ERCNN.ipynb 133 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "sufficient-michigan",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import pathlib\n",
    "import pandas as pd\n",
    "import pydot\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "alert-phone",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       angry  fear  surprise   sad  neutral  happy\n",
      "train   3995  4097      3171  4830     4965   7215\n",
      "      angry  fear  surprise   sad  neutral  happy\n",
      "test    958  1024       831  1247     1233   1774\n"
     ]
    }
   ],
   "source": [
    "train_dir = './fer2013/train/'\n",
    "test_dir = './fer2013/test/'\n",
    "\n",
    "row, col = 48, 48\n",
    "classes = 7\n",
    "\n",
    "def count_exp(path, set_):\n",
    "    dict_={}\n",
    "    for expression in os.listdir(path):\n",
    "        dir_ = path + expression\n",
    "        dict_[expression] = len(os.listdir(dir_))\n",
    "    df = pd.DataFrame(dict_, index=[set_])\n",
    "    return df\n",
    "train_count = count_exp(train_dir, 'train')\n",
    "test_count = count_exp(test_dir, 'test')\n",
    "print(train_count)\n",
    "print(test_count)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ranking-today",
   "metadata": {},
   "source": [
    "### Plot of number of images in train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "induced-journal",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mk7Q/cByw0vYm208AK4E5ndyYiIh4cUPq05c0FTgc+BkwyfYjZdGjwKQyPRlY2/KydaVtoPaIiOiStkNf0kuBy4EP2/5V6zLbBtyJgiTNl7RK0qoNGzZ04i0jIqJoK/QljaEK/G/a/ofS/FjptqE8P17a1wMHtLx8SmkbqP15bC+xPdP2zIkTJw5lWyIiYhDtjN4RcBFwj+3PtSxaAfSNwJkHLG9pP6OM4pkFbCndQFcDsyWNLwdwZ5e2iIjokt3aWOdo4H3AHZJuLW0fA84HLpN0FvAwcGpZdiVwArAGeAo4E8D2JknnATeV9c61vakTGxEREe0ZNPRt/wugARYf28/6BhYM8F5LgaVDKTAiIjonZ+RGRDRIQj8iokES+hERDZLQj4hokIR+RESDJPQjIhokoR8R0SAJ/YiIBknoR0Q0SEI/IqJBEvoREQ2S0I+IaJCEfkREgyT0IyIaJKEfEdEgCf2IiAZJ6EdENEhCPyKiQRL6ERENktCPiGiQQUNf0lJJj0u6s6VtX0krJa0uz+NLuyQtlrRG0u2Sjmh5zbyy/mpJ84ZncyIi4sW0s6f/dWDOdm0LgWttTweuLfMAxwPTy2M+cCFUXxLAIuAo4EhgUd8XRUREdM+goW/7x8Cm7ZrnAsvK9DLgpJb2i125ARgnaX/gOGCl7U22nwBW8sIvkoiIGGY72qc/yfYjZfpRYFKZngysbVlvXWkbqD0iIrpopw/k2jbgDtQCgKT5klZJWrVhw4ZOvW1ERLDjof9Y6bahPD9e2tcDB7SsN6W0DdT+AraX2J5pe+bEiRN3sLyIiOjPjob+CqBvBM48YHlL+xllFM8sYEvpBroamC1pfDmAO7u0RUREF+022AqSvgW8FdhP0jqqUTjnA5dJOgt4GDi1rH4lcAKwBngKOBPA9iZJ5wE3lfXOtb39weGIiBhmg4a+7fcOsOjYftY1sGCA91kKLB1SdRER0VE5IzciokES+hERDZLQj4hokIR+RESDJPQjIhokoR8R0SAJ/YiIBknoR0Q0SEI/IqJBEvoREQ2S0I+IaJCEfkREgyT0IyIaJKEfEdEgCf2IiAZJ6EdENEhCPyKiQRL6ERENktCPiGiQhH5ERIN0PfQlzZF0n6Q1khZ2+/MjIpqsq6EvaVfg/wDHA4cA75V0SDdriIhosm7v6R8JrLH9oO3fApcCc7tcQ0REY8l29z5MOhmYY/vPy/z7gKNsf7BlnfnA/DJ7MHBf1wqE/YBfdvHzui3bN7r18vb18rZB97fvlbYn9rdgty4W0RbbS4AldXy2pFW2Z9bx2d2Q7Rvdenn7ennbYGRtX7e7d9YDB7TMTyltERHRBd0O/ZuA6ZKmSdodOA1Y0eUaIiIaq6vdO7a3SfogcDWwK7DU9l3drGEQtXQrdVG2b3Tr5e3r5W2DEbR9XT2QGxER9coZuRERDZLQj4hokIR+RESDJPQjIhqk8aEv6UOSxtddR6dJ2lXSvXXXMZwkTZJ0kaSryvwhks6qu66dJekOSbcP9Ki7vmjPSM2WEXdGbg0mATdJugVYClztHhjSZPvZcjXTA23/a931DJOvA18D/nuZvx/4NnBRXQV1yDvL84Ly/I3yfHoNtXScpCeB/v6PCbDtl3W5pOEyIrMlQzYBSQJmA2cCM4HLgItsP1BrYTtJ0o+Bw4Ebgd/0tdt+V21FdZCkm2y/WdLPbR9e2m61PaPm0jqidbta2m6xfURdNcXQjMRsyZ4+1a6FpEeBR4FtwHjgO5JW2v7reqvbKR+vu4Bh9htJEyh7jZJmAVvqLamjJOlo2z8pM39ID3bJSno5MLZvvpd+mY7EbGn8nr6ks4EzqK6A91Xge7afkbQLsNr2QbUWGAOSdATwBeBQ4E5gInCy7Z7o95b0JqpugX2ouj6eAP7M9i21FtYhkt4F/C/gFcDjwCuBe2y/vtbCOmSkZkv29Ktv3nfbfri10fbvJL1zgNeMCmXP9wvA64DdqS598Zte6TO1fYukt1BdglvAfbafqbmsjrF9M3CYpH3KfC/9igE4D5gFXGP7cElvA/6k5po6aV9GYLY0ek+/3MnrLtuvrbuW4SBpFdVF7f6eqj/xDOA1ts+ptbAOkXQK8APbT0r6H8ARwCd7ZU8YQNKJwOt5fvfHufVV1Dl9lxuWdBtweAnD22wfVndtnVJ+jR5D1QX5k5Hwb7Pn+geHwvazwH2SDqy7luFiew2wq+1nbX8NmFN3TR308RL4xwDHUo3aubDmmjpG0peA9wAfovolcwpVF0iv2CzppcCPgW9KuoCWAQejnaSPA8uACVQ3Ufla2TmpVaP39KG3R7iUbfuPVP2JjwKPAH/aK3tSfaNbJP0dcIftS/ob8TJaSbrd9htbnl8KXGX739ddWydI2gv4f1Q7n6dTHbv4pu2NtRbWIZLuAw6zvbXM7wncavvgOutKn35vj3B5H9V/qA8C/5XqBjZ/XGtFnbVe0peBdwCflrQHvfXrdWt5fkrSK4BNwP411tMxpWv1+7bfBvyOao+41/wbVbdc39/jHoyAm0Y1PvRt/3PdNQwX2w+XvYv9bf9N3fUMg1Opuqs+a3uzpP2B/1ZzTZ30j5LGAZ8BbqHqF/5KrRV1SDl58HeS9unBA9R9tgB3SVpJ9Xf3DuBGSYsBbP9lHUU1PvQHODtwC7AK+CvbD3a/qs6Q9EfAZ6lG7kyTNAM4d7R3XUl6me1fUe1FXVfa9gWepvp76xX3As/avlzSIVQHqr9Xb0kd9WvgjhKKrV2rtYThMPhuefS5rqY6nid9+tJ5wDrgEqqDZacBB1HtWb3f9lvrq27nSLoZeDtwXcsZq3fYfkO9le0cSd+3/U5Jv6D6wlbLYtt+VU2ldVRLX/4xVMMbPwt8wvZRNZfWEZLm9dNs2xd3vZhhUm4L+1qqf6f32f5tzSVlTx9413YHNpeUU/k/KuljtVXVGc/Y3lKdCf6cUf8tXwJfwFt66ezNfjxbnk8EvmL7CkmfrLOgDhtn+4LWhnJCU0+QdALwZeABqh2TaZL+wvZVddbVSwe9dtRTkk6VtEt5nMrvD7yM9oC8S9J/BnaVNF3SF4Dr6y6qE8qFq66ou45h1neg+j3AlT14oLq/Pf0/7XYRw+hzwNtsv9X2W4C3AZ+vuaae+ge0o06nGuXyOPBYmf6TcgD0g3UWtqMk9V2V8QGqE3ueBr4F/Ar4cE1lDYdbJL257iKG0anA1cBxtjdTneE56g9US3qvpH+k2vNd0fL4EdUIpV7xZDlPps+DwJN1FdOn8X36vUjS3VTj86+i2rt4Hts98R9L1f0CXg08THUgsO/SvG+stbB4UZJeCUwD/g5Y2LLoSeB229tqKazDJF1IdTLdZVS9BqcA/wpcA2D7H2qpq+mhL2ki8F+AqbQc47D9Z3XVtLMk/SXwfuBVPH9ccF8o9sqBzn7PTt3+WicRdZD0tRdZ7LoyJqEvXQ/8X+Bmfn/gDNuX11ZUh0i60Pb7665jOI3Ea5tEe7YbLr07MIYeuiDgSJXQ76GbbjSNpE9Q/WTu+5l8EvD3tntphEsjlNFYc4FZthcOtv5oIGkscBYvvGBerb0IOZAL3y9Dq2L0OR14s+1FthdRXab3fTXXFDvAle8Bx9VdSwd9A/h3VNv0z8AURsCB3IzTh7OBj0l6GniG3rtPZy8bkdc2ifZIenfL7C5Ul//eOsDqo9GrbZ8iaa7tZZIuoepKrlXjQ9/23uUU/um0/ASLUWFEXtsk2vZHLdPbgIeounh6Rd8NfTZLOpTqSrcvr7EeIH36SPpzqr39KcCtVF0E19s+ts66YnADnMb/HNu9eOXGGCVKtlwOvAH4OvBSqntAfLnWuhL6ugN4M3CD7RmSXgv8re13D/LSqFG5NO/Ftk+vu5bYMZJeQ3XTm0m2D5X0RqrLovTEgfhyBvUfUw0HH1OaXfedz3IgF7a23ORgD9v3Ut1zNUawctezV5YLWsXo9BXgHEo3iKsb2p9Wa0WdtZyqu2ob1RVFf80IuDNY4/v0gXXlmuXfA1ZKeoLqDM8Y+R4EfiJpBc+/NO/n6isphuAltm/c7oKAPXE2bjHF9oi7PWnjQ9/2fyqT/7Nc+2Mf4Ac1lhTte6A8dgH2rrmWGLpfSjqIcoKWpJOpbunZK66X9Abbd9RdSKvG9+lHRD0kvQpYAvwh8ATwC+D00X4ZjXKc0FQ71dOpfpE+zQi5NlRCP0at8svsBf+Abb+9hnJiiMqBzpOpDnTuS3UV2NoPdO6sga4J1afuL7XGd+/EqPaRlumxVCMleqlPuNctBzZT3aXu3+otpXPqDvXBZE8/eoqkG20fWXcdMThJd9o+tO46miZ7+jFqlTOp+/Sdxr9PTeXE0I3IA529Lnv6MWptd2P0Z6hO4z/X9r/UWVe0p9zs59VUB3BHzIHOXpc9/RjNPgr8wPavJH0cOAJ4quaaon3H111AE2VPP0YtSbfbfqOkY4DzgM8Cn7B9VM2lRYxYuQxDjGZ9dzo7EfiK7Suo7sAUEQNI6Mdotl7Sl4H3AFeWcd/5Nx3xItK9E6OWpJcAc4A7bK+WtD/wBtv/VHNpESNWQj8iokHyUzgiokES+hERDZLQj4hokIR+RESDJPQjIhrk/wPmc92z9NCNjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_count.transpose().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "multiple-paraguay",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 48, 3)\n",
      "(48, 48, 3)\n",
      "(48, 48, 3)\n",
      "(48, 48, 3)\n",
      "(48, 48, 3)\n",
      "(48, 48, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1584 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 6, figsize=(14, 22))\n",
    "i = 0\n",
    "for expression in os.listdir(train_dir):\n",
    "    img = load_img((train_dir + expression + '/' + os.listdir(train_dir + expression)[1]))\n",
    "    axs[i].imshow(img)\n",
    "    axs[i].set_title(expression)\n",
    "    axs[i].axis('off')\n",
    "    print(np.array(img).shape) ## (48, 48, 3)\n",
    "    i += 1\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charming-submission",
   "metadata": {},
   "source": [
    "### Creating Training and test sets\n",
    "\n",
    "ImageDataGenerator를 만들 시 rescale, shear_range, zoom_range, horizontal_flip를 설정해줌으로써 해당 데이터의 전처리를 어떻게 할지를 정해준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fossil-jungle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22619 images belonging to 6 classes.\n",
      "Found 5654 images belonging to 6 classes.\n",
      "Found 7067 images belonging to 6 classes.\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "train_generator = ImageDataGenerator(rescale=1./255, \n",
    "                                     validation_split=0.2)  \n",
    "test_generator = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_dir = './fer2013/train'\n",
    "test_dir = './fer2013/test'\n",
    "\n",
    "\n",
    "train_set = train_generator.flow_from_directory(train_dir,\n",
    "                                                 batch_size=64,\n",
    "                                                 shuffle=True,\n",
    "                                                 target_size=(48, 48), \n",
    "                                                 subset=\"training\",\n",
    "                                                 color_mode='grayscale', \n",
    "                                                 class_mode='categorical')\n",
    "\n",
    "validation_set = train_generator.flow_from_directory(train_dir,\n",
    "                                                 batch_size=64,\n",
    "                                                 shuffle=True,\n",
    "                                                 target_size=(48, 48), \n",
    "                                                 subset=\"validation\",\n",
    "                                                 color_mode='grayscale',\n",
    "                                                 class_mode='categorical')\n",
    "\n",
    "test_set = test_generator.flow_from_directory(test_dir,\n",
    "                                              batch_size=64,\n",
    "                                              shuffle=True,\n",
    "                                              target_size=(48, 48),\n",
    "                                              color_mode='grayscale',\n",
    "                                              class_mode='categorical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "extensive-ocean",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tensorflow.python.keras.preprocessing.image.DirectoryIterator object at 0x7f221c0707f0>\n",
      "[0 0 0 ... 5 5 5]\n",
      "{'angry': 0, 'fear': 1, 'happy': 2, 'neutral': 3, 'sad': 4, 'surprise': 5}\n"
     ]
    }
   ],
   "source": [
    "print(train_set)\n",
    "print(train_set.classes)\n",
    "print(train_set.class_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "minor-roommate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_13\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_44 (Conv2D)           (None, 48, 48, 64)        640       \n",
      "_________________________________________________________________\n",
      "batch_normalization_20 (Batc (None, 48, 48, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_44 (MaxPooling (None, 24, 24, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_45 (Conv2D)           (None, 24, 24, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_21 (Batc (None, 24, 24, 128)       512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_45 (MaxPooling (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_46 (Conv2D)           (None, 12, 12, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_22 (Batc (None, 12, 12, 128)       512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_46 (MaxPooling (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_47 (Conv2D)           (None, 6, 6, 256)         295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_23 (Batc (None, 6, 6, 256)         1024      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_47 (MaxPooling (None, 3, 3, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_13 (Flatten)         (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 1000)              2305000   \n",
      "_________________________________________________________________\n",
      "dropout_26 (Dropout)         (None, 1000)              0         \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 100)               100100    \n",
      "_________________________________________________________________\n",
      "dropout_27 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 6)                 606       \n",
      "=================================================================\n",
      "Total params: 2,925,258\n",
      "Trainable params: 2,924,106\n",
      "Non-trainable params: 1,152\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(keras.layers.Conv2D(64, kernel_size=3, activation='relu', padding='same', input_shape=(48, 48, 1)))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(keras.layers.MaxPool2D(2))\n",
    "model.add(keras.layers.Conv2D(128, kernel_size=3, activation='relu', padding='same'))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(keras.layers.MaxPool2D(2))\n",
    "model.add(keras.layers.Conv2D(128, kernel_size=3, activation='relu', padding='same'))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(keras.layers.MaxPool2D(2))\n",
    "model.add(keras.layers.Conv2D(256, kernel_size=3, activation='relu', padding='same'))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(keras.layers.MaxPool2D(2))\n",
    "model.add(keras.layers.Flatten())\n",
    "model.add(keras.layers.Dense(1000, activation='relu'))\n",
    "model.add(keras.layers.Dropout(0.4))\n",
    "model.add(keras.layers.Dense(100, activation='relu'))\n",
    "model.add(keras.layers.Dropout(0.4))\n",
    "model.add(keras.layers.Dense(6, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "structured-interview",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Failed to import pydot. You must `pip install pydot` and install graphviz (https://graphviz.gitlab.io/download/), ', 'for `pydotprint` to work.')\n"
     ]
    }
   ],
   "source": [
    "keras.utils.plot_model(model, show_shapes=True, to_file='emotionRecognition_CNN.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "judicial-fleece",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "354/354 [==============================] - 10s 26ms/step - loss: 1.9681 - accuracy: 0.2380 - val_loss: 1.7464 - val_accuracy: 0.2552\n",
      "Epoch 2/60\n",
      "354/354 [==============================] - 9s 26ms/step - loss: 1.6678 - accuracy: 0.2863 - val_loss: 1.5923 - val_accuracy: 0.3214\n",
      "Epoch 3/60\n",
      "354/354 [==============================] - 9s 27ms/step - loss: 1.5676 - accuracy: 0.3480 - val_loss: 1.6141 - val_accuracy: 0.3479\n",
      "Epoch 4/60\n",
      "354/354 [==============================] - 9s 27ms/step - loss: 1.4546 - accuracy: 0.4060 - val_loss: 1.3717 - val_accuracy: 0.4466\n",
      "Epoch 5/60\n",
      "354/354 [==============================] - 9s 26ms/step - loss: 1.3678 - accuracy: 0.4408 - val_loss: 1.5732 - val_accuracy: 0.3845\n",
      "Epoch 6/60\n",
      "354/354 [==============================] - 10s 27ms/step - loss: 1.3038 - accuracy: 0.4674 - val_loss: 1.5171 - val_accuracy: 0.3750\n",
      "Epoch 7/60\n",
      "354/354 [==============================] - 9s 27ms/step - loss: 1.2260 - accuracy: 0.5038 - val_loss: 1.3009 - val_accuracy: 0.4961\n",
      "Epoch 8/60\n",
      "354/354 [==============================] - 9s 26ms/step - loss: 1.1547 - accuracy: 0.5378 - val_loss: 1.5893 - val_accuracy: 0.3300\n",
      "Epoch 9/60\n",
      "354/354 [==============================] - 9s 26ms/step - loss: 1.0838 - accuracy: 0.5680 - val_loss: 1.2040 - val_accuracy: 0.5409\n",
      "Epoch 10/60\n",
      "354/354 [==============================] - 9s 26ms/step - loss: 0.9956 - accuracy: 0.6054 - val_loss: 1.3680 - val_accuracy: 0.5129\n",
      "Epoch 11/60\n",
      "354/354 [==============================] - 9s 27ms/step - loss: 0.9237 - accuracy: 0.6448 - val_loss: 1.3021 - val_accuracy: 0.5486\n",
      "Epoch 12/60\n",
      "354/354 [==============================] - 9s 26ms/step - loss: 0.8164 - accuracy: 0.6861 - val_loss: 1.5032 - val_accuracy: 0.5294\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics='accuracy')\n",
    "checkpoint_cb = keras.callbacks.ModelCheckpoint('er-best-cnn-model.h5')\n",
    "earlystopping_cb = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n",
    "history = model.fit(train_set, epochs=60, validation_data= validation_set, \n",
    "                   callbacks=[checkpoint_cb, earlystopping_cb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "prime-general",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title(\"model accuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Accuracy\",\"Validation Loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}