dogcat_vgg16_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import VGG16\n",
    "\n",
    "#\n",
    "conv_base = VGG16(weights='imagenet',\n",
    " include_top=False,\n",
    " input_shape=(150, 150, 3))\n",
    "\n",
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "1/1 [==============================] - 1s 736ms/step\n",
      "1/1 [==============================] - 1s 565ms/step\n",
      "1/1 [==============================] - 1s 518ms/step\n",
      "1/1 [==============================] - 1s 510ms/step\n",
      "1/1 [==============================] - 1s 532ms/step\n",
      "1/1 [==============================] - 1s 528ms/step\n",
      "1/1 [==============================] - 1s 518ms/step\n",
      "1/1 [==============================] - 1s 534ms/step\n",
      "1/1 [==============================] - 1s 518ms/step\n",
      "1/1 [==============================] - 1s 512ms/step\n",
      "1/1 [==============================] - 1s 513ms/step\n",
      "1/1 [==============================] - 1s 504ms/step\n",
      "1/1 [==============================] - 1s 506ms/step\n",
      "1/1 [==============================] - 1s 503ms/step\n",
      "1/1 [==============================] - 1s 501ms/step\n",
      "1/1 [==============================] - 1s 513ms/step\n",
      "1/1 [==============================] - 1s 511ms/step\n",
      "1/1 [==============================] - 1s 535ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 486ms/step\n",
      "1/1 [==============================] - 0s 481ms/step\n",
      "1/1 [==============================] - 0s 489ms/step\n",
      "1/1 [==============================] - 0s 487ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 493ms/step\n",
      "1/1 [==============================] - 0s 486ms/step\n",
      "1/1 [==============================] - 0s 494ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 497ms/step\n",
      "1/1 [==============================] - 0s 485ms/step\n",
      "1/1 [==============================] - 0s 489ms/step\n",
      "1/1 [==============================] - 0s 489ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 486ms/step\n",
      "1/1 [==============================] - 0s 486ms/step\n",
      "1/1 [==============================] - 0s 494ms/step\n",
      "1/1 [==============================] - 0s 493ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 487ms/step\n",
      "1/1 [==============================] - 0s 497ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 498ms/step\n",
      "1/1 [==============================] - 0s 488ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 489ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 493ms/step\n",
      "1/1 [==============================] - 1s 512ms/step\n",
      "1/1 [==============================] - 0s 496ms/step\n",
      "1/1 [==============================] - 1s 501ms/step\n",
      "1/1 [==============================] - 1s 503ms/step\n",
      "1/1 [==============================] - 0s 493ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 498ms/step\n",
      "1/1 [==============================] - 1s 512ms/step\n",
      "1/1 [==============================] - 1s 562ms/step\n",
      "1/1 [==============================] - 1s 604ms/step\n",
      "1/1 [==============================] - 1s 596ms/step\n",
      "1/1 [==============================] - 0s 496ms/step\n",
      "1/1 [==============================] - 1s 534ms/step\n",
      "1/1 [==============================] - 1s 531ms/step\n",
      "1/1 [==============================] - 1s 569ms/step\n",
      "1/1 [==============================] - 1s 549ms/step\n",
      "1/1 [==============================] - 1s 546ms/step\n",
      "1/1 [==============================] - 1s 548ms/step\n",
      "1/1 [==============================] - 1s 510ms/step\n",
      "1/1 [==============================] - 1s 514ms/step\n",
      "1/1 [==============================] - 1s 506ms/step\n",
      "1/1 [==============================] - 1s 535ms/step\n",
      "1/1 [==============================] - 1s 526ms/step\n",
      "1/1 [==============================] - 1s 590ms/step\n",
      "1/1 [==============================] - 1s 618ms/step\n",
      "1/1 [==============================] - 1s 514ms/step\n",
      "1/1 [==============================] - 1s 513ms/step\n",
      "1/1 [==============================] - 0s 483ms/step\n",
      "1/1 [==============================] - 0s 481ms/step\n",
      "1/1 [==============================] - 1s 510ms/step\n",
      "1/1 [==============================] - 0s 485ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 484ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 487ms/step\n",
      "1/1 [==============================] - 0s 500ms/step\n",
      "1/1 [==============================] - 1s 514ms/step\n",
      "1/1 [==============================] - 1s 512ms/step\n",
      "1/1 [==============================] - 0s 499ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 487ms/step\n",
      "1/1 [==============================] - 1s 504ms/step\n",
      "1/1 [==============================] - 0s 496ms/step\n",
      "1/1 [==============================] - 1s 538ms/step\n",
      "1/1 [==============================] - 1s 528ms/step\n",
      "1/1 [==============================] - 1s 546ms/step\n",
      "1/1 [==============================] - 1s 586ms/step\n",
      "1/1 [==============================] - 1s 512ms/step\n",
      "1/1 [==============================] - 1s 500ms/step\n",
      "Found 1000 images belonging to 2 classes.\n",
      "1/1 [==============================] - 0s 498ms/step\n",
      "1/1 [==============================] - 1s 519ms/step\n",
      "1/1 [==============================] - 0s 495ms/step\n",
      "1/1 [==============================] - 1s 521ms/step\n",
      "1/1 [==============================] - 1s 564ms/step\n",
      "1/1 [==============================] - 1s 506ms/step\n",
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      "1/1 [==============================] - 1s 520ms/step\n",
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      "1/1 [==============================] - 1s 510ms/step\n",
      "1/1 [==============================] - 1s 510ms/step\n",
      "1/1 [==============================] - 0s 499ms/step\n",
      "1/1 [==============================] - 1s 514ms/step\n",
      "1/1 [==============================] - 0s 495ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 500ms/step\n",
      "1/1 [==============================] - 1s 521ms/step\n",
      "1/1 [==============================] - 1s 519ms/step\n",
      "1/1 [==============================] - 1s 529ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 496ms/step\n",
      "1/1 [==============================] - 0s 494ms/step\n",
      "1/1 [==============================] - 0s 488ms/step\n",
      "1/1 [==============================] - 1s 500ms/step\n",
      "1/1 [==============================] - 0s 496ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 489ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 484ms/step\n",
      "1/1 [==============================] - 0s 489ms/step\n",
      "1/1 [==============================] - 1s 507ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 1s 504ms/step\n",
      "1/1 [==============================] - 1s 504ms/step\n",
      "1/1 [==============================] - 1s 507ms/step\n",
      "1/1 [==============================] - 0s 499ms/step\n",
      "1/1 [==============================] - 0s 498ms/step\n",
      "1/1 [==============================] - 0s 497ms/step\n",
      "1/1 [==============================] - 1s 501ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 498ms/step\n",
      "1/1 [==============================] - 0s 499ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 489ms/step\n",
      "1/1 [==============================] - 1s 521ms/step\n",
      "1/1 [==============================] - 0s 496ms/step\n",
      "1/1 [==============================] - 1s 519ms/step\n",
      "1/1 [==============================] - 1s 513ms/step\n",
      "1/1 [==============================] - 1s 560ms/step\n",
      "1/1 [==============================] - 1s 506ms/step\n",
      "Found 1000 images belonging to 2 classes.\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 493ms/step\n",
      "1/1 [==============================] - 0s 487ms/step\n",
      "1/1 [==============================] - 0s 498ms/step\n",
      "1/1 [==============================] - 0s 494ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 484ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 1s 564ms/step\n",
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      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 490ms/step\n",
      "1/1 [==============================] - 0s 493ms/step\n",
      "1/1 [==============================] - 1s 501ms/step\n",
      "1/1 [==============================] - 0s 494ms/step\n",
      "1/1 [==============================] - 0s 485ms/step\n",
      "1/1 [==============================] - 1s 503ms/step\n",
      "1/1 [==============================] - 1s 515ms/step\n",
      "1/1 [==============================] - 0s 494ms/step\n",
      "1/1 [==============================] - 1s 583ms/step\n",
      "1/1 [==============================] - 1s 512ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 1s 548ms/step\n",
      "1/1 [==============================] - 1s 502ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 1s 508ms/step\n",
      "1/1 [==============================] - 1s 573ms/step\n",
      "1/1 [==============================] - 1s 584ms/step\n",
      "1/1 [==============================] - 1s 534ms/step\n",
      "1/1 [==============================] - 0s 487ms/step\n",
      "1/1 [==============================] - 0s 486ms/step\n",
      "1/1 [==============================] - 0s 486ms/step\n",
      "1/1 [==============================] - 0s 495ms/step\n",
      "1/1 [==============================] - 0s 496ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 488ms/step\n",
      "1/1 [==============================] - 0s 491ms/step\n",
      "1/1 [==============================] - 0s 492ms/step\n",
      "1/1 [==============================] - 0s 488ms/step\n",
      "1/1 [==============================] - 1s 513ms/step\n",
      "1/1 [==============================] - 1s 534ms/step\n",
      "1/1 [==============================] - 0s 499ms/step\n",
      "1/1 [==============================] - 0s 500ms/step\n",
      "1/1 [==============================] - 1s 504ms/step\n",
      "1/1 [==============================] - 1s 505ms/step\n",
      "1/1 [==============================] - 0s 498ms/step\n",
      "1/1 [==============================] - 1s 500ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.RMSprop.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 2s 16ms/step - loss: 0.7897 - acc: 0.7365 - val_loss: 0.5031 - val_acc: 0.7480\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.3506 - acc: 0.8465 - val_loss: 0.2704 - val_acc: 0.8960\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.2735 - acc: 0.8935 - val_loss: 0.3524 - val_acc: 0.8430\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.2567 - acc: 0.8915 - val_loss: 0.2459 - val_acc: 0.9010\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.2183 - acc: 0.9080 - val_loss: 0.2462 - val_acc: 0.9100\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.1783 - acc: 0.9300 - val_loss: 0.2658 - val_acc: 0.8980\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.1537 - acc: 0.9425 - val_loss: 0.4373 - val_acc: 0.8670\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.1503 - acc: 0.9385 - val_loss: 0.3016 - val_acc: 0.8990\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.1224 - acc: 0.9490 - val_loss: 0.2848 - val_acc: 0.9030\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.1292 - acc: 0.9505 - val_loss: 0.3184 - val_acc: 0.8990\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.1001 - acc: 0.9570 - val_loss: 0.3480 - val_acc: 0.8970\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.1038 - acc: 0.9565 - val_loss: 0.7394 - val_acc: 0.8360\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0875 - acc: 0.9670 - val_loss: 0.3563 - val_acc: 0.9060\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0680 - acc: 0.9705 - val_loss: 0.7851 - val_acc: 0.8450\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0729 - acc: 0.9705 - val_loss: 0.3632 - val_acc: 0.9070\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0560 - acc: 0.9770 - val_loss: 0.3785 - val_acc: 0.9070\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0488 - acc: 0.9815 - val_loss: 0.4685 - val_acc: 0.8960\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0395 - acc: 0.9865 - val_loss: 0.5390 - val_acc: 0.8970\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0470 - acc: 0.9830 - val_loss: 0.4824 - val_acc: 0.8890\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0493 - acc: 0.9835 - val_loss: 0.4847 - val_acc: 0.8970\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0351 - acc: 0.9885 - val_loss: 0.6220 - val_acc: 0.8910\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0333 - acc: 0.9875 - val_loss: 0.5566 - val_acc: 0.8880\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0347 - acc: 0.9875 - val_loss: 0.4967 - val_acc: 0.9010\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0241 - acc: 0.9915 - val_loss: 0.5566 - val_acc: 0.8990\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0366 - acc: 0.9895 - val_loss: 0.5735 - val_acc: 0.8920\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0336 - acc: 0.9865 - val_loss: 0.5464 - val_acc: 0.9030\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0254 - acc: 0.9905 - val_loss: 0.8065 - val_acc: 0.8810\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0213 - acc: 0.9910 - val_loss: 0.7098 - val_acc: 0.8970\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0267 - acc: 0.9905 - val_loss: 0.6133 - val_acc: 0.8990\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 1s 14ms/step - loss: 0.0261 - acc: 0.9920 - val_loss: 0.6222 - val_acc: 0.8950\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras import optimizers\n",
    "import numpy as np\n",
    "\n",
    "train_dir = 'D:\\\\TrainData\\\\dog_cat_small\\\\train'\n",
    "validation_dir = 'D:\\\\TrainData\\\\dog_cat_small\\\\validation'\n",
    "test_dir = 'D:\\\\TrainData\\\\dog_cat_small\\\\test'\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1./255) \n",
    "batch_size = 20\n",
    "\n",
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, 4, 4, 512))\n",
    "    labels = np.zeros(shape=(sample_count))\n",
    "    generator = datagen.flow_from_directory(directory, target_size=(150, 150), batch_size=batch_size, class_mode='binary')\n",
    "    i=0\n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        # 使用预训练的卷积基提取特征\n",
    "        # (None, 4, 4, 512)\n",
    "        features_batch = conv_base.predict(inputs_batch)\n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            break\n",
    "    return features,labels\n",
    "\n",
    "train_features, train_labels = extract_features(train_dir, 2000)\n",
    "validation_features, validation_labels = extract_features(validation_dir, 1000) \n",
    "test_features, test_labels = extract_features(test_dir, 1000)\n",
    "#提取的特征形状为 (samples, 4, 4, 512)。我们要将其输入到密集连接分类器中，\n",
    "#所以首先必须将其形状展平为 (samples, 8192)\n",
    "train_features = np.reshape(train_features, (2000, 4 * 4 * 512))\n",
    "validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))\n",
    "test_features = np.reshape(test_features, (1000, 4 * 4 * 512))\n",
    "\n",
    "from tensorflow.keras import models\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=2e-5),\n",
    " loss='binary_crossentropy',\n",
    " metrics=['acc'])\n",
    "history = model.fit(train_features, train_labels,\n",
    " epochs=30,\n",
    " batch_size=20,\n",
    " validation_data=(validation_features, validation_labels))\n",
    "\n",
    "model.save('cats_and_dogs_small_3.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
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