negatives: 201.1429 - true_positives: 202.5714 - val_accuracy: 0.7812 - val_false_negatives: 15.0000 - val_false_positives: 13.0000 - val_loss: 0.4337 - val_true_negatives: 51.0000 - val_true_positives: 49.0000\n",
      "Epoch 86/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m801s\u001b[0m 138s/step - accuracy: 0.8038 - false_negatives: 45.1429 - false_positives: 48.7143 - loss: 0.4441 - true_negatives: 198.1429 - true_positives: 201.7143 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 8.0000 - val_loss: 0.3143 - val_true_negatives: 56.0000 - val_true_positives: 55.0000\n",
      "Epoch 87/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m807s\u001b[0m 138s/step - accuracy: 0.8130 - false_negatives: 47.5714 - false_positives: 42.8571 - loss: 0.4107 - true_negatives: 204.0000 - true_positives: 199.2857 - val_accuracy: 0.7344 - val_false_negatives: 17.0000 - val_false_positives: 17.0000 - val_loss: 0.5694 - val_true_negatives: 47.0000 - val_true_positives: 47.0000\n",
      "Epoch 88/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m829s\u001b[0m 143s/step - accuracy: 0.7906 - false_negatives: 50.7143 - false_positives: 52.0000 - loss: 0.4137 - true_negatives: 194.8571 - true_positives: 196.1429 - val_accuracy: 0.8281 - val_false_negatives: 12.0000 - val_false_positives: 10.0000 - val_loss: 0.3955 - val_true_negatives: 54.0000 - val_true_positives: 52.0000\n",
      "Epoch 89/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m828s\u001b[0m 143s/step - accuracy: 0.7983 - false_negatives: 50.4286 - false_positives: 50.4286 - loss: 0.4441 - true_negatives: 196.4286 - true_positives: 196.4286 - val_accuracy: 0.8281 - val_false_negatives: 12.0000 - val_false_positives: 12.0000 - val_loss: 0.4046 - val_true_negatives: 52.0000 - val_true_positives: 52.0000\n",
      "Epoch 90/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m826s\u001b[0m 142s/step - accuracy: 0.8307 - false_negatives: 47.0000 - false_positives: 42.0000 - loss: 0.3867 - true_negatives: 204.8571 - true_positives: 199.8571 - val_accuracy: 0.8438 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3951 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 91/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m816s\u001b[0m 142s/step - accuracy: 0.8281 - false_negatives: 46.0000 - false_positives: 44.0000 - loss: 0.3871 - true_negatives: 202.8571 - true_positives: 200.8571 - val_accuracy: 0.7188 - val_false_negatives: 17.0000 - val_false_positives: 18.0000 - val_loss: 0.4680 - val_true_negatives: 46.0000 - val_true_positives: 47.0000\n",
      "Epoch 92/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m812s\u001b[0m 142s/step - accuracy: 0.8268 - false_negatives: 42.1429 - false_positives: 41.7143 - loss: 0.4280 - true_negatives: 205.1429 - true_positives: 204.7143 - val_accuracy: 0.7344 - val_false_negatives: 17.0000 - val_false_positives: 18.0000 - val_loss: 0.4962 - val_true_negatives: 46.0000 - val_true_positives: 47.0000\n",
      "Epoch 93/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m785s\u001b[0m 136s/step - accuracy: 0.8478 - false_negatives: 38.7143 - false_positives: 38.8571 - loss: 0.3885 - true_negatives: 208.0000 - true_positives: 208.1429 - val_accuracy: 0.8125 - val_false_negatives: 12.0000 - val_false_positives: 12.0000 - val_loss: 0.3465 - val_true_negatives: 52.0000 - val_true_positives: 52.0000\n",
      "Epoch 94/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m878s\u001b[0m 149s/step - accuracy: 0.8079 - false_negatives: 47.2857 - false_positives: 49.0000 - loss: 0.4198 - true_negatives: 197.8571 - true_positives: 199.5714 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 10.0000 - val_loss: 0.4038 - val_true_negatives: 54.0000 - val_true_positives: 53.0000\n",
      "Epoch 95/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m841s\u001b[0m 144s/step - accuracy: 0.8115 - false_negatives: 48.1429 - false_positives: 48.0000 - loss: 0.4164 - true_negatives: 198.8571 - true_positives: 198.7143 - val_accuracy: 0.7969 - val_false_negatives: 12.0000 - val_false_positives: 13.0000 - val_loss: 0.4315 - val_true_negatives: 51.0000 - val_true_positives: 52.0000\n",
      "Epoch 96/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m848s\u001b[0m 144s/step - accuracy: 0.8070 - false_negatives: 49.7143 - false_positives: 47.0000 - loss: 0.4089 - true_negatives: 199.8571 - true_positives: 197.1429 - val_accuracy: 0.9062 - val_false_negatives: 6.0000 - val_false_positives: 5.0000 - val_loss: 0.2686 - val_true_negatives: 59.0000 - val_true_positives: 58.0000\n",
      "Epoch 97/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m847s\u001b[0m 144s/step - accuracy: 0.8083 - false_negatives: 46.7143 - false_positives: 46.2857 - loss: 0.4117 - true_negatives: 200.5714 - true_positives: 200.1429 - val_accuracy: 0.7031 - val_false_negatives: 20.0000 - val_false_positives: 19.0000 - val_loss: 0.4887 - val_true_negatives: 45.0000 - val_true_positives: 44.0000\n",
      "Epoch 98/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m858s\u001b[0m 148s/step - accuracy: 0.8387 - false_negatives: 40.7143 - false_positives: 38.1429 - loss: 0.3541 - true_negatives: 208.7143 - true_positives: 206.1429 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.4360 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 99/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m843s\u001b[0m 144s/step - accuracy: 0.7878 - false_negatives: 50.1429 - false_positives: 52.1429 - loss: 0.4416 - true_negatives: 194.7143 - true_positives: 196.7143 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 13.0000 - val_loss: 0.3603 - val_true_negatives: 51.0000 - val_true_positives: 51.0000\n",
      "Epoch 100/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m899s\u001b[0m 155s/step - accuracy: 0.8060 - false_negatives: 46.7143 - false_positives: 48.5714 - loss: 0.4158 - true_negatives: 198.2857 - true_positives: 200.1429 - val_accuracy: 0.7500 - val_false_negatives: 16.0000 - val_false_positives: 16.0000 - val_loss: 0.4652 - val_true_negatives: 48.0000 - val_true_positives: 48.0000\n",
      "Epoch 101/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m880s\u001b[0m 150s/step - accuracy: 0.8148 - false_negatives: 49.4286 - false_positives: 46.7143 - loss: 0.4293 - true_negatives: 200.1429 - true_positives: 197.4286 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 13.0000 - val_loss: 0.3867 - val_true_negatives: 51.0000 - val_true_positives: 53.0000\n",
      "Epoch 102/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m852s\u001b[0m 148s/step - accuracy: 0.8260 - false_negatives: 45.5714 - false_positives: 42.2857 - loss: 0.3910 - true_negatives: 204.5714 - true_positives: 201.2857 - val_accuracy: 0.8125 - val_false_negatives: 12.0000 - val_false_positives: 11.0000 - val_loss: 0.3973 - val_true_negatives: 53.0000 - val_true_positives: 52.0000\n",
      "Epoch 103/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m849s\u001b[0m 144s/step - accuracy: 0.8353 - false_negatives: 38.5714 - false_positives: 38.7143 - loss: 0.3840 - true_negatives: 208.1429 - true_positives: 208.2857 - val_accuracy: 0.7500 - val_false_negatives: 16.0000 - val_false_positives: 14.0000 - val_loss: 0.4502 - val_true_negatives: 50.0000 - val_true_positives: 48.0000\n",
      "Epoch 104/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m870s\u001b[0m 151s/step - accuracy: 0.8174 - false_negatives: 42.0000 - false_positives: 47.5714 - loss: 0.4157 - true_negatives: 199.2857 - true_positives: 204.8571 - val_accuracy: 0.7812 - val_false_negatives: 13.0000 - val_false_positives: 15.0000 - val_loss: 0.3891 - val_true_negatives: 49.0000 - val_true_positives: 51.0000\n",
      "Epoch 105/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m870s\u001b[0m 148s/step - accuracy: 0.8059 - false_negatives: 46.7143 - false_positives: 48.2857 - loss: 0.4275 - true_negatives: 198.5714 - true_positives: 200.1429 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.4009 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 106/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m896s\u001b[0m 154s/step - accuracy: 0.8347 - false_negatives: 40.5714 - false_positives: 40.2857 - loss: 0.3583 - true_negatives: 206.5714 - true_positives: 206.2857 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3702 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 107/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m859s\u001b[0m 149s/step - accuracy: 0.7795 - false_negatives: 55.1429 - false_positives: 57.1429 - loss: 0.4432 - true_negatives: 189.7143 - true_positives: 191.7143 - val_accuracy: 0.8594 - val_false_negatives: 8.0000 - val_false_positives: 9.0000 - val_loss: 0.2809 - val_true_negatives: 55.0000 - val_true_positives: 56.0000\n",
      "Epoch 108/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m852s\u001b[0m 149s/step - accuracy: 0.8178 - false_negatives: 45.7143 - false_positives: 45.5714 - loss: 0.3798 - true_negatives: 201.2857 - true_positives: 201.1429 - val_accuracy: 0.7500 - val_false_negatives: 16.0000 - val_false_positives: 17.0000 - val_loss: 0.4573 - val_true_negatives: 47.0000 - val_true_positives: 48.0000\n",
      "Epoch 109/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m909s\u001b[0m 155s/step - accuracy: 0.8606 - false_negatives: 38.1429 - false_positives: 37.7143 - loss: 0.3781 - true_negatives: 209.1429 - true_positives: 208.7143 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3604 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 110/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m883s\u001b[0m 151s/step - accuracy: 0.8205 - false_negatives: 44.8571 - false_positives: 46.2857 - loss: 0.4002 - true_negatives: 200.5714 - true_positives: 202.0000 - val_accuracy: 0.8750 - val_false_negatives: 7.0000 - val_false_positives: 8.0000 - val_loss: 0.3248 - val_true_negatives: 56.0000 - val_true_positives: 57.0000\n",
      "Epoch 111/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m850s\u001b[0m 144s/step - accuracy: 0.8358 - false_negatives: 43.0000 - false_positives: 40.8571 - loss: 0.3645 - true_negatives: 206.0000 - true_positives: 203.8571 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.3276 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 112/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m884s\u001b[0m 154s/step - accuracy: 0.8536 - false_negatives: 39.2857 - false_positives: 40.7143 - loss: 0.4007 - true_negatives: 206.1429 - true_positives: 207.5714 - val_accuracy: 0.8594 - val_false_negatives: 10.0000 - val_false_positives: 9.0000 - val_loss: 0.3727 - val_true_negatives: 55.0000 - val_true_positives: 54.0000\n",
      "Epoch 113/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m864s\u001b[0m 149s/step - accuracy: 0.8450 - false_negatives: 40.2857 - false_positives: 39.2857 - loss: 0.3393 - true_negatives: 207.5714 - true_positives: 206.5714 - val_accuracy: 0.8750 - val_false_negatives: 7.0000 - val_false_positives: 8.0000 - val_loss: 0.3078 - val_true_negatives: 56.0000 - val_true_positives: 57.0000\n",
      "Epoch 114/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m862s\u001b[0m 148s/step - accuracy: 0.8485 - false_negatives: 39.1429 - false_positives: 39.0000 - loss: 0.3693 - true_negatives: 207.8571 - true_positives: 207.7143 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3629 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 115/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m854s\u001b[0m 145s/step - accuracy: 0.8506 - false_negatives: 39.5714 - false_positives: 40.5714 - loss: 0.3674 - true_negatives: 206.2857 - true_positives: 207.2857 - val_accuracy: 0.8906 - val_false_negatives: 6.0000 - val_false_positives: 7.0000 - val_loss: 0.3098 - val_true_negatives: 57.0000 - val_true_positives: 58.0000\n",
      "Epoch 116/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m849s\u001b[0m 146s/step - accuracy: 0.7866 - false_negatives: 50.0000 - false_positives: 51.2857 - loss: 0.4803 - true_negatives: 195.5714 - true_positives: 196.8571 - val_accuracy: 0.7500 - val_false_negatives: 17.0000 - val_false_positives: 17.0000 - val_loss: 0.4499 - val_true_negatives: 47.0000 - val_true_positives: 47.0000\n",
      "Epoch 117/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m847s\u001b[0m 144s/step - accuracy: 0.8308 - false_negatives: 42.7143 - false_positives: 43.8571 - loss: 0.3633 - true_negatives: 203.0000 - true_positives: 204.1429 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.3024 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 118/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m833s\u001b[0m 143s/step - accuracy: 0.8339 - false_negatives: 39.7143 - false_positives: 40.2857 - loss: 0.3892 - true_negatives: 206.5714 - true_positives: 207.1429 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.3490 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 119/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m841s\u001b[0m 145s/step - accuracy: 0.8259 - false_negatives: 43.2857 - false_positives: 42.7143 - loss: 0.4161 - true_negatives: 204.1429 - true_positives: 203.5714 - val_accuracy: 0.8906 - val_false_negatives: 8.0000 - val_false_positives: 7.0000 - val_loss: 0.3811 - val_true_negatives: 57.0000 - val_true_positives: 56.0000\n",
      "Epoch 120/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m811s\u001b[0m 138s/step - accuracy: 0.8519 - false_negatives: 37.2857 - false_positives: 37.7143 - loss: 0.3767 - true_negatives: 209.1429 - true_positives: 209.5714 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.3765 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 121/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m824s\u001b[0m 142s/step - accuracy: 0.8174 - false_negatives: 46.5714 - false_positives: 47.4286 - loss: 0.4006 - true_negatives: 199.4286 - true_positives: 200.2857 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3201 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 122/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m854s\u001b[0m 145s/step - accuracy: 0.8097 - false_negatives: 44.7143 - false_positives: 44.8571 - loss: 0.3910 - true_negatives: 202.0000 - true_positives: 202.1429 - val_accuracy: 0.7812 - val_false_negatives: 12.0000 - val_false_positives: 13.0000 - val_loss: 0.3686 - val_true_negatives: 51.0000 - val_true_positives: 52.0000\n",
      "Epoch 123/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m908s\u001b[0m 154s/step - accuracy: 0.8073 - false_negatives: 45.0000 - false_positives: 43.7143 - loss: 0.4260 - true_negatives: 203.1429 - true_positives: 201.8571 - val_accuracy: 0.9062 - val_false_negatives: 7.0000 - val_false_positives: 6.0000 - val_loss: 0.3440 - val_true_negatives: 58.0000 - val_true_positives: 57.0000\n",
      "Epoch 124/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m884s\u001b[0m 149s/step - accuracy: 0.8289 - false_negatives: 41.2857 - false_positives: 40.0000 - loss: 0.3446 - true_negatives: 206.8571 - true_positives: 205.5714 - val_accuracy: 0.7969 - val_false_negatives: 14.0000 - val_false_positives: 13.0000 - val_loss: 0.4455 - val_true_negatives: 51.0000 - val_true_positives: 50.0000\n",
      "Epoch 125/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m849s\u001b[0m 147s/step - accuracy: 0.8791 - false_negatives: 32.0000 - false_positives: 28.2857 - loss: 0.2886 - true_negatives: 218.5714 - true_positives: 214.8571 - val_accuracy: 0.9062 - val_false_negatives: 7.0000 - val_false_positives: 6.0000 - val_loss: 0.3234 - val_true_negatives: 58.0000 - val_true_positives: 57.0000\n",
      "Epoch 126/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m877s\u001b[0m 151s/step - accuracy: 0.8448 - false_negatives: 41.2857 - false_positives: 41.0000 - loss: 0.3353 - true_negatives: 205.8571 - true_positives: 205.5714 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 13.0000 - val_loss: 0.4374 - val_true_negatives: 51.0000 - val_true_positives: 51.0000\n",
      "Epoch 127/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m839s\u001b[0m 143s/step - accuracy: 0.8325 - false_negatives: 41.0000 - false_positives: 39.4286 - loss: 0.4021 - true_negatives: 207.4286 - true_positives: 205.8571 - val_accuracy: 0.7812 - val_false_negatives: 14.0000 - val_false_positives: 14.0000 - val_loss: 0.3958 - val_true_negatives: 50.0000 - val_true_positives: 50.0000\n",
      "Epoch 128/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m872s\u001b[0m 151s/step - accuracy: 0.8274 - false_negatives: 41.7143 - false_positives: 41.2857 - loss: 0.3929 - true_negatives: 205.5714 - true_positives: 205.1429 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3744 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 129/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m879s\u001b[0m 151s/step - accuracy: 0.7993 - false_negatives: 50.0000 - false_positives: 46.1429 - loss: 0.4265 - true_negatives: 200.7143 - true_positives: 196.8571 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.4698 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 130/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m861s\u001b[0m 150s/step - accuracy: 0.8437 - false_negatives: 40.2857 - false_positives: 41.4286 - loss: 0.3614 - true_negatives: 205.4286 - true_positives: 206.5714 - val_accuracy: 0.8906 - val_false_negatives: 7.0000 - val_false_positives: 7.0000 - val_loss: 0.3645 - val_true_negatives: 57.0000 - val_true_positives: 57.0000\n",
      "Epoch 131/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m868s\u001b[0m 148s/step - accuracy: 0.8163 - false_negatives: 46.8571 - false_positives: 44.2857 - loss: 0.3819 - true_negatives: 202.5714 - true_positives: 200.0000 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.4422 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 132/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m842s\u001b[0m 145s/step - accuracy: 0.8121 - false_negatives: 44.8571 - false_positives: 43.5714 - loss: 0.4021 - true_negatives: 203.2857 - true_positives: 202.0000 - val_accuracy: 0.7969 - val_false_negatives: 14.0000 - val_false_positives: 12.0000 - val_loss: 0.4417 - val_true_negatives: 52.0000 - val_true_positives: 50.0000\n",
      "Epoch 133/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m871s\u001b[0m 151s/step - accuracy: 0.8292 - false_negatives: 42.8571 - false_positives: 43.2857 - loss: 0.3886 - true_negatives: 203.5714 - true_positives: 204.0000 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 8.0000 - val_loss: 0.2599 - val_true_negatives: 56.0000 - val_true_positives: 55.0000\n",
      "Epoch 134/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m865s\u001b[0m 151s/step - accuracy: 0.8363 - false_negatives: 41.8571 - false_positives: 45.5714 - loss: 0.3936 - true_negatives: 201.2857 - true_positives: 205.0000 - val_accuracy: 0.8906 - val_false_negatives: 7.0000 - val_false_positives: 8.0000 - val_loss: 0.3115 - val_true_negatives: 56.0000 - val_true_positives: 57.0000\n",
      "Epoch 135/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m824s\u001b[0m 142s/step - accuracy: 0.8506 - false_negatives: 37.8571 - false_positives: 35.7143 - loss: 0.3729 - true_negatives: 211.1429 - true_positives: 209.0000 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.2696 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 136/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m809s\u001b[0m 139s/step - accuracy: 0.8395 - false_negatives: 39.5714 - false_positives: 38.8571 - loss: 0.3831 - true_negatives: 208.0000 - true_positives: 207.2857 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 9.0000 - val_loss: 0.3592 - val_true_negatives: 55.0000 - val_true_positives: 54.0000\n",
      "Epoch 137/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m798s\u001b[0m 141s/step - accuracy: 0.8781 - false_negatives: 30.4286 - false_positives: 31.4286 - loss: 0.3163 - true_negatives: 215.4286 - true_positives: 216.4286 - val_accuracy: 0.7969 - val_false_negatives: 11.0000 - val_false_positives: 13.0000 - val_loss: 0.3779 - val_true_negatives: 51.0000 - val_true_positives: 53.0000\n",
      "Epoch 138/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m847s\u001b[0m 147s/step - accuracy: 0.8530 - false_negatives: 37.0000 - false_positives: 37.7143 - loss: 0.3764 - true_negatives: 209.1429 - true_positives: 209.8571 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.2914 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 139/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m814s\u001b[0m 139s/step - accuracy: 0.7766 - false_negatives: 52.8571 - false_positives: 55.5714 - loss: 0.4308 - true_negatives: 191.2857 - true_positives: 194.0000 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 9.0000 - val_loss: 0.3160 - val_true_negatives: 55.0000 - val_true_positives: 56.0000\n",
      "Epoch 140/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m835s\u001b[0m 144s/step - accuracy: 0.8193 - false_negatives: 43.0000 - false_positives: 44.4286 - loss: 0.3825 - true_negatives: 202.4286 - true_positives: 203.8571 - val_accuracy: 0.8906 - val_false_negatives: 8.0000 - val_false_positives: 7.0000 - val_loss: 0.3069 - val_true_negatives: 57.0000 - val_true_positives: 56.0000\n",
      "Epoch 141/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m835s\u001b[0m 143s/step - accuracy: 0.8700 - false_negatives: 35.5714 - false_positives: 36.1429 - loss: 0.3485 - true_negatives: 210.7143 - true_positives: 211.2857 - val_accuracy: 0.8906 - val_false_negatives: 7.0000 - val_false_positives: 8.0000 - val_loss: 0.3038 - val_true_negatives: 56.0000 - val_true_positives: 57.0000\n",
      "Epoch 142/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m794s\u001b[0m 136s/step - accuracy: 0.8391 - false_negatives: 42.2857 - false_positives: 39.2857 - loss: 0.3662 - true_negatives: 207.5714 - true_positives: 204.5714 - val_accuracy: 0.9062 - val_false_negatives: 6.0000 - val_false_positives: 7.0000 - val_loss: 0.3034 - val_true_negatives: 57.0000 - val_true_positives: 58.0000\n",
      "Epoch 143/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m782s\u001b[0m 134s/step - accuracy: 0.8225 - false_negatives: 43.4286 - false_positives: 44.7143 - loss: 0.3998 - true_negatives: 202.1429 - true_positives: 203.4286 - val_accuracy: 0.8750 - val_false_negatives: 9.0000 - val_false_positives: 7.0000 - val_loss: 0.2952 - val_true_negatives: 57.0000 - val_true_positives: 55.0000\n",
      "Epoch 144/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m837s\u001b[0m 145s/step - accuracy: 0.8711 - false_negatives: 35.0000 - false_positives: 33.4286 - loss: 0.3508 - true_negatives: 213.4286 - true_positives: 211.8571 - val_accuracy: 0.9375 - val_false_negatives: 4.0000 - val_false_positives: 4.0000 - val_loss: 0.2355 - val_true_negatives: 60.0000 - val_true_positives: 60.0000\n",
      "Epoch 145/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m820s\u001b[0m 142s/step - accuracy: 0.8095 - false_negatives: 47.8571 - false_positives: 42.4286 - loss: 0.4219 - true_negatives: 204.4286 - true_positives: 199.0000 - val_accuracy: 0.9062 - val_false_negatives: 6.0000 - val_false_positives: 6.0000 - val_loss: 0.2796 - val_true_negatives: 58.0000 - val_true_positives: 58.0000\n",
      "Epoch 146/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m837s\u001b[0m 145s/step - accuracy: 0.8140 - false_negatives: 46.0000 - false_positives: 43.8571 - loss: 0.3742 - true_negatives: 203.0000 - true_positives: 200.8571 - val_accuracy: 0.7812 - val_false_negatives: 15.0000 - val_false_positives: 15.0000 - val_loss: 0.3691 - val_true_negatives: 49.0000 - val_true_positives: 49.0000\n",
      "Epoch 147/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m841s\u001b[0m 147s/step - accuracy: 0.8553 - false_negatives: 41.1429 - false_positives: 38.1429 - loss: 0.3539 - true_negatives: 208.7143 - true_positives: 205.7143 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3566 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 148/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m844s\u001b[0m 144s/step - accuracy: 0.8292 - false_negatives: 44.2857 - false_positives: 44.4286 - loss: 0.4020 - true_negatives: 202.4286 - true_positives: 202.5714 - val_accuracy: 0.8125 - val_false_negatives: 12.0000 - val_false_positives: 11.0000 - val_loss: 0.4378 - val_true_negatives: 53.0000 - val_true_positives: 52.0000\n",
      "Epoch 149/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m851s\u001b[0m 146s/step - accuracy: 0.8435 - false_negatives: 38.5714 - false_positives: 33.8571 - loss: 0.3568 - true_negatives: 213.0000 - true_positives: 208.2857 - val_accuracy: 0.9375 - val_false_negatives: 4.0000 - val_false_positives: 4.0000 - val_loss: 0.2652 - val_true_negatives: 60.0000 - val_true_positives: 60.0000\n",
      "Epoch 150/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m875s\u001b[0m 150s/step - accuracy: 0.8621 - false_negatives: 36.0000 - false_positives: 35.8571 - loss: 0.3204 - true_negatives: 211.0000 - true_positives: 210.8571 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 12.0000 - val_loss: 0.3732 - val_true_negatives: 52.0000 - val_true_positives: 51.0000\n",
      "Epoch 151/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m909s\u001b[0m 155s/step - accuracy: 0.8185 - false_negatives: 44.1429 - false_positives: 44.2857 - loss: 0.3866 - true_negatives: 202.5714 - true_positives: 202.7143 - val_accuracy: 0.7656 - val_false_negatives: 15.0000 - val_false_positives: 15.0000 - val_loss: 0.4307 - val_true_negatives: 49.0000 - val_true_positives: 49.0000\n",
      "Epoch 152/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m858s\u001b[0m 146s/step - accuracy: 0.8316 - false_negatives: 38.5714 - false_positives: 39.8571 - loss: 0.3435 - true_negatives: 207.0000 - true_positives: 208.2857 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3521 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 153/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m841s\u001b[0m 144s/step - accuracy: 0.8353 - false_negatives: 39.7143 - false_positives: 40.4286 - loss: 0.3858 - true_negatives: 199.5714 - true_positives: 200.2857 - val_accuracy: 0.8125 - val_false_negatives: 11.0000 - val_false_positives: 12.0000 - val_loss: 0.3367 - val_true_negatives: 52.0000 - val_true_positives: 53.0000\n",
      "Epoch 154/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m856s\u001b[0m 146s/step - accuracy: 0.8487 - false_negatives: 38.2857 - false_positives: 35.8571 - loss: 0.3788 - true_negatives: 211.0000 - true_positives: 208.5714 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.2803 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 155/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m823s\u001b[0m 143s/step - accuracy: 0.8426 - false_negatives: 40.5714 - false_positives: 38.8571 - loss: 0.3963 - true_negatives: 208.0000 - true_positives: 206.2857 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.2938 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 156/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m849s\u001b[0m 147s/step - accuracy: 0.8158 - false_negatives: 47.5714 - false_positives: 45.8571 - loss: 0.3810 - true_negatives: 201.0000 - true_positives: 199.2857 - val_accuracy: 0.9062 - val_false_negatives: 6.0000 - val_false_positives: 6.0000 - val_loss: 0.2351 - val_true_negatives: 58.0000 - val_true_positives: 58.0000\n",
      "Epoch 157/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m846s\u001b[0m 146s/step - accuracy: 0.8283 - false_negatives: 41.0000 - false_positives: 41.2857 - loss: 0.3476 - true_negatives: 205.5714 - true_positives: 205.8571 - val_accuracy: 0.7656 - val_false_negatives: 15.0000 - val_false_positives: 14.0000 - val_loss: 0.4504 - val_true_negatives: 50.0000 - val_true_positives: 49.0000\n",
      "Epoch 158/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m860s\u001b[0m 146s/step - accuracy: 0.8519 - false_negatives: 38.0000 - false_positives: 38.2857 - loss: 0.3551 - true_negatives: 208.5714 - true_positives: 208.8571 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 11.0000 - val_loss: 0.3690 - val_true_negatives: 53.0000 - val_true_positives: 54.0000\n",
      "Epoch 159/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m847s\u001b[0m 143s/step - accuracy: 0.8408 - false_negatives: 36.5714 - false_positives: 36.8571 - loss: 0.3474 - true_negatives: 210.0000 - true_positives: 210.2857 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3094 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 160/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m858s\u001b[0m 148s/step - accuracy: 0.8690 - false_negatives: 35.5714 - false_positives: 33.0000 - loss: 0.2948 - true_negatives: 213.8571 - true_positives: 211.2857 - val_accuracy: 0.7812 - val_false_negatives: 14.0000 - val_false_positives: 14.0000 - val_loss: 0.4068 - val_true_negatives: 50.0000 - val_true_positives: 50.0000\n",
      "Epoch 161/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m862s\u001b[0m 148s/step - accuracy: 0.8339 - false_negatives: 40.5714 - false_positives: 41.7143 - loss: 0.3605 - true_negatives: 205.1429 - true_positives: 206.2857 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3872 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 162/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m846s\u001b[0m 145s/step - accuracy: 0.8169 - false_negatives: 43.2857 - false_positives: 43.1429 - loss: 0.3896 - true_negatives: 203.7143 - true_positives: 203.5714 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.2677 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 163/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m863s\u001b[0m 146s/step - accuracy: 0.8481 - false_negatives: 38.7143 - false_positives: 37.0000 - loss: 0.3596 - true_negatives: 209.8571 - true_positives: 208.1429 - val_accuracy: 0.9062 - val_false_negatives: 6.0000 - val_false_positives: 6.0000 - val_loss: 0.2274 - val_true_negatives: 58.0000 - val_true_positives: 58.0000\n",
      "Epoch 164/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m838s\u001b[0m 143s/step - accuracy: 0.8081 - false_negatives: 45.0000 - false_positives: 44.7143 - loss: 0.3833 - true_negatives: 202.1429 - true_positives: 201.8571 - val_accuracy: 0.8125 - val_false_negatives: 12.0000 - val_false_positives: 12.0000 - val_loss: 0.3816 - val_true_negatives: 52.0000 - val_true_positives: 52.0000\n",
      "Epoch 165/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m848s\u001b[0m 145s/step - accuracy: 0.8126 - false_negatives: 48.2857 - false_positives: 44.8571 - loss: 0.3669 - true_negatives: 202.0000 - true_positives: 198.5714 - val_accuracy: 0.7656 - val_false_negatives: 15.0000 - val_false_positives: 16.0000 - val_loss: 0.4968 - val_true_negatives: 48.0000 - val_true_positives: 49.0000\n",
      "Epoch 166/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m870s\u001b[0m 149s/step - accuracy: 0.8715 - false_negatives: 33.2857 - false_positives: 33.0000 - loss: 0.3042 - true_negatives: 213.8571 - true_positives: 213.5714 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3514 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 167/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m861s\u001b[0m 148s/step - accuracy: 0.8457 - false_negatives: 38.7143 - false_positives: 37.4286 - loss: 0.3652 - true_negatives: 209.4286 - true_positives: 208.1429 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.3621 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 168/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m839s\u001b[0m 145s/step - accuracy: 0.8645 - false_negatives: 33.7143 - false_positives: 34.0000 - loss: 0.3193 - true_negatives: 212.8571 - true_positives: 213.1429 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 13.0000 - val_loss: 0.4346 - val_true_negatives: 51.0000 - val_true_positives: 51.0000\n",
      "Epoch 169/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m845s\u001b[0m 144s/step - accuracy: 0.8378 - false_negatives: 40.8571 - false_positives: 43.4286 - loss: 0.3525 - true_negatives: 203.4286 - true_positives: 206.0000 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.3169 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 170/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m855s\u001b[0m 147s/step - accuracy: 0.8582 - false_negatives: 34.7143 - false_positives: 35.0000 - loss: 0.3894 - true_negatives: 211.8571 - true_positives: 212.1429 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3765 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 171/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m864s\u001b[0m 148s/step - accuracy: 0.8158 - false_negatives: 43.7143 - false_positives: 45.5714 - loss: 0.4066 - true_negatives: 201.2857 - true_positives: 203.1429 - val_accuracy: 0.8125 - val_false_negatives: 12.0000 - val_false_positives: 12.0000 - val_loss: 0.4290 - val_true_negatives: 52.0000 - val_true_positives: 52.0000\n",
      "Epoch 172/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m854s\u001b[0m 146s/step - accuracy: 0.8525 - false_negatives: 35.4286 - false_positives: 35.2857 - loss: 0.3378 - true_negatives: 211.5714 - true_positives: 211.4286 - val_accuracy: 0.6875 - val_false_negatives: 20.0000 - val_false_positives: 20.0000 - val_loss: 0.4814 - val_true_negatives: 44.0000 - val_true_positives: 44.0000\n",
      "Epoch 173/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m848s\u001b[0m 142s/step - accuracy: 0.8477 - false_negatives: 38.0000 - false_positives: 37.0000 - loss: 0.3677 - true_negatives: 209.8571 - true_positives: 208.8571 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.3480 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 174/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m840s\u001b[0m 145s/step - accuracy: 0.8822 - false_negatives: 30.4286 - false_positives: 32.4286 - loss: 0.3277 - true_negatives: 214.4286 - true_positives: 216.4286 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.3664 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 175/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m886s\u001b[0m 153s/step - accuracy: 0.7937 - false_negatives: 47.8571 - false_positives: 48.1429 - loss: 0.4249 - true_negatives: 198.7143 - true_positives: 199.0000 - val_accuracy: 0.8750 - val_false_negatives: 9.0000 - val_false_positives: 8.0000 - val_loss: 0.2775 - val_true_negatives: 56.0000 - val_true_positives: 55.0000\n",
      "Epoch 176/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m840s\u001b[0m 144s/step - accuracy: 0.8556 - false_negatives: 35.7143 - false_positives: 35.8571 - loss: 0.3386 - true_negatives: 211.0000 - true_positives: 211.1429 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3074 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 177/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m837s\u001b[0m 144s/step - accuracy: 0.8476 - false_negatives: 37.2857 - false_positives: 38.8571 - loss: 0.3577 - true_negatives: 208.0000 - true_positives: 209.5714 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 11.0000 - val_loss: 0.2959 - val_true_negatives: 53.0000 - val_true_positives: 54.0000\n",
      "Epoch 178/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m824s\u001b[0m 142s/step - accuracy: 0.8562 - false_negatives: 37.2857 - false_positives: 38.4286 - loss: 0.3134 - true_negatives: 208.4286 - true_positives: 209.5714 - val_accuracy: 0.8750 - val_false_negatives: 7.0000 - val_false_positives: 8.0000 - val_loss: 0.4015 - val_true_negatives: 56.0000 - val_true_positives: 57.0000\n",
      "Epoch 179/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m823s\u001b[0m 140s/step - accuracy: 0.8454 - false_negatives: 39.0000 - false_positives: 36.1429 - loss: 0.3882 - true_negatives: 210.7143 - true_positives: 207.8571 - val_accuracy: 0.8125 - val_false_negatives: 12.0000 - val_false_positives: 12.0000 - val_loss: 0.3333 - val_true_negatives: 52.0000 - val_true_positives: 52.0000\n",
      "Epoch 180/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m859s\u001b[0m 147s/step - accuracy: 0.8343 - false_negatives: 39.1429 - false_positives: 40.2857 - loss: 0.3908 - true_negatives: 206.5714 - true_positives: 207.7143 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3335 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 181/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m843s\u001b[0m 142s/step - accuracy: 0.8637 - false_negatives: 33.8571 - false_positives: 35.2857 - loss: 0.3680 - true_negatives: 211.5714 - true_positives: 213.0000 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3570 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 182/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m827s\u001b[0m 143s/step - accuracy: 0.8455 - false_negatives: 37.7143 - false_positives: 39.1429 - loss: 0.3518 - true_negatives: 207.7143 - true_positives: 209.1429 - val_accuracy: 0.8906 - val_false_negatives: 7.0000 - val_false_positives: 7.0000 - val_loss: 0.3284 - val_true_negatives: 57.0000 - val_true_positives: 57.0000\n",
      "Epoch 183/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m861s\u001b[0m 148s/step - accuracy: 0.8306 - false_negatives: 41.1429 - false_positives: 42.7143 - loss: 0.3998 - true_negatives: 204.1429 - true_positives: 205.7143 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 13.0000 - val_loss: 0.4040 - val_true_negatives: 51.0000 - val_true_positives: 51.0000\n",
      "Epoch 184/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m843s\u001b[0m 145s/step - accuracy: 0.8397 - false_negatives: 41.2857 - false_positives: 40.1429 - loss: 0.3634 - true_negatives: 206.7143 - true_positives: 205.5714 - val_accuracy: 0.8750 - val_false_negatives: 8.0000 - val_false_positives: 8.0000 - val_loss: 0.3078 - val_true_negatives: 56.0000 - val_true_positives: 56.0000\n",
      "Epoch 185/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m856s\u001b[0m 147s/step - accuracy: 0.8229 - false_negatives: 43.2857 - false_positives: 42.4286 - loss: 0.3492 - true_negatives: 204.4286 - true_positives: 203.5714 - val_accuracy: 0.7969 - val_false_negatives: 13.0000 - val_false_positives: 13.0000 - val_loss: 0.3515 - val_true_negatives: 51.0000 - val_true_positives: 51.0000\n",
      "Epoch 186/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m829s\u001b[0m 142s/step - accuracy: 0.8411 - false_negatives: 38.5714 - false_positives: 37.4286 - loss: 0.3572 - true_negatives: 209.4286 - true_positives: 208.2857 - val_accuracy: 0.9531 - val_false_negatives: 4.0000 - val_false_positives: 4.0000 - val_loss: 0.2252 - val_true_negatives: 60.0000 - val_true_positives: 60.0000\n",
      "Epoch 187/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m874s\u001b[0m 149s/step - accuracy: 0.8382 - false_negatives: 38.4286 - false_positives: 38.0000 - loss: 0.3225 - true_negatives: 208.8571 - true_positives: 208.4286 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 9.0000 - val_loss: 0.3135 - val_true_negatives: 55.0000 - val_true_positives: 54.0000\n",
      "Epoch 188/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m842s\u001b[0m 143s/step - accuracy: 0.8250 - false_negatives: 42.4286 - false_positives: 42.4286 - loss: 0.3953 - true_negatives: 204.4286 - true_positives: 204.4286 - val_accuracy: 0.7656 - val_false_negatives: 15.0000 - val_false_positives: 15.0000 - val_loss: 0.4501 - val_true_negatives: 49.0000 - val_true_positives: 49.0000\n",
      "Epoch 189/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m857s\u001b[0m 149s/step - accuracy: 0.7965 - false_negatives: 49.8571 - false_positives: 47.4286 - loss: 0.4329 - true_negatives: 199.4286 - true_positives: 197.0000 - val_accuracy: 0.7812 - val_false_negatives: 14.0000 - val_false_positives: 14.0000 - val_loss: 0.4762 - val_true_negatives: 50.0000 - val_true_positives: 50.0000\n",
      "Epoch 190/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m846s\u001b[0m 144s/step - accuracy: 0.8031 - false_negatives: 46.4286 - false_positives: 46.8571 - loss: 0.4162 - true_negatives: 200.0000 - true_positives: 200.4286 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 10.0000 - val_loss: 0.3315 - val_true_negatives: 54.0000 - val_true_positives: 53.0000\n",
      "Epoch 191/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m855s\u001b[0m 147s/step - accuracy: 0.8398 - false_negatives: 38.4286 - false_positives: 36.2857 - loss: 0.3510 - true_negatives: 210.5714 - true_positives: 208.4286 - val_accuracy: 0.9688 - val_false_negatives: 3.0000 - val_false_positives: 2.0000 - val_loss: 0.2051 - val_true_negatives: 62.0000 - val_true_positives: 61.0000\n",
      "Epoch 192/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m860s\u001b[0m 149s/step - accuracy: 0.8483 - false_negatives: 38.2857 - false_positives: 38.5714 - loss: 0.3496 - true_negatives: 208.2857 - true_positives: 208.5714 - val_accuracy: 0.8594 - val_false_negatives: 9.0000 - val_false_positives: 9.0000 - val_loss: 0.3389 - val_true_negatives: 55.0000 - val_true_positives: 55.0000\n",
      "Epoch 193/200\n",
      "\u001b[1m5/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m1:59\u001b[0m 119s/step - accuracy: 0.8766 - false_negatives: 23.0000 - false_positives: 22.8000 - loss: 0.3000 - true_negatives: 169.2000 - true_positives: 169.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Peter\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m726s\u001b[0m 121s/step - accuracy: 0.8763 - false_negatives: 25.8333 - false_positives: 25.5000 - loss: 0.2918 - true_negatives: 187.8333 - true_positives: 187.5000 - val_accuracy: 0.8125 - val_false_negatives: 14.0000 - val_false_positives: 12.0000 - val_loss: 0.3825 - val_true_negatives: 52.0000 - val_true_positives: 50.0000\n",
      "Epoch 194/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m874s\u001b[0m 148s/step - accuracy: 0.8679 - false_negatives: 31.4286 - false_positives: 32.0000 - loss: 0.3206 - true_negatives: 214.8571 - true_positives: 215.4286 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 9.0000 - val_loss: 0.3345 - val_true_negatives: 55.0000 - val_true_positives: 54.0000\n",
      "Epoch 195/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m895s\u001b[0m 155s/step - accuracy: 0.8378 - false_negatives: 38.5714 - false_positives: 37.1429 - loss: 0.3294 - true_negatives: 209.7143 - true_positives: 208.2857 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3521 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 196/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m872s\u001b[0m 148s/step - accuracy: 0.8836 - false_negatives: 29.4286 - false_positives: 29.0000 - loss: 0.2948 - true_negatives: 217.8571 - true_positives: 217.4286 - val_accuracy: 0.8281 - val_false_negatives: 11.0000 - val_false_positives: 11.0000 - val_loss: 0.3612 - val_true_negatives: 53.0000 - val_true_positives: 53.0000\n",
      "Epoch 197/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m885s\u001b[0m 151s/step - accuracy: 0.8604 - false_negatives: 31.7143 - false_positives: 33.5714 - loss: 0.3524 - true_negatives: 213.2857 - true_positives: 215.1429 - val_accuracy: 0.9062 - val_false_negatives: 6.0000 - val_false_positives: 6.0000 - val_loss: 0.2941 - val_true_negatives: 58.0000 - val_true_positives: 58.0000\n",
      "Epoch 198/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m855s\u001b[0m 146s/step - accuracy: 0.8281 - false_negatives: 42.4286 - false_positives: 43.8571 - loss: 0.3788 - true_negatives: 203.0000 - true_positives: 204.4286 - val_accuracy: 0.8125 - val_false_negatives: 13.0000 - val_false_positives: 13.0000 - val_loss: 0.4083 - val_true_negatives: 51.0000 - val_true_positives: 51.0000\n",
      "Epoch 199/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m805s\u001b[0m 139s/step - accuracy: 0.8414 - false_negatives: 39.8571 - false_positives: 40.5714 - loss: 0.3780 - true_negatives: 206.2857 - true_positives: 207.0000 - val_accuracy: 0.8438 - val_false_negatives: 10.0000 - val_false_positives: 10.0000 - val_loss: 0.3662 - val_true_negatives: 54.0000 - val_true_positives: 54.0000\n",
      "Epoch 200/200\n",
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m807s\u001b[0m 138s/step - accuracy: 0.8842 - false_negatives: 30.0000 - false_positives: 31.2857 - loss: 0.2876 - true_negatives: 215.5714 - true_positives: 216.8571 - val_accuracy: 0.7031 - val_false_negatives: 19.0000 - val_false_positives: 19.0000 - val_loss: 0.5121 - val_true_negatives: 45.0000 - val_true_positives: 45.0000\n"
     ]
    }
   ],
   "source": [
    "# Обучаем модель с использованием генераторов\n",
    "# train_generator - генератор данных для обучения\n",
    "# validation_data - генератор данных для проверки\n",
    "\n",
    "# Need to run with real data to infer shape of different layers\n",
    "history = model.fit(train_generator,\n",
    "    steps_per_epoch=6,\n",
    "    epochs=200,\n",
    "    validation_data=val_generator,\n",
    "    validation_steps=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "seeing-contract",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the training and validation accuracy and loss at each epoch\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'orange', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.savefig('loss-unnorm-cocowm-200epochs-partlevelwm.png')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "plt.plot(epochs, acc, 'blue', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'orange', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.savefig('acc-unnorm-cocowm-200epochs-partlevelwm.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "north-indication",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m386/386\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47889s\u001b[0m 124s/step - accuracy: 0.6581 - false_negatives: 4251.1914 - false_positives: 4325.0312 - loss: 0.9467 - true_negatives: 8090.7827 - true_positives: 8164.6226\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_generator)\n",
    "# print(f\"\\nТочность на тестовых данных: {(scores[1]*100):.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ecf2da07-4ca7-45bc-a31b-018954db5063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'compile_metrics']\n",
      "[0.9456852674484253, 0.6529959440231323, 8495.0, 8642.0, 16058.0, 16205.0]\n"
     ]
    }
   ],
   "source": [
    "print(model.metrics_names)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16298878-9d4b-4745-a417-b77670f52173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент корреляции Мэтьюса = -0.30619975473216293\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "mcc = (scores[-4]*scores[-3] - scores[-2]*scores[-1])/sqrt((scores[-4]+scores[-2])*(scores[-4]+scores[-1])*(scores[-3]+scores[-2])*(scores[-3]+scores[-1]))\n",
    "print(f'Коэффициент корреляции Мэтьюса = {mcc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55e416bd-394e-4077-99c7-a767df6151b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('pretrained-classificator-wm-coco.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e23d99af-b598-4337-8275-fff65c1ccb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\"pretrained-classificator-wm-coco.keras\")\n",
    "print(\"Loaded model from disk\")\n",
    "\n",
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "              loss='binary_crossentropy', # 'categorical_crossentropy',\n",
    "              metrics=['accuracy', metrics.TruePositives(), metrics.TrueNegatives(), metrics.FalsePositives(), metrics.FalseNegatives()])\n",
    "\n",
    "test_dir = 'DATASET-coco/test'\n",
    "test_datagen = ImageDataGenerator()\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=64,\n",
    "    class_mode='categorical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a361e3a5-b14e-46f4-888c-1b904ee1bc57",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = model.evaluate(test_generator)\n",
    "print(model.metrics_names)\n",
    "print(scores)\n",
    "\n",
    "from math import sqrt\n",
    "mcc = (scores[-4]*scores[-3] - scores[-2]*scores[-1])/sqrt((scores[-4]+scores[-2])*(scores[-4]+scores[-1])*(scores[-3]+scores[-2])*(scores[-3]+scores[-1]))\n",
    "print(f'Коэффициент корреляции Мэтьюса = {mcc}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}