03_CNN_use_pretrained_VGG16.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 사전 훈련된 VGG16을 이용한 특성추출"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![swapping FC classifiers](https://s3.amazonaws.com/book.keras.io/img/ch5/swapping_fc_classifier.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf; print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2.2.4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keras\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet',\n",
    "                 include_top=False,\n",
    "                 input_shape=(150,150,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\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",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 증식을 사용하지 않는 빠른 특성 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "base_dir = \"datasets/cats_and_dogs_small\"\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "test_dir = os.path.join(base_dir, '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(\n",
    "        directory,\n",
    "        target_size=(150,150),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='binary')\n",
    "    i = 0 \n",
    "    for inputs_batch, labels_batch in generator:\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2000 samples, validate on 1000 samples\n",
      "Epoch 1/30\n",
      "2000/2000 [==============================] - 5s 3ms/step - loss: 0.6221 - acc: 0.6530 - val_loss: 0.4380 - val_acc: 0.8300\n",
      "Epoch 2/30\n",
      "2000/2000 [==============================] - 5s 3ms/step - loss: 0.4324 - acc: 0.7990 - val_loss: 0.3569 - val_acc: 0.8600\n",
      "Epoch 3/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.3662 - acc: 0.8455 - val_loss: 0.3228 - val_acc: 0.8680\n",
      "Epoch 4/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.3195 - acc: 0.8625 - val_loss: 0.3039 - val_acc: 0.8940\n",
      "Epoch 5/30\n",
      "2000/2000 [==============================] - 5s 3ms/step - loss: 0.2994 - acc: 0.8745 - val_loss: 0.2825 - val_acc: 0.8860\n",
      "Epoch 6/30\n",
      "2000/2000 [==============================] - 5s 3ms/step - loss: 0.2673 - acc: 0.8930 - val_loss: 0.2727 - val_acc: 0.8810 lo\n",
      "Epoch 7/30\n",
      "2000/2000 [==============================] - 5s 3ms/step - loss: 0.2537 - acc: 0.8940 - val_loss: 0.2669 - val_acc: 0.8870\n",
      "Epoch 8/30\n",
      "2000/2000 [==============================] - 5s 2ms/step - loss: 0.2441 - acc: 0.9035 - val_loss: 0.2577 - val_acc: 0.8970\n",
      "Epoch 9/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.2236 - acc: 0.9190 - val_loss: 0.2540 - val_acc: 0.8930\n",
      "Epoch 10/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.2017 - acc: 0.9255 - val_loss: 0.2509 - val_acc: 0.8950\n",
      "Epoch 11/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1923 - acc: 0.9290 - val_loss: 0.2510 - val_acc: 0.8960\n",
      "Epoch 12/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1917 - acc: 0.9250 - val_loss: 0.2447 - val_acc: 0.8970\n",
      "Epoch 13/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1804 - acc: 0.9380 - val_loss: 0.2428 - val_acc: 0.9010\n",
      "Epoch 14/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1691 - acc: 0.9390 - val_loss: 0.2628 - val_acc: 0.8870\n",
      "Epoch 15/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1682 - acc: 0.9405 - val_loss: 0.2400 - val_acc: 0.8980\n",
      "Epoch 16/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1611 - acc: 0.9455 - val_loss: 0.2409 - val_acc: 0.9010\n",
      "Epoch 17/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1492 - acc: 0.9500 - val_loss: 0.2381 - val_acc: 0.8990\n",
      "Epoch 18/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1393 - acc: 0.9505 - val_loss: 0.2408 - val_acc: 0.9010\n",
      "Epoch 19/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1377 - acc: 0.9520 - val_loss: 0.2500 - val_acc: 0.8980\n",
      "Epoch 20/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1241 - acc: 0.9575 - val_loss: 0.2393 - val_acc: 0.9010\n",
      "Epoch 21/30\n",
      "2000/2000 [==============================] - 5s 2ms/step - loss: 0.1255 - acc: 0.9560 - val_loss: 0.2375 - val_acc: 0.9010\n",
      "Epoch 22/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1153 - acc: 0.9675 - val_loss: 0.2372 - val_acc: 0.9010\n",
      "Epoch 23/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1153 - acc: 0.9625 - val_loss: 0.2370 - val_acc: 0.9010\n",
      "Epoch 24/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1074 - acc: 0.9665 - val_loss: 0.2435 - val_acc: 0.9010\n",
      "Epoch 25/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.1076 - acc: 0.9630 - val_loss: 0.2371 - val_acc: 0.9010\n",
      "Epoch 26/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.0997 - acc: 0.9670 - val_loss: 0.2401 - val_acc: 0.9010\n",
      "Epoch 27/30\n",
      "2000/2000 [==============================] - 4s 2ms/step - loss: 0.0968 - acc: 0.9695 - val_loss: 0.2484 - val_acc: 0.8980\n",
      "Epoch 28/30\n",
      "2000/2000 [==============================] - 5s 2ms/step - loss: 0.0933 - acc: 0.9705 - val_loss: 0.2385 - val_acc: 0.9030\n",
      "Epoch 29/30\n",
      "2000/2000 [==============================] - 5s 2ms/step - loss: 0.0886 - acc: 0.9755 - val_loss: 0.2524 - val_acc: 0.8980\n",
      "Epoch 30/30\n",
      "2000/2000 [==============================] - 5s 3ms/step - loss: 0.0893 - acc: 0.9695 - val_loss: 0.2399 - val_acc: 0.9040\n"
     ]
    }
   ],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "from keras import optimizers\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",
    "\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['acc'])\n",
    "\n",
    "history = model.fit(train_features, train_labels,\n",
    "                    epochs=30,\n",
    "                    batch_size=20,\n",
    "                    validation_data=(validation_features, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wXxWlJZoHnB99PhfoamY9ovn9zKzSzF4xs3NSfYGZTYjyVFZXV8coUtsaN24c06ZNq5c2bdo0xo0bF2v9ww8/nMcee6zF33/fffdRk3CH7emnn+aggw5q8fYkMzI9rnprDBuQLrWV5784QT/VoCzJA8B8EzjJzF4DTgLeBWoHlOnn7uXAxcB9ZnZEg425T3X3cncv79WrV/zSt5ELLriAP/7xj3z44YcArFixglWrVjF69Gi2bt3KKaecQllZGUOGDOH3v/99g/VXrFhBSUkJEIZIGDt2LKWlpVx00UV1Qx8AXHPNNXXDMk+aNAmAH/3oR6xatYpPfepTfOpTnwKguLiYdevWAfDDH/6QkpISSkpK6oZlXrFiBYMGDeKqq65i8ODBnHbaafW+p9Yf/vAHjjvuOIYPH86pp57KmjVrANi6dSuXX345Q4YMobS0tG4Yh2eeeYaysjKGDh3KKaeckpFjm6vSGSAsboBujWEDRBpo7qcAMZp3kvIfAFQ1suzXwAVNfV9zzTvXX+9+0kmZna6/vvmfTmeeeaY/9dRT7h6GN/7mN7/p7uEJ2U2bNrm7e3V1tR9xxBG+e/dud9/TvLN8+XIfPHiwu7vfe++9fvnll7u7+7x587yoqKiuead2SOOdO3f6SSed5PPmzXN39/79+3t1dXVdWWrnKysrvaSkxLdu3epbtmzxY445xl999VVfvny5FxUV1Q25fOGFF/pDDz3UYJ82bNhQV9af//znfuONN7q7+0033eTXJxyUDRs2+Nq1a71Pnz6+bNmyemVNVijNO63R/z3bD1JJbiODzTuzgYFmNsDM9gHGAvV64ZhZTzOr3dbNwC+j9O5mtm9tHmAU0HCEsByQ2MST2LTj7txyyy2UlpZy6qmn8u6779bVmFN5+eWXueSSSwAoLS2ltLS0btn06dMpKytj+PDhLFiwIOVgaolmzZrFueeeS5cuXTjggAM477zz+Nvf/gbAgAEDGDZsGND48M1VVVWcfvrpDBkyhLvvvpsFCxYA8Nxzz/G1r32tLl/37t155ZVXOPHEExkwYACQ38MvZ3oEx7j9ytPtf66mGGmJjs1lcPedZnYtMJPQk+eX7r7AzO4gXFlmACcDd5mZAy8DtRFjEPAzM9tNaEr6nrvvVdCPWjDa3DnnnMONN97Iq6++yvbt2ykrKwPCAGbV1dXMmTOHTp06UVxcnHI45USphjFevnw599xzD7Nnz6Z79+5cdtllzW4nXNxTqx2WGcLQzKmad6677jpuvPFGzjrrLF588UVuv/32uu0mlzFVWntQUREeHlq5MjSDTJ6cOvilk2/ChD0PKdU220D9/P36hWXJmhpXPU5QjptPpKVi9dN396fd/Uh3P8LdJ0dp/xUFfNz9MXcfGOW50t0/jNL/4e5D3H1o9PfB1tuV1nXAAQdw8sknc8UVV9S7gbtp0yYOOeQQOnXqxAsvvMDbqSJBghNPPLHu5edvvPEG8+fPB8KwzF26dOHAAw9kzZo1/PnPf65bp2vXrmzZsiXltp566ilqamrYtm0bTz75JCeccELsfdq0aRO9e4d78r/5zW/q0k877TR+/OMf181v3LiR448/npdeeonly5cD7WP45bjt6um0v7fGCI4i7YmeyE3DuHHjmDdvXt2bqwDGjx9PZWUl5eXlVFRUcPTRRze5jWuuuYatW7dSWlrKD37wA0aOHAmEt2ANHz6cwYMHc8UVV9QblnnChAmcccYZdTdya5WVlXHZZZcxcuRIjjvuOK688kqGDx8ee39uv/12LrzwQk444QR69uxZl37bbbexceNGSkpKGDp0KC+88AK9evVi6tSpnHfeeQwdOpSLLroo9ve0lrgBujXeoKShACRXWVNNBNlQXl7ulZWV9dLefPNNBg0alKUSSTra8lx16BBq7snMQjt3uvkgtOGn+rHWv39oNxdpr8xsjoeekk1STV9ylrpCiqRPQV9yVtwA3VpvUBLJRTkT9NtbM5Q01Nw5ivsEa1zqCimSvpxo01++fDldu3alR48e7bLboISAv379erZs2VLXlz9RcldICLVt1aJFMiNum35OBP0dO3ZQVVXVbL91aXvbtsHGjbBjB7z77n507dqHsWM7NciXzg3SuH3qRWSPuEG/2Yez2oNOnTqlrD1KdjVWe9+1q2GQjtsVMu7DUSLSMjlR05f2KZ3ae9y86jIp0jLqsimtLp3xZ+L2oGmtV/aJSKCgLyll+v2rcXvQ6JV9Iq1LQV8aaI33r0K8rpB6OEqkdSnoSwNxx6ppjQeZ9HCUSOvSjVxpIJ2xakSkfdCNXGkxtauL5C8FfWlA7eoi+UtBXxpQu7pI/sqJJ3Kl7em1fSL5STV9EZECoqBfQDI9tLGI5B417xQIDWQmIhCzpm9mY8xskZktNbOJKZb3N7O/mtl8M3vRzPokLLvUzJZE06WZLLzEl87LwUWkabt2wc6dmdmWO7z2GnzrWzCxQXTNvGaDvpkVAQ8AZwDHAOPM7JikbPcAv3X3UuAO4K5o3YOBScBxwEhgkpl1z1zxJS4NZCayd6qr4aGHYOxY6NkTDj4Yzj8fHnwQVq9Of3tLlsAdd8CgQVBWBvfdB++8k/lyJ4vTvDMSWOruywDMbBpwNrAwIc8xwA3R5xeAp6LPpwPPuvuGaN1ngTHAo3tfdElHv36phyzOxQeu3OGtt2DWLPj732HhQrj+evjCF1r/uzdtggUL4PXX90xbt4bjWDv17bvn86GHhnsoqfZh/fpw0U01AZSUhGnIkDAdckh65XzjjfrlXLs2M8cgF3XvXv9YDhkSAndTamvgf/oTPP00/OtfIe2QQ+Ccc6BTp5D+xBMhf1kZfPazcOaZcOyxUFTUcJurVsHvfgePPAKVlaFL9EknwY03hgtIjx6Z3/dkcYJ+byDx+lNFqLknmgecD9wPnAt0NbMejazbu8WllRabPDn1C09y4YGrHTtg3rwQ5GunNWvCsoMOCv9RLroI5s8PNadUQTZdH30EixbVD5qvv17/l1HXriGQfOxjsHQp/PWvsGVL/e106gR9+oQLwGGHwbp1YRvvvAPbt9fPu//+ey4WO3fCjBmhFlnrkEPqB60hQ2DgwLC95HIm1hi7dQvlHDo0BJlCtGYNPPkk/OIXe9IOPTQcw8SLQb9+oSJRG+hra/DHHguTJoWAPmLEnn9j7uF4/+lPYZo8Gb7znXBBOeOMkP8//gOeey4E+hdfDOuUlcE994R/t336NChuq2p27B0zuxA43d2vjOa/CIx09+sS8hwO/BgYALxMuAAMBiYA+7r7d6N8/wnUuPu9Sd8xIcpLv379RrydqkoqjYr7esGKCrjpplDb6N0bvv/97N/E3bkTNm8ONdNNm+p/XrIkBPhXXtlzsSouhtGj90yDBoWLwte+FgLkWWeFn+DdurWsPLt2wX//N/znf4YaPEDHjnD00fWDbUnJnofXEm3aVL/G/s47ez6vWhWCQeKvgsSpR4+G21uzpmFAX7Cg4QUDwgUmuZxDhoRfHoUa7BO5w3vv7TmOtb+EFiyA5DexdusGp58eau5jxoQLexwbNsDMmeEC8Mwz4ddcrYED4eKLYdw4OOqozO1XrYy9I9fMjgdud/fTo/mbAdz9rkbyHwD8n7v3MbNxwMnu/pVo2c+AF9290eYdDbgWpBPI47xw/P334dvfhh//eM8NqCOOCIFz1Kjw9+ij0w8Ou3eH/0jvvVc/YMedtm1rfNsdOoTaaW2AHzUqXKxScYcHHoCvfz38h5oxI+xfOubPhyuvhNmzQw1t/PgQNI86CvbZJ71ttaZdu2D58hCwliwJQX3IEDjyyPZVzlyxa1doLnz99XBcjz0WPvnJcBHd2+3++9+hWWj06PALoTUvvpkM+h2BxcApwLvAbOBid1+QkKcnsMHdd5vZZGCXu/9XdCN3DlAWZX0VGFHbxp9Krgb9OXPgH/+AL30JDjxw77YVN5BD868X3LULfvUruOWW0LQwYUKobVRW7mkqqa4O6/TosecCMGpU+Ef60Uf1a6vJU1VVqGk3Zr/9wvFInrp1S52eOB12WGhCScfzz8OFF4aLwPTpcOqpza/zwQfw3e+GXz7du8OPfhR+dqt2LLkkbtDH3ZudgDMJgf8t4NYo7Q7grOjzBcCSKM8vCE06teteASyNpsub+64RI0Z4rnn/fffevd3BvWtX95tucl+1quXb698/bCt56t+/YV6z1HnN3P/+d/cRI8L86NHur77acP3du90XLXJ/8EH3yy93P/LIPdsoKmq43aIi9379wvYuvth94kT3n/zE/Ykn3J9/3r2y0n3JEve1a90//LDlx2BvvPWW++DBoaz33x/2sTEvv+x+1FFh3770Jfd169qunCKZBFR6nHgeJ1NbTrkY9K+80r1DB/ff/MZ97NjweZ993K+6yn3x4vS311QgT9bYBaJLl/C3d2/3Rx5pOvAle++9EMRvucX9+993f/TRcAFZudJ9x4709ycbNm92P/vscAyuuML9gw/qL9+0yf2aa/ZcTJ95JivFFMkYBf028pe/hKP4rW/tSVu61P3qq9333TcE6gsvDDXguNKp6T/8sHvnzg3zduzofuut7lu27O0e5q5du9xvuy0cj09+0n316pA+Y0a4GJq5f/3rhX2MJH8o6LeBzZtDID7ySPeamobL33vP/eab3Q88MBzpU091f/bZ5mvdqQJ5584hPZXf/ta9V689eUeMCE0cEkyf7r7//u59+rife244RiUl7q+8ku2SiWSOgn4b+OpXQ21x1qym823a5P6DH7gfeuieoHzffaHtuzEPPxwuKGbhb3LA373b/V//CjXVww4L2x00KPzykIZefdW9b9/Q7Pad72TvfoNIa4kb9PWO3BZ66SU4+eTwJOh998Vb54MPQh/y++8PfYMhdLM788zQH/jEE5vvcvfmm/Doo+FBj7feCvk/+9nQ97f2KUFJbdOm8PBUWz8MI9IWMtZls63lQtCvqYHS0tCYMn8+dOmS/jaWLQtP/P3pT/DCC/Dhh3DAAaGLYe2j3IcfHvK+8w5MmxYC/dy5of/6pz8dul6ee254KlVEClvcoK+hlVvg1ltDLfuFF1oW8AE+/nG49towbdsW+pfXPsr9VDRy0fDhYfuzZoX5kSPDr4ovfCH0YRcRSZdq+mn6xz/Cw0tXXw0/+Unmt+8eHg+vvQBs2QIXXBBG9vvEJzL/fSKSH9S80wq2bw+17+3bQ2BO92lRiD+8gohIOtS80wq+/e0w8uLMmS0P+Hp7lYhkk96RG9Ps2XD33fDlL8Npp7VsG3p7lYhkm4J+DB9+CJdfHm6e3ntv8/kbo7dXiUi2KejHMHly6Ff/s5/t3Qiajb2lKhffXiUiuUlBvxlz58Jdd8EXvxj6z++NyZPDEMmJcuXtVSKSHxT0m7BwYRgfv0eP+E/dNmX8+DAmfu0bl/r3Tz1GvohIa1HvnRT++U/43vfC25f23x8eeyy8+T4Txo9XkBeR7FFNP+IehkU46aTwqrRZs8KLkFeuDEMiNKWiIrzBqkOH8Leioi1KLCKSvoKv6e/cGV6r9/3vh3F0+vYNTTlf/nIYC6c56nsvIrmkYGv6NTXhRdoDB4bgvHMn/PrXsHRpGDkzTsAH9b0XkdxSkDX9P/wh1OSrq+H448NQx5/7XGieSZf63otILim4oL9pUwj4hx4Kjz8eBk8za/n2+vULTTqp0kVE2puCa965805Ytw5+9Ss44YS9C/igvvciklsKKui/9Va4SXvZZTBiRGa2qb73IpJLYgV9MxtjZovMbKmZTUyxvJ+ZvWBmr5nZfDM7M0ovNrPtZjY3mv4n0zuQjptuCq8TzHQtfPx4WLECdu8OfxXwRaS9arZN38yKgAeAzwBVwGwzm+HuCxOy3QZMd/efmtkxwNNAcbTsLXcfltlip+/FF+GJJ0LA11unRKRQxanpjwSWuvsyd/8ImAacnZTHgW7R5wOBVZkr4t7btQu+/vXQ9HLDDdkujYhI9sQJ+r2BdxLmq6K0RLcDl5hZFaGWf13CsgFRs89LZnZCqi8wswlmVmlmldXV1fFLH9OvfgXz5sEPfhCGVYhLT9qKSL6JE/RT9W9JfsfiOODX7t4HOBN4yMw6AKuT5N6aAAANqklEQVSBfu4+HLgReMTMuiWti7tPdfdydy/v1atXenvQjM2bw4NSo0bBhRfGX6/2Sdu33w5DNNQ+aavALyK5LE7QrwL6Jsz3oWHzzZeB6QDu/k9gP6Cnu3/o7uuj9DnAW8CRe1vodNx5J6xdG3rtpNM9U0/aikg+ihP0ZwMDzWyAme0DjAVmJOVZCZwCYGaDCEG/2sx6RTeCMbOPAwOBZZkqfHOWLYMpU+DSS6G82dcF16cnbUUkHzUb9N19J3AtMBN4k9BLZ4GZ3WFmZ0XZvgFcZWbzgEeBy9zdgROB+VH6Y8DV7r6hNXYklZtugo4dQ20/XXrLlYjko1jDMLj704QbtIlp/5XweSEwKsV6jwOP72UZW+Sll8IwC9/5Dhx+ePrrT55cf/RM0JO2IpL78vKJ3Noumn37wje+0bJt6ElbEclHeTng2q9/Hd5t++ij6XXRTKa3XIlIvsm7mn5tF81PfhIuuijbpRERaV/yrqZ/112wZk0YM39vR9AUEck3eVXTX74cfvhD+NKX4Nhjs10aEZH2J6+C/t500RQRKQR5E/QXLw5dNCdOhN7JIwOJiAiQR236Rx4Js2fDoEHZLomISPuVN0EfMvc2LBGRfJU3zTsiItI8BX0RkQKioC8iUkAU9EVECoiCvohIAVHQFxEpIAr6IiIFREFfRKSAKOiLiBQQBX0RkQKioC8iUkAU9EVECoiCvohIAYkV9M1sjJktMrOlZjYxxfJ+ZvaCmb1mZvPN7MyEZTdH6y0ys9MzWfiWqKiA4mLo0CH8rajIdolERNpOs0Mrm1kR8ADwGaAKmG1mM9x9YUK224Dp7v5TMzsGeBoojj6PBQYDhwPPmdmR7r4r0zsSR0UFTJgANTVh/u23wzzA+PHZKJGISNuKU9MfCSx192Xu/hEwDTg7KY8D3aLPBwKros9nA9Pc/UN3Xw4sjbaXFbfeuifg16qpCekiIoUgTtDvDbyTMF8VpSW6HbjEzKoItfzr0lgXM5tgZpVmVlldXR2z6OlbuTK9dBGRfBMn6FuKNE+aHwf82t37AGcCD5lZh5jr4u5T3b3c3ct79eoVo0gt069feukiIvkmTtCvAvomzPdhT/NNrS8D0wHc/Z/AfkDPmOu2mcmToXPn+mmdO4d0EZFCECfozwYGmtkAM9uHcGN2RlKelcApAGY2iBD0q6N8Y81sXzMbAAwE/p2pwqdr/HiYOhX69wez8HfqVN3EFZHC0WzvHXffaWbXAjOBIuCX7r7AzO4AKt19BvAN4OdmdgOh+eYyd3dggZlNBxYCO4GvZavnTq3x4xXkRaRwWYjN7Ud5eblXVlZmuxgiIjnFzOa4e3lz+fRErohIAVHQFxEpIAr6IiIFREFfRKSAKOiLiBQQBX0RkQKioC8iUkAU9EVECoiCvohIAVHQFxEpIAr6IiIFREFfRKSAKOiLiBQQBX0RkQKioC8iUkAU9EVECoiCvohIAVHQFxEpIAr6IiIFREFfRKSAKOiLiBSQWEHfzMaY2SIzW2pmE1Msn2Jmc6NpsZm9n7BsV8KyGZksvIiIpKdjcxnMrAh4APgMUAXMNrMZ7r6wNo+735CQ/zpgeMImtrv7sMwVWUREWipOTX8ksNTdl7n7R8A04Owm8o8DHs1E4UREJLPiBP3ewDsJ81VRWgNm1h8YADyfkLyfmVWa2Stmdk4j602I8lRWV1fHLLqIiKQrTtC3FGneSN6xwGPuvishrZ+7lwMXA/eZ2RENNuY+1d3L3b28V69eMYokIiItESfoVwF9E+b7AKsayTuWpKYdd18V/V0GvEj99n4REWlDcYL+bGCgmQ0ws30Igb1BLxwzOwroDvwzIa27me0bfe4JjAIWJq8rIiJto9neO+6+08yuBWYCRcAv3X2Bmd0BVLp77QVgHDDN3RObfgYBPzOz3YQLzPcSe/2IiEjbsvoxOvvKy8u9srIy28UQEckpZjYnun/aJD2RKyJSQBT0RUQKiIK+iEgBUdAXESkgCvoiIgVEQV9EpIAo6IuIFBAFfRGRAqKgLyJSQBT0RUQKiIK+iEgBUdAXESkgCvoiIgVEQV9EpIAo6IuIFBAFfRGRAqKgLyJSQBT0RUQKiIK+iEgBUdAXESkgCvoiIgVEQV9EpIDECvpmNsbMFpnZUjObmGL5FDObG02Lzez9hGWXmtmSaLo0k4UXEZH0dGwug5kVAQ8AnwGqgNlmNsPdF9bmcfcbEvJfBwyPPh8MTALKAQfmROtuzOheiIhILHFq+iOBpe6+zN0/AqYBZzeRfxzwaPT5dOBZd98QBfpngTF7U2AREWm5OEG/N/BOwnxVlNaAmfUHBgDPp7OumU0ws0ozq6yuro5TbhERaYE4Qd9SpHkjeccCj7n7rnTWdfep7l7u7uW9evWKUSQREWmJOEG/CuibMN8HWNVI3rHsadpJd10REWllcYL+bGCgmQ0ws30IgX1GciYzOwroDvwzIXkmcJqZdTez7sBpUZqIiGRBs7133H2nmV1LCNZFwC/dfYGZ3QFUunvtBWAcMM3dPWHdDWb2HcKFA+AOd9+Q2V0QEZG4LCFGtwvl5eVeWVmZ7WKIiOQUM5vj7uXN5dMTuSIiBURBX0SkgCjoi4gUEAV9EZECoqAvIlJAFPRFRAqIgr6ISAFR0BcRKSAK+iIiBURBX0SkgCjoi4gUEAV9EZECoqAvIlJAFPRFRAqIgr6ISAFR0BcRKSAK+iIiBURBX0SkgCjoi4gUEAV9EZECkjdBv6ICiouhQ4fwt6Ii2yUSEWl/Oma7AJlQUQETJkBNTZh/++0wDzB+fPbKJSLS3sSq6ZvZGDNbZGZLzWxiI3m+YGYLzWyBmT2SkL7LzOZG04xMFTzRrbfuCfi1ampCuoiI7NFsTd/MioAHgM8AVcBsM5vh7gsT8gwEbgZGuftGMzskYRPb3X1Yhstdz8qV6aWLiBSqODX9kcBSd1/m7h8B04Czk/JcBTzg7hsB3H1tZovZtH790ksXESlUcYJ+b+CdhPmqKC3RkcCRZvZ3M3vFzMYkLNvPzCqj9HNSfYGZTYjyVFZXV6e1AwCTJ0PnzvXTOncO6SIiskecoG8p0jxpviMwEDgZGAf8wswOipb1c/dy4GLgPjM7osHG3Ke6e7m7l/fq1St24WuNHw9Tp0L//mAW/k6dqpu4IiLJ4vTeqQL6Jsz3AValyPOKu+8AlpvZIsJFYLa7rwJw92Vm9iIwHHhrbwuebPx4BXkRkebEqenPBgaa2QAz2wcYCyT3wnkK+BSAmfUkNPcsM7PuZrZvQvooYCEiIpIVzdb03X2nmV0LzASKgF+6+wIzuwOodPcZ0bLTzGwhsAv4lruvN7NPAj8zs92EC8z3Env9iIhI2zL35Ob57CovL/fKyspsF0NEJKeY2Zzo/mmT8mYYBhERaZ6CvohIAWl3zTtmVg28nZTcE1iXheK0pnzbp3zbH8i/fcq3/YH826e92Z/+7t5sn/d2F/RTMbPKOG1VuSTf9inf9gfyb5/ybX8g//apLfZHzTsiIgVEQV9EpIDkStCfmu0CtIJ826d82x/Iv33Kt/2B/NunVt+fnGjTFxGRzMiVmr6IiGSAgr6ISAFp90E/zqsac4mZrTCz16PXR+bkeBNm9kszW2tmbySkHWxmz5rZkuhv92yWMR2N7M/tZvZuwqs+z8xmGdNlZn3N7AUzezN6hen1UXpOnqcm9idnz5OZ7Wdm/zazedE+fTtKH2Bm/4rO0e+igS4z973tuU0/elXjYhJe1QiMy+VB28xsBVDu7jn7QImZnQhsBX7r7iVR2g+ADe7+veji3N3d/182yxlXI/tzO7DV3e/JZtlayswOAw5z91fNrCswBzgHuIwcPE9N7M8XyNHzZGYGdHH3rWbWCZgFXA/cCDzh7tPM7H+Aee7+00x9b3uv6cd5VaO0MXd/GdiQlHw28Jvo828I/yFzQiP7k9PcfbW7vxp93gK8SXjjXU6epyb2J2d5sDWa7RRNDnwaeCxKz/g5au9BP86rGnONA38xszlmNiHbhcmgj7n7agj/QYFDslyeTLjWzOZHzT850QySipkVE15e9C/y4Dwl7Q/k8HkysyIzmwusBZ4lvGDqfXffGWXJeMxr70E/zqsac80ody8DzgC+FjUtSPvzU+AIYBiwGrg3u8VpGTM7AHgc+Lq7b852efZWiv3J6fPk7rvcfRjhjYQjgUGpsmXyO9t70I/zqsackvD6yLXAk4QTnQ/WRO2ute2va7Ncnr3i7mui/5C7gZ+Tg+cpaid+HKhw9yei5Jw9T6n2Jx/OE4C7vw+8CPwHcJCZ1b7gKuMxr70H/TivaswZZtYlugmFmXUBTgPeaHqtnDEDuDT6fCnw+yyWZa/VBsbIueTYeYpuEj4IvOnuP0xYlJPnqbH9yeXzZGa9zOyg6PP+wKmEexUvABdE2TJ+jtp17x2AqAvWfex5VePkLBepxczs44TaPYRXVT6Si/tjZo8CJxOGgV0DTCK8J3k60A9YCVzo7jlxc7SR/TmZ0GTgwArgK7Vt4bnAzEYDfwNeB3ZHybcQ2sFz7jw1sT/jyNHzZGalhBu1RYQK+HR3vyOKE9OAg4HXgEvc/cOMfW97D/oiIpI57b15R0REMkhBX0SkgCjoi4gUEAV9EZECoqAvIlJAFPRFRAqIgr6ISAH5/xznS4qh/zCHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\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",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\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",
    "\n",
    "plt.figure()\n",
    "\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",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 증식을 사용한 특성 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(conv_base)\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               2097408   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 16,812,353\n",
      "Trainable params: 16,812,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no freezing num :  30\n",
      "yes freezing num :  4\n"
     ]
    }
   ],
   "source": [
    "print(\"no freezing num : \",len(model.trainable_weights))\n",
    "\n",
    "conv_base.trainable = False\n",
    "\n",
    "print(\"yes freezing num : \", len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import optimizers\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest')\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(150,150),\n",
    "    batch_size=20,\n",
    "    class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=20,\n",
    "        class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Epoch 1/30\n",
      " - 451s - loss: 0.6083 - acc: 0.6775 - val_loss: 0.4532 - val_acc: 0.8200\n",
      "Epoch 2/30\n",
      " - 451s - loss: 0.4839 - acc: 0.7775 - val_loss: 0.3793 - val_acc: 0.8560\n",
      "Epoch 3/30\n",
      " - 472s - loss: 0.4365 - acc: 0.8035 - val_loss: 0.3397 - val_acc: 0.8590\n",
      "Epoch 4/30\n",
      " - 426s - loss: 0.4002 - acc: 0.8260 - val_loss: 0.3142 - val_acc: 0.8830\n",
      "Epoch 5/30\n",
      " - 421s - loss: 0.3829 - acc: 0.8300 - val_loss: 0.3066 - val_acc: 0.8710\n",
      "Epoch 6/30\n",
      " - 422s - loss: 0.3707 - acc: 0.8290 - val_loss: 0.2866 - val_acc: 0.8850\n",
      "Epoch 7/30\n",
      " - 419s - loss: 0.3578 - acc: 0.8500 - val_loss: 0.2826 - val_acc: 0.8930\n",
      "Epoch 8/30\n",
      " - 30349s - loss: 0.3529 - acc: 0.8410 - val_loss: 0.2727 - val_acc: 0.8950\n",
      "Epoch 9/30\n",
      " - 437s - loss: 0.3412 - acc: 0.8500 - val_loss: 0.2702 - val_acc: 0.8950\n",
      "Epoch 10/30\n",
      " - 434s - loss: 0.3418 - acc: 0.8480 - val_loss: 0.2642 - val_acc: 0.8950\n",
      "Epoch 11/30\n",
      " - 428s - loss: 0.3426 - acc: 0.8345 - val_loss: 0.2642 - val_acc: 0.9060\n",
      "Epoch 12/30\n",
      " - 427s - loss: 0.3324 - acc: 0.8550 - val_loss: 0.2589 - val_acc: 0.9020\n",
      "Epoch 13/30\n",
      " - 489s - loss: 0.3248 - acc: 0.8610 - val_loss: 0.2570 - val_acc: 0.8990\n",
      "Epoch 14/30\n",
      " - 285s - loss: 0.3231 - acc: 0.8495 - val_loss: 0.2528 - val_acc: 0.9000\n",
      "Epoch 15/30\n",
      " - 278s - loss: 0.3087 - acc: 0.8560 - val_loss: 0.2525 - val_acc: 0.8980\n",
      "Epoch 16/30\n",
      " - 283s - loss: 0.3108 - acc: 0.8570 - val_loss: 0.2624 - val_acc: 0.8930\n",
      "Epoch 17/30\n",
      " - 273s - loss: 0.3218 - acc: 0.8660 - val_loss: 0.2488 - val_acc: 0.9010\n",
      "Epoch 18/30\n",
      " - 281s - loss: 0.3098 - acc: 0.8610 - val_loss: 0.2495 - val_acc: 0.9060\n",
      "Epoch 19/30\n",
      " - 276s - loss: 0.2953 - acc: 0.8750 - val_loss: 0.2666 - val_acc: 0.8910\n",
      "Epoch 20/30\n",
      " - 258s - loss: 0.3103 - acc: 0.8640 - val_loss: 0.2616 - val_acc: 0.8940\n",
      "Epoch 21/30\n",
      " - 257s - loss: 0.2929 - acc: 0.8745 - val_loss: 0.2467 - val_acc: 0.9030\n",
      "Epoch 22/30\n",
      " - 260s - loss: 0.2892 - acc: 0.8830 - val_loss: 0.2441 - val_acc: 0.9060\n",
      "Epoch 23/30\n",
      " - 257s - loss: 0.2783 - acc: 0.8795 - val_loss: 0.2455 - val_acc: 0.9060\n",
      "Epoch 24/30\n",
      " - 256s - loss: 0.3058 - acc: 0.8680 - val_loss: 0.2444 - val_acc: 0.9050\n",
      "Epoch 25/30\n",
      " - 255s - loss: 0.2930 - acc: 0.8730 - val_loss: 0.2423 - val_acc: 0.9030\n",
      "Epoch 26/30\n",
      " - 254s - loss: 0.2732 - acc: 0.8875 - val_loss: 0.2449 - val_acc: 0.9050\n",
      "Epoch 27/30\n",
      " - 254s - loss: 0.2866 - acc: 0.8825 - val_loss: 0.2429 - val_acc: 0.9050\n",
      "Epoch 28/30\n",
      " - 253s - loss: 0.2856 - acc: 0.8795 - val_loss: 0.2443 - val_acc: 0.9050\n",
      "Epoch 29/30\n",
      " - 266s - loss: 0.2852 - acc: 0.8740 - val_loss: 0.2555 - val_acc: 0.8930\n",
      "Epoch 30/30\n",
      " - 256s - loss: 0.2906 - acc: 0.8765 - val_loss: 0.2443 - val_acc: 0.9070\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "             optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "             metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=100,\n",
    "    epochs=30,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=50,\n",
    "    verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\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",
    "\n",
    "plt.figure()\n",
    "\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",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 미세조정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 사전에 훈련된 네트워크 위에 새 네트워크 추가\n",
    "2. 기반 네트워크 동결\n",
    "3. 추가 네트워크 훈련\n",
    "4. 기반 네트워크에서 일부 층 동경 해제\n",
    "5. 동경 해제 층과 새로 추가한 층을 훈련"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![fine-tuning VGG16](https://s3.amazonaws.com/book.keras.io/img/ch5/vgg16_fine_tuning.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\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",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 하위층은 일반적이고 재사용 가능한 특성들을 인코딩하는 반면 상위층은 좀 더 특화된 특성을 인코딩한다.\n",
    "#### 즉 새로운 문제에 재활용하도록 수정이 필요한 것은 상위층이다.\n",
    "\n",
    "#### 위와 더불어 과적합 문제로 인해 최상위 2~3층만 미세 조정하는 것이 좋다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = True\n",
    "\n",
    "set_trainable = False\n",
    "for layer in conv_base.layers:\n",
    "    if layer.name == 'block5_conv1':\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 98s 980ms/step - loss: 0.5383 - acc: 0.7315 - val_loss: 0.3361 - val_acc: 0.8580\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 92s 917ms/step - loss: 0.3725 - acc: 0.8340 - val_loss: 0.2658 - val_acc: 0.8900\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 95s 948ms/step - loss: 0.3070 - acc: 0.8685 - val_loss: 0.2357 - val_acc: 0.8980\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2845 - acc: 0.8810 - val_loss: 0.2012 - val_acc: 0.9110\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.2653 - acc: 0.8860 - val_loss: 0.1997 - val_acc: 0.9150\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.2408 - acc: 0.9045 - val_loss: 0.1894 - val_acc: 0.9240\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 17s 172ms/step - loss: 0.2393 - acc: 0.8985 - val_loss: 0.2118 - val_acc: 0.9130\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.2234 - acc: 0.9025 - val_loss: 0.2115 - val_acc: 0.9160\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.2121 - acc: 0.9115 - val_loss: 0.2210 - val_acc: 0.9050\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.2152 - acc: 0.9185 - val_loss: 0.1702 - val_acc: 0.9290\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.2213 - acc: 0.9090 - val_loss: 0.1671 - val_acc: 0.9290\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.1788 - acc: 0.9290 - val_loss: 0.1811 - val_acc: 0.9270\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.1701 - acc: 0.9330 - val_loss: 0.1862 - val_acc: 0.9220\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 18s 179ms/step - loss: 0.1706 - acc: 0.9335 - val_loss: 0.1610 - val_acc: 0.9260\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.1660 - acc: 0.9285 - val_loss: 0.1779 - val_acc: 0.9310\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.1726 - acc: 0.9315 - val_loss: 0.1680 - val_acc: 0.9300\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.1494 - acc: 0.9440 - val_loss: 0.1524 - val_acc: 0.9330\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.1434 - acc: 0.9465 - val_loss: 0.1589 - val_acc: 0.9340\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 19s 186ms/step - loss: 0.1349 - acc: 0.9500 - val_loss: 0.2091 - val_acc: 0.9190\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.1284 - acc: 0.9530 - val_loss: 0.1546 - val_acc: 0.9330\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 21s 214ms/step - loss: 0.1382 - acc: 0.9515 - val_loss: 0.1582 - val_acc: 0.9270\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.1274 - acc: 0.9535 - val_loss: 0.1513 - val_acc: 0.9340\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.1152 - acc: 0.9570 - val_loss: 0.1585 - val_acc: 0.9360\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 21s 212ms/step - loss: 0.1114 - acc: 0.9555 - val_loss: 0.1544 - val_acc: 0.9410\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.1210 - acc: 0.9510 - val_loss: 0.2459 - val_acc: 0.9090\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 21s 213ms/step - loss: 0.1100 - acc: 0.9590 - val_loss: 0.1511 - val_acc: 0.9400\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 23s 228ms/step - loss: 0.1053 - acc: 0.9605 - val_loss: 0.2398 - val_acc: 0.9160\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.0960 - acc: 0.9605 - val_loss: 0.1668 - val_acc: 0.9350\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.0907 - acc: 0.9615 - val_loss: 0.1561 - val_acc: 0.9370\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 18s 180ms/step - loss: 0.1002 - acc: 0.9580 - val_loss: 0.1738 - val_acc: 0.9320\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0989 - acc: 0.9605 - val_loss: 0.2272 - val_acc: 0.9210\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0894 - acc: 0.9670 - val_loss: 0.1813 - val_acc: 0.9310\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 18s 178ms/step - loss: 0.0892 - acc: 0.9600 - val_loss: 0.1862 - val_acc: 0.9320\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 23s 226ms/step - loss: 0.0748 - acc: 0.9740 - val_loss: 0.2062 - val_acc: 0.9300\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.0725 - acc: 0.9735 - val_loss: 0.1819 - val_acc: 0.9370\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 17s 173ms/step - loss: 0.0828 - acc: 0.9685 - val_loss: 0.1884 - val_acc: 0.9300\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 21s 215ms/step - loss: 0.0691 - acc: 0.9720 - val_loss: 0.1701 - val_acc: 0.9350\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.0776 - acc: 0.9705 - val_loss: 0.1657 - val_acc: 0.9360\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 19s 186ms/step - loss: 0.0809 - acc: 0.9705 - val_loss: 0.1635 - val_acc: 0.9380\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 18s 177ms/step - loss: 0.0683 - acc: 0.9755 - val_loss: 0.1702 - val_acc: 0.9350\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 18s 181ms/step - loss: 0.0868 - acc: 0.9640 - val_loss: 0.1710 - val_acc: 0.9370\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 23s 226ms/step - loss: 0.0734 - acc: 0.9735 - val_loss: 0.1884 - val_acc: 0.9340\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 22s 221ms/step - loss: 0.0747 - acc: 0.9750 - val_loss: 0.1650 - val_acc: 0.9410\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0642 - acc: 0.9755 - val_loss: 0.1912 - val_acc: 0.9340\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0482 - acc: 0.9835 - val_loss: 0.1896 - val_acc: 0.9370\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0735 - acc: 0.9705 - val_loss: 0.1638 - val_acc: 0.9410\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0586 - acc: 0.9765 - val_loss: 0.2661 - val_acc: 0.9230\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 20s 202ms/step - loss: 0.0567 - acc: 0.9760 - val_loss: 0.1934 - val_acc: 0.9390\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 23s 226ms/step - loss: 0.0609 - acc: 0.9765 - val_loss: 0.1815 - val_acc: 0.9340\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 20s 201ms/step - loss: 0.0601 - acc: 0.9780 - val_loss: 0.2744 - val_acc: 0.9230\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0550 - acc: 0.9770 - val_loss: 0.1976 - val_acc: 0.9380\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0519 - acc: 0.9795 - val_loss: 0.1789 - val_acc: 0.9380\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 21s 208ms/step - loss: 0.0401 - acc: 0.9850 - val_loss: 0.2542 - val_acc: 0.9310\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 23s 226ms/step - loss: 0.0474 - acc: 0.9835 - val_loss: 0.2075 - val_acc: 0.9350\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 20s 196ms/step - loss: 0.0526 - acc: 0.9780 - val_loss: 0.2334 - val_acc: 0.9320\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 18s 184ms/step - loss: 0.0481 - acc: 0.9850 - val_loss: 0.2124 - val_acc: 0.9400\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 23s 227ms/step - loss: 0.0491 - acc: 0.9830 - val_loss: 0.2573 - val_acc: 0.9310\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.0517 - acc: 0.9815 - val_loss: 0.2267 - val_acc: 0.9300\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0558 - acc: 0.9825 - val_loss: 0.4742 - val_acc: 0.9050\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0409 - acc: 0.9820 - val_loss: 0.2103 - val_acc: 0.9330\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/100\n",
      "100/100 [==============================] - 20s 197ms/step - loss: 0.0417 - acc: 0.9855 - val_loss: 0.2075 - val_acc: 0.9320\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 23s 226ms/step - loss: 0.0451 - acc: 0.9855 - val_loss: 0.2206 - val_acc: 0.9340\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 21s 205ms/step - loss: 0.0415 - acc: 0.9830 - val_loss: 0.3302 - val_acc: 0.9250\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0394 - acc: 0.9865 - val_loss: 0.2204 - val_acc: 0.9380\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0289 - acc: 0.9895 - val_loss: 0.3501 - val_acc: 0.9230\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0387 - acc: 0.9835 - val_loss: 0.1942 - val_acc: 0.9410\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0423 - acc: 0.9840 - val_loss: 0.1999 - val_acc: 0.9470\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0393 - acc: 0.9845 - val_loss: 0.2447 - val_acc: 0.9340\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0369 - acc: 0.9860 - val_loss: 0.3050 - val_acc: 0.9260\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0371 - acc: 0.9840 - val_loss: 0.2220 - val_acc: 0.9400\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.0313 - acc: 0.9860 - val_loss: 0.2135 - val_acc: 0.9410\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 23s 229ms/step - loss: 0.0387 - acc: 0.9845 - val_loss: 0.2304 - val_acc: 0.9380\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 22s 216ms/step - loss: 0.0271 - acc: 0.9895 - val_loss: 0.2510 - val_acc: 0.9380\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0332 - acc: 0.9860 - val_loss: 0.4889 - val_acc: 0.9090\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0273 - acc: 0.9905 - val_loss: 0.2569 - val_acc: 0.9370\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0303 - acc: 0.9910 - val_loss: 0.2285 - val_acc: 0.9330\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0435 - acc: 0.9845 - val_loss: 0.3874 - val_acc: 0.9110\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0363 - acc: 0.9855 - val_loss: 0.2205 - val_acc: 0.9330\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 21s 214ms/step - loss: 0.0277 - acc: 0.9900 - val_loss: 0.2823 - val_acc: 0.9270\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 23s 230ms/step - loss: 0.0328 - acc: 0.9890 - val_loss: 0.4510 - val_acc: 0.9120\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.0356 - acc: 0.9860 - val_loss: 0.3352 - val_acc: 0.9270\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 17s 174ms/step - loss: 0.0333 - acc: 0.9895 - val_loss: 0.3439 - val_acc: 0.9220\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0457 - acc: 0.9865 - val_loss: 0.2884 - val_acc: 0.9290\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0260 - acc: 0.9910 - val_loss: 0.2198 - val_acc: 0.9430\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0340 - acc: 0.9885 - val_loss: 0.2427 - val_acc: 0.9380\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0287 - acc: 0.9905 - val_loss: 0.4385 - val_acc: 0.9110\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0292 - acc: 0.9880 - val_loss: 0.2975 - val_acc: 0.9320\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 22s 220ms/step - loss: 0.0267 - acc: 0.9905 - val_loss: 0.2707 - val_acc: 0.9320\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 23s 229ms/step - loss: 0.0277 - acc: 0.9915 - val_loss: 0.2361 - val_acc: 0.9410\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 19s 186ms/step - loss: 0.0244 - acc: 0.9915 - val_loss: 0.3125 - val_acc: 0.9360\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0376 - acc: 0.9880 - val_loss: 0.3075 - val_acc: 0.9280\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0256 - acc: 0.9900 - val_loss: 0.2759 - val_acc: 0.9320\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0301 - acc: 0.9895 - val_loss: 0.2814 - val_acc: 0.9330\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0281 - acc: 0.9870 - val_loss: 0.2439 - val_acc: 0.9400\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0280 - acc: 0.9905 - val_loss: 0.2742 - val_acc: 0.9390\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0396 - acc: 0.9860 - val_loss: 0.2532 - val_acc: 0.9350\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 17s 175ms/step - loss: 0.0267 - acc: 0.9900 - val_loss: 0.2474 - val_acc: 0.9370\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0245 - acc: 0.9910 - val_loss: 0.3716 - val_acc: 0.9290\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 18s 175ms/step - loss: 0.0262 - acc: 0.9905 - val_loss: 0.2347 - val_acc: 0.9420\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 18s 176ms/step - loss: 0.0234 - acc: 0.9920 - val_loss: 0.4633 - val_acc: 0.9150\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-5),\n",
    "              metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=100,\n",
    "    epochs=100,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 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",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\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",
    "\n",
    "plt.figure()\n",
    "\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",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smooth_curve(points, factor=0.8):\n",
    "    smoothed_points = []\n",
    "    for point in points:\n",
    "        if smoothed_points:\n",
    "            previous = smoothed_points[-1]\n",
    "            smoothed_points.append(previous * factor + point * (1 - factor))\n",
    "        else:\n",
    "            smoothed_points.append(point)\n",
    "    return smoothed_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CRo0gJwcyM93r4EG3bJMmsHs3/P3vMGsWXHKJm/bpp27f1azpfgxatoT27cNX5uPHYcUKqF0b2raFM84I37qLa/16OHbMnRHWrAlJSfl3Yfv6+mt44w146y3YsMHtW39Vqrjt6tIl8uUu744dg8OH3bH+6Sf3o7h9O2RkuO9T375Qo4b7AV2yBGbMgHPPdRWQQPs/EizoVwDFadKZNKlsyhRpp07B2rXw2Wfu4vOBA+7v5s2wcWPgJ3fVrg1XXul6Kf34o6uJVqsGLVq4nENt2kCvXtCqlQvwI0fCZZe5wL5unftMfzfcAP/3f+7MAGDXLnem8PnnLtB9841bf7t27p/3+HFX1kOH3EPiGzeGevXg44/h/ffdjxW4QNm2LaSl5b9SU919E5GUnQ133+2Cja8OHdz4G290++711+G119x+EYEePeB3v3OViuRk98PZuLGb1qULDBsGq1fH35mTKqxaBf/4B3zwgfteFJbipGpVuPxy2LQJtmxx/7Pz5rnK3JQpZRP4rctmOReOJh1VVwOpVi0yZQyH7GxYswZWrnSBfulSVzvPVbOmC55nn+2C6znnQOXKrjZ1/LgLmr16uX+qUB0+DI884n5cLr7Y/QC0aQNHjrig/fbb7h+xcmUYOtT9c3/1lVu2Rg3o1g3OP9/9GK9b584SKleGunVdeffvzz+7qF8fBg1yr+PH4YsvXJBMT3fzgfvBeucdlxAvEjZtgiFD3PaOGeMC+dGjrjb60ktufM2abr+Am3799XDttS7IB7N8OfTs6X4wZs9243budPvq6FH3Skpy85R3e/bA99+7H+TC/l8OHnQ94l580W1n7o9fnz5uX1Wp4s7kGjVy3aObNHHf73ffdT8QP/uZ+7++5hq4/3547jkYN879D5c08IfaZRNVLVevLl26qMmXnKzqwnbgV/Xqqq++Gnz5tWtV+/ZVPeMM1X/9q3RlOXFCdc8e1Zyc4i975Ijq3LmqDz2k+sc/qv7lL6rPPqv6q1+ppqaqVq6cv01NmqiOGKE6a5bq9u2qx4+Xrtyl8d13qoMHq1arpnrFFa7sq1cH3gfHj6ueOlVw3OHDqlu3un0XyKlT7jPGj3fbPm1a6cq7d6/qkiWqx44VLNfTT6vWqqV65pmqixYFLse//+2OxxNPuDIVx4QJrvw336zaqVPg7+rzz5du20K1Zo3qzp2hz79iheovf6naqlV+WRMS3Pdy+HDVYcNUr75atU8f1S5d3P9k1apuvs6dVadPV83MLHl5T55UHT3arW/8+JKvB3ezbJEx1mr65VylSsGfZFVY7X7rVnjySXcaX6eOq2nu2QOffOJO5cGdxs+Z42pojRqdvo5Dh1xt5uWXXbPK//7nxrdsCWPHws03F2yXzspyp6ybN7vml5Mn3euLL1yb8KFDBR/FCK5cXbq4V7du0LVrflNKeaIa2VNvVXcWsHixO9sJdC3hp59cbfzTT91r/353dtKrl6uhP/ecq2kfO+aas0aOdGdAEye6Zqg+feBvf3M1z3A7edKt/9//hu7dYeBAd6ZQu7arMf/ud65565VXXKqQcMvJcddonn7anSk2buya4c49N/gye/e67/GMGe7s7JJL3Blfixb5Z50bN7rveLVq7nXmmW7fNmrkzv66dg3P9+LUKbj1Vvc/MnduyZr5rKZfwb36auG1/OTk05fJzlZ9/XXVXr3cPFWqqN57r+q+farff6/atKl77dihOmOGq/WBaoMGqm+9lb+e1atVb79dtWZNN71TJ9U773S1uT/9STUtzY1v1kx1wADVDh1U69ULXtbatV1NaulSV0M+fFg1I8OVyb9mHM/27FFt1Ei1XTu3j/77X9VJk9yZxrnnutqn7/Hv1ElVJH9cYqKrMc6fr3rttflnT+eco7pwYeT39bFjqv/7X+BpR4+qXn6524a//MWdeQwerNq6tepFF6kOHao6Zow7UymuTZtU27Rx23r22aqPP6561lmqjRurfv11/nynTrl5585Vvf9+9/2vXNm9P3iwZNscTjk5wc8IQ0GINf2oB3n/lwV9F/CrVw8eRKtXV5050zWZZGW50/Jbb1WtUyc/IDz2mAvuvr780p3i56774otV333XnaKC6jXX5L9PTFQdOVL1889PDxanTqn+85+qvXu7wDNwoOodd7imjzffdJ+ze7fqjz+6H5xoNs9UNP/4h9v/Z5yRf7xbt3bH5pFHXEDPyMiff98+t8+ff/70gPnDD6rvv1+wqSeaDh5UveCC/O1q0cJt1+WXux+1ypVVW7ZU3bAh9HUuX65av757LViQ3+y2fr37AW3USHXqVNUhQ1QbNsz/7DPOcM2eX30VmW2NBgv6FVjz5sEDfr16rs3bf3yNGqo33aS6eLFrIwxm8WJXk/zrX/Pn++kn1UcfdbWwjh1dW/v+/WWzreZ0f/qTOzN67TUXuGPJwYPuh8i/QqKq+tlnLkjXru0qI7t2uQrERx+pbttWsPKxe7fqiy+64N26tavB+9uwwa0v96z05pvdWcbq1e47H2tCDfrWpl+OHDvm7qK9557C57vwQujXDxITXS+Bpk1dz48aNUr3+SdOuN4nZdVf2Bh/O3bAVVe5NnV/deq43lXff+96B4G7Mezvf3dt7YEcOuSuLyUnx/73OtQ2feunXw6cOOG6zI0bl3+xNJBatdwFvHDeMOSrSpXIrNeYUDVv7jobvPyy68Rw1lku2G/Z4i5ib9jgLl6npbmL/xdd5CoqwdSqFX/3DhTFgn4UHTnivtx/+pPr411Y/+zq1eGFFyIX8I0pL2rUgLvuKjiuT5/olCUWWZbNKMjOhj/8wZ1y3n23617WsGHgO0LBMmUaY8LHavplbMkS+PWv3enqwIH5d0YGq+WLxEcuHWNM2bCafhnZvh1GjXLZCHMThC1c6AJ6SkrwG7AicSONMSZ+WU0/wjZvdnfGvvKKC/Zjx8L48e7uvqLy6sRSAjVjTPlgQT8CsrJcmt85c9yt4FWrwu23wwMPQLNm+fMVlirZcuIbYyLBgn4YqcLzz7vgnp3tMkGOHw+/+Y3LqperqFTJ1o5vjIkUC/phcvAg3HYbzJ8Pv/iFeyhCoGRMoaRKtnZ8Y0ykWNAPg82boX9/+O47mDzZ1fSD9cYp6ulX1o5vjIkk671TShkZrkfO/v3uwR9jxgQO+HPmuF46hT39yvrjG2MizWr6pbB3r3vm5f797oJtsGeEhtKkEy/PtjXGRJcF/RI6eNAlPdu61T0bs7CHQluTjjGmvLDmnRJQhV/+0mUCXLDAZforzI4dwadZk44xpixZTb8Enn/e9cOfMgUGDCh6/ubNA7flW5OOMaasWU2/mFavds/77N8f7ruv8Hl9L976d920Jh1jTDSEFPRFpJ+IfCsim0VkbIDpySKyRETWishHIpLkM22kiGzyXiPDWfiypOqaaa67zuX4njWr8FTIuRdvc2v46vNgbWvSMcZES5HNOyKSADwH9AEygJUislBVN/jMNhV4RVVnicgVwJPATSJyJvB7IA1QYJW3bCGPCilf3nsPnnrKPcBh7173lPp//xsaNCh8uUAXb1WtSccYE12h1PS7AZtV9TtVPQ7MBa7ym+d8YIn3fpnP9J8Di1V1vxfoFwP9Sl/ssvHXv7pHt+U+wu2ZZ+CLL1wq5KIEu3hb2EVdY4yJtFAu5DYFvvcZzgAu8JvnS+BaYBowGKglIvWDLNvU/wNEZDQwGqB5OchBoApPPAGPPOJSKrzxRvGfPxvs4m052DxjTBwLpaYf6HHC/tnf7wcuE5EvgMuAnUBOiMuiqtNVNU1V0xo2bBhCkSJH1V2ofeQRuOkmeOedkj1wfNIkd7HWl128NcZEWyhBPwPwSQhMErDLdwZV3aWq16hqJ+Bhb1xWKMuWJydPuouvTz8N994LM2cW72Hhub11KlVybfojR7o2fBG7eGuMKR9Cad5ZCbQSkRa4Gvww4EbfGUSkAbBfVU8B44CXvEkfAE+ISD1vuK83vdw5ccI92eq11+DRR2HixNO7WRbGP9XC9u2uh48FemNMeVJkTV9Vc4C7cAH8a2C+qq4XkcdEZJA3W0/gWxHZCDQCJnnL7gf+gPvhWAk85o0rd+680wX8yZPhsceKF/AhcG+do0fdeGOMKS9Egz2cNUrS0tI0PT29TD/zyy+hUyfXlj91asnWUalS4OfcisCpU6UrnzHGFEVEVqlqWlHz2R25wEMPQd267uJtSQXrlWO9dYwx5UncB/2PP4ZFi9wDy+vWLfl6rLeOMaYiiOugr+qCfZMmcNddpVvX8OHuoq311jHGlGdxnWXz3XdhxQoXnP1r6aHKfcj5jh2uKWfSJAv0xpjyK26Dfk4OjBsHrVvDLbeUbB2BummOHu3eW+A3xpRHcdu887e/wYYNrotm5RL+9Fk3TWNMRROXQf/gQRg/Hi65BK6+uuTrsaRqxpiKJi6bd558EjIzXa+d4t6E5cuSqhljKpq4q+lv3+7y448YAWlF3sYQmD0RyxhTUcVd0H/oIReon3iiZMvbE7GMMRVZXDXvrFsHr78OY8ZAs2ZFzx+IPRHLGFORxVVNf+JEqFkTHnig5Ouwi7fGmIosboL+2rWwYAHccw+ceWbJ12M5dowxFVncBP2JE6F2bZdJszQsx44xpiKLi6C/Zg289Rb89rdQr17R8xfGcuwYYyqyuMinP3gwLFvmLrSWJpOmMcaUV5ZP37NsGbz9Ntx/f+kCvu/zb1NS3LAxxlQ0Md1l88QJlzK5RQu4776Sr8cSqxljYkVM1/SfecYlVZs2DapVK/l6LLGaMSZWxGzQ37ULJkyAAQNg4MDSrcv65htjYkXMBv3773fNO9OmlX5d1jffGBMrYjLoL17s0i08+CC0bFn69VnffGNMrIi5oJ+dDbffDq1aueRq4WB9840xsSLmeu88/jhs2QJLlkBiYvjWO3y4BXljTMUXUzX9detgyhQYORKuuKL067O++caYWBMzNf1Tp+DXv4Y6dWDq1NKvz/rmG2NiUczU9LdsgY0b4c9/hgYNSr8+65tvjIlFMVPTb9XKBf1w5daxvvnGmFgUMzV9cBk0S/Ogc1/WN98YE4tCCvoi0k9EvhWRzSIyNsD05iKyTES+EJG1ItLfG58iItkissZ7vRjuDYgU65tvjIlFRTbviEgC8BzQB8gAVorIQlXd4DPbI8B8VX1BRM4HFgEp3rQtqpoa3mJHXu7F2ocfdk06zZu7gG8XcY0xFVkobfrdgM2q+h2AiMwFrgJ8g74Ctb33dYBd4SxktFjffGNMrAmleacp8L3PcIY3ztcEYISIZOBq+Xf7TGvhNfv8W0QuCfQBIjJaRNJFJD0zMzP00htjjCmWUIJ+oEuj/o/bugGYqapJQH9gtohUAnYDzVW1E/A74DURqe23LKo6XVXTVDWtYcOGxduCMLMbsowxsSyUoJ8BNPMZTuL05ptfAfMBVHUFkAg0UNWfVHWfN34VsAVoXdpCR0ruDVnbt4Nq/g1ZFviNMbEilKC/EmglIi1E5AxgGLDQb54dQC8AEWmDC/qZItLQuxCMiJwNtAK+C1fhw81uyDLGxLoiL+Sqao6I3AV8ACQAL6nqehF5DEhX1YXAfcBfReS3uKafUaqqInIp8JiI5AAngd+o6v6IbU0p2Q1ZxphYJ6r+zfPRlZaWpunp6VH57JQU16TjLzkZtm0r69IYY0zoRGSVqqYVNV9M3ZFbWnZDljEm1lnQ92EPSzHGxLqYSbgWLnZDljEmlllN3xhj4ogFfWOMiSMW9LG7cI0x8SPu2/TtsYjGmHgS9zV9uwvXGBNP4j7o2124xph4EvdB3x6LaIyJJ3Ef9O0uXGNMPIn7oG934Rpj4knc994BuwvXGBM/4r6mb4wx8cSCvjHGxBEL+sYYE0cs6BtjTByxoG+MMXHEgr4xxsSRuA36llnTGBOP4rKfvmXWNMbEq7is6VtmTWNMvIrLoG+ZNY0x8Soug75l1jTGxKu4DPqWWdMYE6/iMuhbZk1jTLyKy947YJk1jTHxKS5r+sYYE68s6BtjTBwJKeiLSD8R+VZENovI2ADTm4vIMhH5QkTWikh/n2njvOW+FZGfh7PwxhhjiqfINn0RSQCeA/oAGcBKEVmoqhufUQdSAAAURElEQVR8ZnsEmK+qL4jI+cAiIMV7PwxoCzQBPhSR1qp6MtwbYowxpmih1PS7AZtV9TtVPQ7MBa7ym0eB2t77OsAu7/1VwFxV/UlVtwKbvfUZY4yJglCCflPge5/hDG+crwnACBHJwNXy7y7GsmXGkqwZY+JdKEFfAoxTv+EbgJmqmgT0B2aLSKUQl0VERotIuoikZ2ZmhlCk4stNsrZ9O6jmJ1mzwG+MiSehBP0MoJnPcBL5zTe5fgXMB1DVFUAi0CDEZVHV6aqapqppDRs2DL30xWBJ1owxJrSgvxJoJSItROQM3IXZhX7z7AB6AYhIG1zQz/TmGyYiVUWkBdAK+G+4Cl8clmTNGGNCCPqqmgPcBXwAfI3rpbNeRB4TkUHebPcBt4nIl8DrwCh11uPOADYA/wTujFbPHUuyZowxIKqnNbFHVVpamqanp4d9vf4PTgGXZM1y7hhjYoGIrFLVtKLmi5s7ci3JmjHGxFnCNUuyZoyJd3FT0zfGGGNB3xhj4ooFfWOMiSMW9I0xJo5Y0DfGmDhiQd8YY+KIBX1jjIkjFvSNMSaOxHzQtxz6xhiTL6bvyPXPt5ObQx/szlxjTHyK6Zq+5dA3xpiCYjroWw59Y4wpKKaDvuXQN8aYgmI66E+a5HLm+6pe3Y03xph4FNNB33LoG2NMQTHdewcsh74xxviK6Zq+McaYgmK+pm9MpJw4cYKMjAyOHTsW7aKYOJKYmEhSUhJVqlQp0fIW9I0poYyMDGrVqkVKSgoiEu3imDigquzbt4+MjAxatGhRonVY844xJXTs2DHq169vAd+UGRGhfv36pTq7tKBvTClYwDdlrbTfOQv6xhgTRyzoG1NGKnLG123btvHaa6/lDc+cOZO77rqrxOv76KOPuPLKK08bv2bNGhYtWlTs9e3atYshQ4YUOV///v05cOBAsdcfSyzoG1MGcjO+bt8OqvkZXytK4PcP+pFSWNDPyckJulyTJk1YsGBBketftGgRdevWLXH5YoEFfWPKQCQyvh45coQBAwbQsWNH2rVrx7x58wBISUnhoYceonv37qSlpbF69Wp+/vOf07JlS1588UXA9QJ54IEHaNeuHe3bt89bNtj4sWPHsnz5clJTU3nqqacAV7vu168frVq14sEHH8wr17/+9S+6d+9O586dGTp0KIcPHwbgn//8J+eddx4XX3wxb7311mnbc/z4ccaPH8+8efNITU1l3rx5TJgwgdGjR9O3b19uvvlmtm3bxiWXXELnzp3p3Lkzn332GeB+lNq1awe4s5BrrrkmYNlSUlLYu3cv27Zto02bNtx22220bduWvn37kp2dDcDKlSvp0KED3bt3z9sX/g4fPkyvXr3o3Lkz7du355133smb9sorr9ChQwc6duzITTfdBMCePXsYPHgwHTt2pGPHjnnljgpVLVevLl26qDEVwYYNG0KeV0TV1fELvkRK/vkLFizQW2+9NW/4wIEDqqqanJyszz//vKqq3nvvvdq+fXs9ePCg/vjjj9qwYcO8ZXv37q05OTn6ww8/aLNmzXTXrl1Bxy9btkwHDBiQ91kvv/yytmjRQg8cOKDZ2dnavHlz3bFjh2ZmZuoll1yihw8fVlXVyZMn68SJEzU7O1uTkpJ048aNeurUKR06dGiB9fmu984778wb/v3vf6+dO3fWo0ePqqrqkSNHNDs7W1VVN27cqLnxYuvWrdq2bdtCy5a7bzIzM3Xr1q2akJCgX3zxhaqqDh06VGfPnq2qqm3bttVPP/1UVVXHjBmTt15fJ06c0KysLFVVzczM1JYtW+qpU6d03bp12rp1a83MzFRV1X379qmq6nXXXadPPfWUqqrm5OTkHauSCvTdA9I1hBgbkzX9itx2amJTJDK+tm/fng8//JAxY8awfPly6tSpkzdt0KBBefNccMEF1KpVi4YNG5KYmMiBAwf45JNPuOGGG0hISKBRo0ZcdtllrFy5Muj4QHr16kWdOnVITEzk/PPPZ/v27Xz++eds2LCBHj16kJqayqxZs9i+fTvffPMNLVq0oFWrVogII0aMCHk7Bw0aRLVq1QB3Q9xtt91G+/btGTp0KBs2bAi5bP5atGhBamoqAF26dGHbtm0cOHCAQ4cOcdFFFwFw4403Bly/qvLQQw/RoUMHevfuzc6dO9mzZw9Lly5lyJAhNGjQAIAzzzwTgKVLl3L77bcDkJCQUOBYlbWYuznLnpZlyqNJkwp+L6H0GV9bt27NqlWrWLRoEePGjaNv376MHz8egKpVqwJQqVKlvPe5wzk5ObiK4emCjQ/Ed70JCQl56+3Tpw+vv/56gXnXrFlT4q6GNWrUyHv/1FNP0ahRI7788ktOnTpFYmJiyGUrap7s7OyQt3/OnDlkZmayatUqqlSpQkpKCseOHUNVy3033pBq+iLST0S+FZHNIjI2wPSnRGSN99ooIgd8pp30mbYwnIUPxJ6WZcqjSGR83bVrF9WrV2fEiBHcf//9rF69OuRlL730UubNm8fJkyfJzMzk448/plu3bkHH16pVi0OHDhW53gsvvJBPP/2UzZs3A3D06FE2btzIeeedx9atW9myZQvAaT8KuYr6nKysLBo3bkylSpWYPXs2J0+eDHmbQ1GvXj1q1arF559/DsDcuXODluOss86iSpUqLFu2LO9MolevXsyfP599+/YBsH///rzxL7zwAgAnT57k4MGDYS13cRRZ0xeRBOA5oA+QAawUkYWqmndepaq/9Zn/bqCTzyqyVTU1fEUunD0ty5RX4c74+tVXX/HAAw9QqVIlqlSpkhdUQjF48GBWrFhBx44dERGmTJnCz372s6Dj69evT+XKlenYsSOjRo2iXr16AdfbsGFDZs6cyQ033MBPP/0EwOOPP07r1q2ZPn06AwYMoEGDBlx88cWsW7futOUvv/xyJk+eTGpqKuPGjTtt+h133MG1117LG2+8weWXX17gLCBcZsyYwW233UaNGjXo2bNnwKaY4cOHM3DgQNLS0khNTeW8884DoG3btjz88MNcdtllJCQk0KlTJ2bOnMm0adMYPXo0M2bMICEhgRdeeIHu3buHveyhkKJOZ0SkOzBBVX/uDY8DUNUng8z/GfB7VV3sDR9W1ZqhFigtLU3T09NDnf00KSmuScdfcjJs21bi1Rpzmq+//po2bdpEuxgmzA4fPkzNmi5kTZ48md27dzNt2rQol6qgQN89EVmlqmlFLRtK805T4Huf4Qxv3GlEJBloASz1GZ0oIuki8rmIXB1kudHePOmZmZkhFCk4e1qWMaY03n//fVJTU2nXrh3Lly/nkUceiXaRwiqUC7mBrkoEOz0YBixQVd+GtuaquktEzgaWishXqrqlwMpUpwPTwdX0QyhTULmnzw8/7Jp0mjd3Ad8u4hpjQnH99ddz/fXXR7sYERNK0M8AmvkMJwG7gsw7DLjTd4Sq7vL+ficiH+Ha+7ecvmj42NOyjDEmsFCad1YCrUSkhYicgQvsp/XCEZFzgXrACp9x9USkqve+AdADCNyx1hhjTMQVWdNX1RwRuQv4AEgAXlLV9SLyGO4OsNwfgBuAuVrwynAb4C8icgr3AzPZt9ePMcaYshXSzVmqughY5DduvN/whADLfQa0L0X5jDHGhFFMpmEwJl5MmjSJtm3b0qFDB1JTU/nPf/4Tsc8qq/TKpVnPwoULmTx5csD5crthBnPgwAGef/75vOFQ0zVXNBb0jamgVqxYwXvvvcfq1atZu3YtH374Ic2aNSt6wRIqq/TKpTFo0CDGjj0taUBI/IN+qOmaKxoL+saEwb33Qs+e4X3de2/hn7l7924aNGiQl0OmQYMGNGnSBKi46ZUBLrjgAtavX5833LNnT1atWsV///tfLrroIjp16sRFF13Et99+e9qyvmcfW7dupXv37nTt2pVHH300b55gaZHHjh3Lli1bSE1N5YEHHiiQrvnYsWPccssttG/fnk6dOrFs2bK8zwuWxtnXY489RteuXWnXrh2jR4/Oy/GzefNmevfuTceOHencuXNemoopU6bQvn17OnbsWOIfsaBCScVZli9LrWwqCt/0tvfco3rZZeF93XNP4Z9/6NAh7dixo7Zq1Upvv/12/eijj/KmVeT0yn/+8591/Pjxqqq6a9cubdWqlaqqZmVl6YkTJ1RVdfHixXrNNdeoqhYol29q5oEDB+qsWbNUVfXZZ5/VGjVqqGrwtMi+6ZlVC6Zrnjp1qo4aNUpVVb/++mtt1qyZZmdnF5rG2VduimVV1REjRujChQtVVbVbt2761ltvqapqdna2HjlyRBctWqTdu3fXI0eOnLZsrtKkVo65LJvGRMPTT5f9Z9asWZNVq1axfPlyli1bxvXXX8/kyZMZNWoUUDC98uHDh6lVqxa1atUqcXrl2rVrn1aG3BTGQF4K4wMHDuSlVwb3cJTu3bsXSK8MMGLECKZPn37aOq+77jr69OnDxIkTmT9/PkOHDgVckrORI0eyadMmRIQTJ04Uun8+/fRT3nzzTQBuuukmxowZA+SnRf7444+pVKlSXlrkwnzyySfcfffdAJx33nkkJyezcePGoPvAv5lt2bJlTJkyhaNHj7J//37atm1Lz5492blzJ4MHDwbIyxj64Ycfcsstt1DdSy2Qm545XGKmecdy6Jt4lJCQQM+ePZk4cSLPPvtsXpCD6KdXXrNmDWvWrGHDhg3MmDEDIKS0w02bNqV+/fqsXbuWefPmMWzYMAAeffRRLr/8ctatW8e7777LsWPHilxXoM/zTYu8Zs0aGjVqVOS6CtsnRaVxPnbsGHfccQcLFizgq6++4rbbbstLwxzssyKZnjkmgn5Ff/6oMSXx7bffsmnTprzhNWvWkJycHPLy5TW9MsCwYcOYMmUKWVlZtG/ven1nZWXRtKlL+zVz5swiy9GjR4+81MhzfIJBsLTIhW3fpZdemreOjRs3smPHDs4999wiywDk/aA0aNCAw4cP510crl27NklJSbz99tsA/PTTTxw9epS+ffvy0ksvcdTLEZ+bnjlcYiLoWw59E48OHz7MyJEjOf/88+nQoQMbNmxgwoQJIS8/ePDgvGe5XnHFFQXSKwca36FDh7z0yrkXcgPxTa/coUMHLrzwQr755hsSExPz0itffPHFhf5ADRkyhLlz53LdddfljXvwwQcZN24cPXr0CCmP/rRp03juuefo2rUrWVlZeeOHDx9Oeno6aWlpzJkzJy8tcv369enRowft2rXjgQceKLCuO+64g5MnT9K+fXuuv/56Zs6cWaCGX5i6devmPe3r6quvpmvXrnnTZs+ezTPPPEOHDh246KKL+OGHH+jXrx+DBg3KS9s8derUkD4nVEWmVi5rJUmtXKmSq+H7E4FTp8JUMGP8WGplEy2RTq1c7kXi+aPGGBOLYiLoWw59Y4wJTUwE/Ug8f9SYUJS35lET+0r7nYuZfvqWQ9+UtcTERPbt20f9+vUj2sXOmFyqyr59+/L69JdEzAR9Y8paUlISGRkZlPYRn8YUR2JiIklJSSVe3oK+MSVUpUoVWrRoEe1iGFMsMdGmb4wxJjQW9I0xJo5Y0DfGmDhS7u7IFZFMYHsxF2sA7I1AccqzeNxmiM/tjsdthvjc7tJsc7KqNixqpnIX9EtCRNJDuf04lsTjNkN8bnc8bjPE53aXxTZb844xxsQRC/rGGBNHYiXon/74ndgXj9sM8bnd8bjNEJ/bHfFtjok2fWOMMaGJlZq+McaYEFjQN8aYOFKhg76I9BORb0Vks4iMjXZ5IkVEmonIMhH5WkTWi8g93vgzRWSxiGzy/taLdlnDTUQSROQLEXnPG24hIv/xtnmeiJwR7TKGk4jUFZEFIvKNd7y7x8lx/q333V4nIq+LSGIsHmsReUlEfhSRdT7jAh5fcZ7x4ttaEekcjjJU2KAvIgnAc8AvgPOBG0Tk/OiWKmJygPtUtQ1wIXCnt61jgSWq2gpY4g3HmnuAr32G/wg85W3z/4BfRaVUkTMN+Keqngd0xG17TB9nEWkK/H9Amqq2AxKAYcTmsZ4J9PMbF+z4/gJo5b1GAy+EowAVNugD3YDNqvqdqh4H5gJXRblMEaGqu1V1tff+EC4QNMVt7yxvtlnA1dEpYWSISBIwAPibNyzAFcACb5aY2mYRqQ1cCswAUNXjqnqAGD/OnspANRGpDFQHdhODx1pVPwb2+40OdnyvAl5R53Ogrog0Lm0ZKnLQbwp87zOc4Y2LaSKSAnQC/gM0UtXd4H4YgLOiV7KIeBp4EMh9vH194ICq5njDsXbMzwYygZe9Jq2/iUgNYvw4q+pOYCqwAxfss4BVxPax9hXs+EYkxlXkoB/oUUUx3f9URGoCbwL3qurBaJcnkkTkSuBHVV3lOzrArLF0zCsDnYEXVLUTcIQYa8oJxGvDvgpoATQBauCaNvzF0rEORUS+7xU56GcAzXyGk4BdUSpLxIlIFVzAn6Oqb3mj9+Se7nl/f4xW+SKgBzBIRLbhmu6uwNX863pNABB7xzwDyFDV/3jDC3A/ArF8nAF6A1tVNVNVTwBvARcR28faV7DjG5EYV5GD/kqglXeF/wzchZ+FUS5TRHht2TOAr1X1zz6TFgIjvfcjgXfKumyRoqrjVDVJVVNwx3apqg4HlgFDvNlibZt/AL4XkXO9Ub2ADcTwcfbsAC4Ukeredz13u2P2WPsJdnwXAjd7vXguBLJym4FKRVUr7AvoD2wEtgAPR7s8EdzOi3GndWuBNd6rP66Newmwyft7ZrTLGqHt7wm8570/G/gvsBl4A6ga7fKFeVtTgXTvWL8N1IuH4wxMBL4B1gGzgaqxeKyB13HXLU7gavK/CnZ8cc07z3nx7Stc76ZSl8HSMBhjTBypyM07xhhjismCvjHGxBEL+sYYE0cs6BtjTByxoG+MMXHEgr4xxsQRC/rGGBNH/h/777Bp4oz0pQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KlCmkpKRw6KGHMnr0aObPn89tt91GjRo1SElJ4dlnnw17+YMVtTlUMzIy1CbrMNXV8uXLOfroo6NdDBMB27dvZ//99wdgzJgxbNy4kSeffDLKpfLP39+hiCxQ1Yzy9rWauzEmobz//vs8+uijFBQUkJaWVuJGdTyJ2TZ3SyJmjKmIiy66iKysLJYsWcL7779Pw4blTkcak2Ky5m5JxIwxpmxB1dxFpK+IrBSR1SIy0s/rQ0UkV0SyPI8rw1/UYpZEzBhjylZuzV1EkoDxwClADjBfRGapaunRBTNUteKZhkJgScSMMaZswdTcjwNWq+paVd0NvAacHdlilc2SiBljTNmCCe5NgF98lnM860o7X0QWi8hMEfGbvk5EholIpohk5ubmVqC4jiURM4kgHlP+VuY4s2bNYsyYMX6383ZtDCQvL49nnnlm73KwKYSD0bNnT6pjt+5ggru/McWlO8e/CzRX1fbAp8AkfwdS1QmqmqGqGZW5Qz14MEyYAGlpIOJ+TphgN1NN/IjXlL+V0b9/f0aO3OeWX1BKB/dgUwjHsmCCew7g+1fVFNjgu4GqblHVvz2LLwCdw1O8wCyJmIln8ZjyF+D4449n6dKle5d79uzJggUL+P777+natSsdO3aka9eurFy5cp99fb9NrFu3ji5dunDsscdyzz337N1m+/bt9O7dm06dOpGens4777yz9/rWrFlDhw4duO2220qkEA6U0res1MKBTJ8+nfT0dNq1a8cdd9wBQGFhIUOHDt37O/f+fp966inatGlD+/btufjii8s9dsjKSxuJu+m6FmgB1AQWAW1LbdPI5/m5wHflHbciKX+NqSq+qVZvuEG1R4/wPm64oezzx2vK3yeeeEJHjx6tqqobNmzQVq1aqapqfn6+7tmzR1VVP/nkEz3vvPNUVUuUyzdd8FlnnaWTJk1SVdWnn35a69atq6qqe/bs0fz8fFVVzc3N1cMPP1yLiopKpPBVDS6lb1mphX316NFD58+fr7/++qs2a9ZMN23apHv27NFevXrp22+/rZmZmdqnT5+92//555+qqtqoUSPdtWtXiXWlRTTlr6oWAMOBj4DlwOuqulREHhCR/p7NrheRpSKyCLgeGBrGzx9jEk68pvy98MILeeONNwB4/fXXGTBgAOAShg0YMIB27dpx0003lajd+/P1118zcOBAgL3peMFVVu+66y7at29Pnz59+PXXX/n999/LPFaglL6BfgeBzJ8/n549e9KwYUOSk5MZPHgwc+fOpWXLlqxdu5YRI0bw4YcfcsABBwDQvn17Bg8ezJQpU0hODv+Qo6COqKqzgdml1o32eX4nsG8WIGPiwH/+E53zelP+9uzZk/T0dCZNmsTQoUOB6Kf8LZ31MSsrK6iUv02aNCE1NZXFixczY8YMnn/+eQDuueceevXqxdtvv012djY9e/Ys91j+zjd16lRyc3NZsGABKSkpNG/enF27dpV5nLJ+J8GkFi7vOPXr12fRokV89NFHjB8/ntdff52XX36Z999/n7lz5zJr1iwefPBBli5dGtYgH7PpB4yJZ/Ga8hfg4osvZuzYseTn55Oeng64mnuTJq4TXjC5Xk488cS96Xqn+uQeyc/P5x//+AcpKSl8/vnne2vaZV1foJS+oTr++OP53//+x+bNmyksLGT69On06NGDzZs3U1RUxPnnn8+DDz7IwoULKSoq4pdffqFXr16MHTuWvLy8vfcuwiUm0w8YE+/iNeUvwAUXXMANN9xQ4kbo7bffzmWXXcYTTzzBySefXO71PfnkkwwaNIgnn3yS888/f+/6wYMHc9ZZZ5GRkUGHDh1o3bo1AKmpqZx44om0a9eO008/neuuu27vPoFS+oaqUaNGPProo/Tq1QtVpV+/fpx99tksWrSIyy+/nKKiIgAeffRRCgsLGTJkCPn5+agqN910U9gn9raUv8b4YSl/TXVQmZS/1ixjjDFxKC6Cu6X/NcaYkmK+zd3S/5pIUdUKT/psTGVVtsk85mvulv7XREKtWrXYsmVLpf/BjKkIVWXLli3UqlWrwseI+Zq7pf81kdC0aVNycnKoTII7YyqjVq1aNG3atML7x3xwP+ww1xTjb70xFZWSkkKLFi2iXQxjKizmm2Us/a8xxuwr5oO7pf81xph9xXyzDLhAbsHcGGOKxXzN3RhjzL4suBtjTByy4G6MMXHIgrsxxsQhC+7GGBOHLLgbY0wcisvgblkijTGJLi76ufuyLJHGGBOHNXfLEmmMMXEY3C1LpDHGxGFwD5QN0rJEGmMSSdwFd8sSaYwxcRjcLUukMcbEYW8ZsCyRxhgTdzV3Y4wxFtyNMSYuWXA3xpg4ZMHdGGPiUFDBXUT6ishKEVktIiPL2O4CEVERyQhfEY0xxoSq3OAuIknAeOB0oA0wUETa+NmuHnA9MC/chawMSyJmjElEwdTcjwNWq+paVd0NvAac7We7B4GxwK4wlq9SvEnE1q8H1eIkYhbgjTHxLpjg3gT4xWc5x7NuLxHpCDRT1ffKOpCIDBORTBHJzM3NDbmwobIkYsaYRBVMcBc/63TviyI1gHHALeUdSFUnqGqGqmY0bNgw+FJWkCURM8YkqmCCew7QzGe5KbDBZ7ke0A74QkSygROAWdXhpqolETPGJKpggvt8oJWItBCRmsDFwCzvi6qar6oNVLW5qjYHvgP6q2pmREocAksiZoxJVOUGd1UtAIYDHwHLgddVdamIPCAi/SNdwMqwJGLGmEQlqlr+VhGQkZGhmZlRr9wbY0xMEZEFqlpus7eNUDXGmDhkwd0YY+KQBXdjjIlDFtyNMSYOJVRwtzwzxphEEZfT7PnjzTPjTUfgzTMD1jXSGBN/EqbmbnlmjDGJJGGCu+WZMcYkkoQJ7pZnxhiTSBImuFueGWNMIkmY4G55ZowxiSRhesuAC+QWzI0xiSBhau7GGJNILLgbY0wcStjgbqNVjTHxLKHa3L1stKoxJt4lZM3dRqsaY+JdzAX3nTth9uzKHcNGqxpj4l3MBfeHHoKzzoIlSyp+DButaoyJdzEX3G++GQ44AG65BSo6/auNVjXGxLuYC+6pqTB6NHz8MXz4YcWOYaNVjTHxTrSi1d9KysjI0MzMzArtu3s3tGsHycmwaBGkpIS5cMYYU02JyAJVzShvu5iruQPUrAn//jcsX+5q3MaY+PHRR3D77bBtW7RLEttiMrgD9O8PvXrBvffC1q2VO5YNaDIm+jZuhIsvhr59XeXt5JNh06Zolyp2xWxwF4HHHoMtW+CFFyp+HO+ApvXr3Q1a74AmC/DGVI3t22HsWGjdGv77X3jgAXjrLVi6FE48EdaujXYJY1PMBneAY491tfdx41w7fEXYgCZjomPnThfUW7SAO+5wgfzHH+Gee+Dcc+Gzz+CPP6BrV9i8OfBxtm6FU05xHS3++KPqyl8RqvDqq1Uzpiamgzu4trlff4Xp0yu2vw1oMiY6Ro1yQb1zZ/jmGzc4sVWr4tdPOMH1ivv9d3jllcDH+fRT93jwQdfz7c47K99UGynZ2XDZZZUfiBmMmA/up50G6emuja4iHX9sQJMx0fHxx3Dqqa5Lc5cu/rfp3Bm6d4fnn4eiIv/bzJkDdevCwoVw5pmuufb00+GvvyJX9oqaN8/9PP74yJ8r5oO7iKu9L10KH3wQ+v42oMmYqvfHH+5/9qSTyt/2mmtgzRpXO/dnzhx3nI4d3Tf4N96A776D886Dv/8Ob7kra948qF3bVUgjLajgLiJ9RWSliKwWkZF+Xr9aRH4UkSwR+UpE2oS/qIFddBE0a+ba70JlA5qMqXrffON+dutW/rbnnQcNG8Kzz+772q+/wsqV0Lt38brzz4cXX3TfDAYPhoKC8JQ5HL77zn0bSa6CfLzlBncRSQLGA6cDbYCBfoL3NFVNV9UOwFjgibCXtAwpKXDTTfC//7mvZqEaPNi1hRUVuZ8W2I2JrK++cv+3xx5b/rb77Qf//CfMmgU5OSVfmzPH/fQN7gCXXw7/+Q+8+Sbcd19Yilxpu3fDDz9UTZMMBFdzPw5YraprVXU38Bpwtu8Gqup7+6IuUOXDXi+/HGrVgpdeqtxxrM+7MZH31VeuBlu6STSQYcPcPbUXXyy5fs4caNAA2rffd58bboBLLnH346pDd8pFi1wzUXUK7k2AX3yWczzrShCR60RkDa7mfr2/A4nIMBHJFJHM3NzcipQ3oIMOcl/fpk1zXawqwvq8GxN5u3bB/PnBNcl4tWzpOk+88ALs2ePWqbrg3quXq4z5M2aM+4Zwyy3+X9+923W5XLQotGuoiKq8mQrBBXfxs26fmrmqjlfVw4E7gLv9HUhVJ6hqhqpmNGzYMLSSBuGf/4S8PDcQoiKsz7sx4XH77S6w+rNggQuqoQR3gOuugw0b4AlPo++qVa7NvXSTjK/GjeHuu11M8N6QVYV333WjYRs2dPt36OCWI1nDnzcPDj3U3R+sEqpa5gPoAnzks3wncGcZ29cA8ss7bufOnTXcCgtV09JU+/Sp2P4iqu6tL/kQCWsxjYlpBQVlv75pk2pSkmpysuqaNfu+PmaM+7/atCm08xYVqV5wgTvuvHmq48e74/z0U9n77dyp2rKlatu2ql98odq1q9uvYUPVf/5T9b//VR09WrVOHdWUFNUrrlB9913VHTtCK195WrVSPfvsyh8HyNRy4qu6Syw3uCcDa4EWQE1gEdC21DatfJ6fFczJIxHcVVXvu88F43XrQt83Lc1/cE9LC3MhjYlRu3apNmmietFFLmj688wz7v8mOVn10kv3ff3MM1Vbt67Y+f/4Q/Www1QPP1z11FPd86Ki8vd7++3i/+dGjVQnTFDds6fkNr/+qjpsmOr++7vtatd2y+V9mAWycmVx2bZsccd85JGKHctX2IK7Oxb9gFXAGmCUZ90DQH/P8yeBpUAW8Hnp4O/vEangnp3tgvt994W+75Qp7tPbN7DXqePWG2NUFy8u/t/o1s0FrdK6d1dt00b11lvd/+KSJcWvFRaq1q+veuWVFS/Dl1+q1qjhynD55cHtU1Skescdqg8/rLp9e9nb7tql+tFHrlYPqjfdFHoZZ81y+44Z45Y/+MAtz5kT+rFKC2twj8QjUsFdVfWUU1xtu7Aw9H2nTHH7iqimprqHiFtnQd4kuhkzXNS4+27VmjVVjzrKVai81q93rz/4oOrmzar16qmee27x60uWuNcnTqxcOe691x1n6tTKHac811/vzvPcc6Htd8YZxd9evv++uEUhP7/yZUro4D59uruy2bMrfgyrxRuzr3vvdbXmHTtU585VPegg1U6dVP/+270+dqyWaAe//363PG+eqxE/9ZRbXr26cuUoKFB95519m1bCraBAtV8/dw/hww+D2ycnx/2OrrmmuAmpe3fVdu3CU6aEDu5//+3aBXv0qPgxrP3dmH1deKELVl7etuw773TLHTuqHndc8etbt6o2aFDyf+jQQ4NrJ68u8vNdYAbVQw5xbf0PPqj611/+t3/ooeIPsLlzi5uQrrgiPOUJNrhXwSDYqlezpuvXevPN8O23gZMSlcWyRRqzr+XL4eiji5fPOQeuvNJ1e2zRwo3AHDeu+PV69WDmTNeXvFYt9zj+eJfqI1YccIAr/7RpkJXlHvfcA5MmwcSJLlWxV1ERvPyy63t/+OHucffdLkf9CSdUccGD+QSIxCOSNXdV1W3bVA8+WLV//4rtbzV3E2kbNsRWDXbPHtX99lO97baS67dtc7V5b7fhDRuiU76q9MUXqi1auOu99dbinkOffeZ+D77Nt3v2qE6eHLimHyqCrLnHfFbIQPbfH0aMcPkoliwJfX9/2SJF3KhVS0tgKuvnn91gFt9abkVNmeLyqETaunVu+LxvzR3c/9qUKZCU5GqsjRpFvizR1qMHLF4M//oXPP44ZGS4vFYvvggHHuhGy3slJ8OQIcGnWgibYD4BIvGIdM1d1d2tr1NH9ZJLKra/t+eMt0ZiN1dNuLzzTvHfUUXGZHjt2OG+odao4fpVR5K3zN995//1Tz6JfBmqow8/VG3c2PWMSUlRve66yJ6PRK+5A6Smutww06a5bI+h8maLTEvbdyIQS0tgKmP58uLnw4dXbKIZcLnLvVPLPfjd9yGnAAAWGklEQVRg5ctVFm+ZW7f2/3qfPnDkkZEtQ3V02mluesABA9y3+2HDol0iJ66DO7gbq0lJ8MgjFT+G3Vw14bZihcsz8tBD8P77bkLoinj2WTjqKNd5YNo0d9xIWbbM5Wo58MDInSNWHXyw+/3/+af/DJXREPfBvWlT90n6yisVTwpkU/GZcPP2Ohkxws0gNGIE5OeXvU9mJvz0U/FyVpab/OHqq12irtq1Xa+MSJfZBFbl7epliPvgDm7C3OTkiv/h21R8JpxUiwNlcrKb+ev33+F6v4mynT//dDcrjzvOBXVwtfbatd2Eyw0bug+I115z09dFsswmNiREcG/c2M3DOHmySxMaKpuKz4TTb7/B1q3FbdcZGa7f9Kuvuoc/zz0H27e7WYlOOcXV2KdOdWlq69d329x6q5souryZhzIz3d9u6VmNypKT487fpkon0DSVEsxd10g8qqK3jK/ffnM9EwYNqvyxfPPPWM4ZE6o5c1yvk08+KV5XUOBGVNetq7piRcntd+50IyNPO80N62/UyA2HB5e3xJd3uP9bb/k/919/qR5xhNumadOSSb3K8tFHbp/PPw/2Kk2kYL1lSjrkENcrYfr0ys26YrM1mcry9jrxbeJISnJ/Q7Vrw4UXlpxN7NVXXbPN7bfDEUe4SSfq13cjPUvPQTpyJHTq5P4mf/tt33OPGgWrV8P/+39u4uhu3WDu3IqV2VRzwXwCROJR1TV3VdXcXJegv0mT8hP8B2IjV01lDR/usiX6G536/vvu76l3b/dts6DA1bQzMkpuv3mz/3S7qqrLlqnWquUSXvnuM3eu+7bp7Ye9bp3Lq16zZuCavte//uVS9cbSiNp4hdXc99Wggav17NoFPXu6GkyorFukqazly117u7/8Kv36udwkX3/tpn676y73d3r77SW3T0113e/8OfpoGDsWZs92OV8WLnTfVv/5Tze62jv9XfPmbqLqTp3gggtcj7JAli1z7e2xlBMm4QXzCRCJRzRq7l6LFrk87U2bqq5aFdq+VnM3ldWkif8ZinwtWqR65JHub+uII0KfDaiw0M1rUPrv1F+b+bZtxdv++9/+50Fo0KByE2yY8MFq7oG1b+9mTd+1y3Ut+/jj4Pe1bpGmMrZudZM6Bxrl6dW+vevVcuutrstjUlJo56lRw+VV+vBDNzn066+7CZp79tx32/33dxNGDxgAt93mct5cey28/bb7u+7ZEzZvtp4yMSeYT4BIPKJZc/das0Y1Pd3l5Xj88eDbE222JlNR8+a5GvLbb0e7JPsqKHAzG513XsmJajp2dJkgN2+OdgmNavA1d3HbVr2MjAzNzMyMyrl9bd8OQ4fCm29C166uxnLBBa4/cXm8PWd27CheV6eO9YE3gb36qht0tGKFSxtQXe3c6b45tG7tBkiZ6kNEFqhqRnnbJWSzjK/993fJl8aPh02bXGrOpk1dnojyjBpVMrCDJRQzZVu+3I1Kbdky2iUpW+3a0L27BfZYlvDBHVwPgGuvhZUr4ZNP3D/esGHl94CxnjMmVMuXQ6tWkJIS7ZKYeGfB3UeNGi5t6YwZrrWxvFSsllDMhGrFChsIZKqGBXc/mjd3+Tnefdf1NAjEZmsyodi92/VZL6+njDHhYME9gBtvdN3RRoyAbdv8b+ObUAxcYPfW9C0tgQHXhXDkSBg0yH0rLCy0mrupGhbcA0hJgeefhw0bXMrgQGy2JhPI1q3Qt6+bY/P7710ulwsvdDP3GBNpydEuQHV2wgmu5v7UU+4f8qyzAm9rN1eNr5073d/LokXwzjtwxhnRLpFJNFZzL8djj8Exx8Dll7uRhYEEuomqau3viWDVKhg3Dl580U2ZN2AAfPml69dugd1Eg9Xcy1Grlus907mza4KZM8f/UPCHH953QJOXt/0dbHBTPFAtTqD1559uYmpvCl1fzz4LAwdWffmMAau5B+Woo9wgp//9z7W/FxXtu03pm6ulWft7fLjpJqhZE5o0cbnUW7WC//zHjXLOznbNcFlZrj/71VdHu7QmkSV8+oFQDBsGL7zg0rJOmuRSCPtTo0bg/vFpaa6WbzX46ue661w/9FNOcfdYjjnGvZdeU6e6Ecxnn+1S7m7Y4EZyjh7t0vMaUxWCTT8QVJIvoC+wElgNjPTz+s3AMmAxMAdIK++Y1SFxWKiKilSfftpNbtC4serHH/vfLlBaYO+jTh1LMFbdZGa696ZRo+L3qX171aws9/rKlW4KvO7dVffsiW5ZTWIjXCl/RSQJGA+cDrQBBopI6eSfPwAZqtoemAmMDeojKMaIuNrdvHkuJ82pp8KJJ7reEL5NNf4GN/myJprq5+GH4cADXXPKhg3uxuimTa7p5ZFHXBfGWrVczqFku1NlYkAwbe7HAatVda2q7gZeA8723UBVP1dV763E74Cm4S1m9dKhA/zwAzz9NGzcCOec42aw//1393p57e9gXSSrk6VLXe7y6693Ab5RI7jiCvjxR9cEM2qU69L46qsuqZwxsSCY4N4E+MVnOcezLpArgA/8vSAiw0QkU0Qyc3Nzgy9lNVSnjqvFr1oFkye7ttrevV1tD0oObvLHukhWH488AnXrwg03lFzfoIGb5OL11909ln79olM+YyoimC+Y/mZN9Hu7UESGABlAD3+vq+oEYAK4G6pBlrFaS052N9maNHH9mXv3hs8+K06Val0ko0/V1b5//90F8bp1XXPL6ae7sQuvvQY33+xukpYm4vqsGxNrggnuOUAzn+WmwIbSG4lIH2AU0ENV/w5P8WJHr17w3ntw5plu0o8bb3R9nL1Be9QoF8xL87a/W3CPnI8+gkcfdR+4e/a4CVoKClyQb9zYpZq4+eZol9KY8AqmWWY+0EpEWohITeBiYJbvBiLSEXge6K+qm8JfzNhw8snwwQcuaAwf7tpuBw1y7fHZ2YFnjo/19vesLDeC99pr4eWXYfFi+O234kdhYXTL93//54J4To4bdLRzp8vbP3iwy/9y003uvTImrgTTpQboB6wC1gCjPOsewAVzgE+B34Esz2NWeceMxa6QofjhB9Xrr1etV081KUn12mtVmzb13zUyKanq5mB9/33Viy5SnT8/uO3//FP13ntVH3tM9a23VBctUl2xQnX5ctWvvnLzbYK7zgMO8H99zZurPvWU6vbtEb00vxYtcmUYM6bqz21MJGBzqFYPmzbBAw/Ac8+5kY2FhS6vdyCRnIM1L8+NtvXe9D3vPDd0PtCs9kVFLvnV7NmBj3nAAa7me+ON7vnq1bBgAeTnu9f37HHpG77+Gg4+2CViGzHCf/t2JAwd6qZRzMmB+vWr5pzGRFJYBzFF4hHvNffSVqxQzchwNfSDDiqusfur6TZpEpkyXHedao0aql98oXr//cXfKu67T3X37n23Hz3alWf8eNW8PFfbnzFDddo01enTVd94Q3XLluDO/dVXqv37u+PVrat6662qGzaE9/pUVQsKip9v2KCakqI6fHj4z2NMtBBkzd2CexX66y/VAQPcb33oUP+B3ft48UU3IraibrxR9dhjVZcudcuZme6DZcSI4m1yc1WHDHHny8hQXbas+LX//re4nJUpR2k//qg6aJD7kElJUb30UteEVVGFharffKN6zz3uemvUUO3Tx5X/jjvcNa9eHb7yGxNtFtyrqcJC14YNLhCVFeA7dlRdsyb0c6xeXdyOX7u26gsvuMB3yCGuBl7aG2+opqa6cx50kGrbtq523bmz6s6dlb7kgGUcMcKdx3utV1zh2ubnzFFdu9Z9m8jPV331VdUzz3Rt91dc4e4b5OSojh2resQRxb/Lrl1dLd333sa550am/MZEiwX3ai4z0wW0soI7uAB94YWq338f/LGvukp1v/1UFyxQ7d27+FhTpwbeZ+NGd9N0+HAXEM85R3X9+spfZ3n+/FP18cdVTz65+APG+6hRQzU52T1v1sw169SrV3Kbbt1c8P/jj+Jj7tmjOnOmu3G8ZEnkr8GYqhRscLcbqlH24IPw738Hnqf1gANcF8r8fJepcMyYsjMQ/vILHH44XHWVS1NcWAhPPOEG64wbF7g7ZnWg6tI5LF/uxgRkZ7sbsv37w/HHuwyNf//tcupnZbnUAG3bRrvUxlStYG+oWnCvJspKE9ysmUtQ9vHH8Mcfru/8iBGu/3zpJFbXX+8miVi9uuzcNsaY2BRscLf8dtXEYYf5H8EKrjY+a5areWdnu8khpk1zmSm7dXP5x886C+rVc/nmL73UArsxic5q7tXE1KmBc9D4SkuDkSNdP/EvvoDPP3fNGOD6cefnw8qVcMQRES+yMSYKrOYeY8rLQeO1fj3ccosb6DR+vFuXnQ3vvuty2xx7rAV2Y4zV3Kul5s3LDvDgavDZ2VVRGmNMdRJszd0myK6GypvJCVzwt3zwxphALLhXQ8HM5ATF+eAtwBtjSrPgXk15Z3KaMqX8+ViHDLFavDGmJAvu1ZzV4o0xFWHBPQaUNx+rl3dWJ2OMseAeQ+xGqzEmWBbcY0goTTSXXOLyyFigNyYxWXCPMcHeaPUOX7C2eGMSkwX3GBVsLR6sR40xiciCewwL9karl9XijUkcFtzjQDA3Wr2sFm9MYrDgHgdKN9EEMyGH1eKNiW8W3OOEt4lGFSZPtrZ4YxKdBfc4FGyPGi+rxRsTfyy4x7FQe9TY6FZj4ocF9zgXSi3eRrcaEz8suCcIG91qTGKx4J5AKjK69fLLoUEDqFHDgr0xscSCewIKpS1+zx7YssUFfKvVGxM7LLgnqFBHt3r51uot0BtTfQUV3EWkr4isFJHVIjLSz+snichCESkQkQvCX0wTKaGMbi3NkpMZU32VG9xFJAkYD5wOtAEGikibUpv9DAwFpoW7gCayKjK61R8bEGVM9RJMzf04YLWqrlXV3cBrwNm+G6hqtqouBooiUEYTYf5Gt4pAairUrBnasawWb0z1EExwbwL84rOc41kXMhEZJiKZIpKZm5tbkUOYCPMG+qIi2LwZXn459Fq91eKNib5ggru/f2mtyMlUdYKqZqhqRsOGDStyCFPFAuWsCTY5md10NSY6ggnuOUAzn+WmwIbIFMdUZxVJTma9a4yJjmCC+3yglYi0EJGawMXArMgWy1R3oSYnA/+BvkEDGyRlTCSUG9xVtQAYDnwELAdeV9WlIvKAiPQHEJFjRSQHGAA8LyJLI1loU32EMiDKlzfQb9nif5CUBX1jKkdUK9R8XmkZGRmamZkZlXObyJg61fWU2bEj/McWcR8Aqalu+Y8/4LDDXD/9wYPDfz5jqisRWaCqGeVtZyNUTdiEq8+8P+XV9K12b0xJFtxNWFWmd01F2ChZY/yz4G4ipqoD/Y4dcNllrp3e2uxNorPgbqpEWaNgve3o4Qj6hYXuHNZ8YxKdBXdT5UqPgt28OfJB37phmkRjwd1UG1UV9K0bpkkEFtxNtRdM0E9Kqvx5rEeOiScW3E3M8g36kyZVPC99eaxHjolFFtxNXPDtYx+JNnsvy3hpYoUFdxM3ymu+gfAF+kBt9IGeN28O117rflpXTVMVLP2ASShTp8KoUfDzz3DwwW7dli3F6Q2ixXv+tDRLqWDKZukHjPEjGt0wg2FdNU24WXA3huCadKqKddU04WDB3ZgyVCRvfaRYV00TCgvuxgQhkhkvK6u8Jp1gb/jaB0N8seBuTJDKy49T1vO0NLjmmsi36/ur3QfzvPQ3AN+ePRb4Y5SqRuXRuXNnNcaoTpmimpamCqoi7md1e3jLlZrqHiKuzFOmBL4ekZLbB9o3mO1L73vNNcX7BCpHvAIyNYgYa10hjalGqmtXzUBKz5AVSlm924Xj2qIxU5fve1WVs4IF2xXSgrsxMSDWgn514S/oe39/lXnu73fvO1ahXz+YPTsygd+CuzEJwBv016+3QF+dhfObhQ1iMiYBVOYmbzQGayWqQN1YI5mIzoK7MXHC30CsYJ6X/mDw7dkDFvgjaccO980rEiy4G2NKfDBkZ8Mzz1RuWkTva8F+Uyhr+7K6lZZXjljw88+ROa4Fd2NMmULJx+MNvpMnu22C/aZQ1valn4fjwydcvOeozLkOOyw8ZSnNbqgaY+KSvx5G4eotU/qGaOlukd7eMuXd6K5Tx418DuWmqvWWMcaYaiDQh0yke8skV6SwxhhjgjN4cHTy81ubuzHGxCEL7sYYE4csuBtjTByy4G6MMXHIgrsxxsShqHWFFJFcYH0IuzQANkeoONVZIl53Il4zJOZ1J+I1Q+WuO01VG5a3UdSCe6hEJDOYvp3xJhGvOxGvGRLzuhPxmqFqrtuaZYwxJg5ZcDfGmDgUS8F9QrQLECWJeN2JeM2QmNediNcMVXDdMdPmbowxJnixVHM3xhgTJAvuxhgTh2IiuItIXxFZKSKrRWRktMsTCSLSTEQ+F5HlIrJURG7wrD9YRD4RkZ88P+tHu6zhJiJJIvKDiLznWW4hIvM81zxDRGpGu4zhJiIHichMEVnhec+7JMh7fZPn73uJiEwXkVrx9n6LyMsisklElvis8/veivOUJ7YtFpFO4SpHtQ/uIpIEjAdOB9oAA0WkTXRLFREFwC2qejRwAnCd5zpHAnNUtRUwx7Mcb24AlvssPwaM81zzn8AVUSlVZD0JfKiqrYFjcNcf1++1iDQBrgcyVLUdkARcTPy93xOBvqXWBXpvTwdaeR7DgGfDVYhqH9yB44DVqrpWVXcDrwFnR7lMYaeqG1V1oef5Ntw/exPctU7ybDYJOCc6JYwMEWkKnAG86FkW4GRgpmeTeLzmA4CTgJcAVHW3quYR5++1RzJQW0SSgTrARuLs/VbVucAfpVYHem/PBl5V5zvgIBFpFI5yxEJwbwL84rOc41kXt0SkOdARmAccoqobwX0AAP+IXski4j/A7UCRZzkVyFPVAs9yPL7fLYFc4BVPc9SLIlKXOH+vVfVX4HHgZ1xQzwcWEP/vNwR+byMW32IhuPubejZu+2+KyP7Am8CNqro12uWJJBE5E9ikqgt8V/vZNN7e72SgE/CsqnYE/iLOmmD88bQznw20ABoDdXHNEqXF2/tdloj9vcdCcM8BmvksNwU2RKksESUiKbjAPlVV3/Ks/t37Nc3zc1O0yhcBJwL9RSQb19x2Mq4mf5DnazvE5/udA+So6jzP8kxcsI/n9xqgD7BOVXNVdQ/wFtCV+H+/IfB7G7H4FgvBfT7QynNHvSbuBsysKJcp7DxtzS8By1X1CZ+XZgGXeZ5fBrxT1WWLFFW9U1Wbqmpz3Pv6maoOBj4HLvBsFlfXDKCqvwG/iMhRnlW9gWXE8Xvt8TNwgojU8fy9e687rt9vj0Dv7SzgUk+vmROAfG/zTaWparV/AP2AVcAaYFS0yxOha+yG+zq2GMjyPPrh2qDnAD95fh4c7bJG6Pp7Au95nrcEvgdWA28A+0W7fBG43g5Apuf9/i9QPxHea+B+YAWwBJgM7Bdv7zcwHXdPYQ+uZn5FoPcW1ywz3hPbfsT1JApLOSz9gDHGxKFYaJYxxhgTIgvuxhgThyy4G2NMHLLgbowxcciCuzHGxCEL7sYYE4csuBtjTBz6/x3Uo+ybcVWfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs,\n",
    "        smooth_curve(acc), 'bo', label=\"smoothed training acc\")\n",
    "plt.plot(epochs,\n",
    "        smooth_curve(val_acc), 'b', label='Smoothed validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,\n",
    "        smooth_curve(loss), 'bo', label='Smoothed training loss')\n",
    "\n",
    "plt.plot(epochs,\n",
    "        smooth_curve(val_loss), 'b', label='Smoothed validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n",
      "test acc :  0.9069999957084656\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir,\n",
    "    target_size = (150,150),\n",
    "    batch_size=20,\n",
    "    class_mode='binary')\n",
    "\n",
    "test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)\n",
    "print('test acc : ', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflowGPU",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}