基于Alexnet卷积神经元网络的人脸识别系统搭建.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras.utils import to_categorical\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras import backend as K\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_width, img_height = 150, 150\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    input_shape = (3, img_width, img_height)\n",
    "else:\n",
    "    input_shape = (img_width, img_height, 3)\n",
    "    \n",
    "    \n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), input_shape=input_shape))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 148, 148, 32)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 72, 72, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 72, 72, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 34, 34, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 34, 34, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 18496)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                1183808   \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 1,212,513\n",
      "Trainable params: 1,212,513\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 203 images belonging to 2 classes.\n",
      "Found 103 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_dir = r'C:\\Users\\MOLE\\Desktop\\graduate\\data\\train'\n",
    "validation_data_dir = r'C:\\Users\\MOLE\\Desktop\\graduate\\data\\validation'\n",
    "nb_train_samples = 203\n",
    "nb_validation_samples = 103\n",
    "epochs = 100\n",
    "batch_size = 20\n",
    "\n",
    "\n",
    "# this is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1. / 255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "# this is the augmentation configuration we will use for testing:\n",
    "# only rescaling\n",
    "test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_data_dir,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    validation_data_dir,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='binary')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "10/10 [==============================] - 3s 283ms/step - loss: 0.2042 - accuracy: 0.9836 - val_loss: 0.0282 - val_accuracy: 0.9800\n",
      "Epoch 2/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 0.1211 - accuracy: 0.9891 - val_loss: 0.0346 - val_accuracy: 0.9518\n",
      "Epoch 3/100\n",
      "10/10 [==============================] - 3s 277ms/step - loss: 0.0967 - accuracy: 0.9781 - val_loss: 5.0227e-06 - val_accuracy: 0.9880\n",
      "Epoch 4/100\n",
      "10/10 [==============================] - 3s 276ms/step - loss: 1.2380e-04 - accuracy: 1.0000 - val_loss: 4.8020e-08 - val_accuracy: 0.9639\n",
      "Epoch 5/100\n",
      "10/10 [==============================] - 3s 282ms/step - loss: 0.3668 - accuracy: 0.9672 - val_loss: 0.0439 - val_accuracy: 0.9759\n",
      "Epoch 6/100\n",
      "10/10 [==============================] - 3s 269ms/step - loss: 0.2413 - accuracy: 0.9891 - val_loss: 0.0048 - val_accuracy: 0.9639\n",
      "Epoch 7/100\n",
      "10/10 [==============================] - 3s 296ms/step - loss: 0.0318 - accuracy: 0.9950 - val_loss: 5.8418e-06 - val_accuracy: 0.9700\n",
      "Epoch 8/100\n",
      "10/10 [==============================] - 3s 276ms/step - loss: 0.2083 - accuracy: 0.9727 - val_loss: 0.2377 - val_accuracy: 0.9639\n",
      "Epoch 9/100\n",
      "10/10 [==============================] - 3s 280ms/step - loss: 0.0843 - accuracy: 0.9891 - val_loss: 0.0265 - val_accuracy: 0.9759\n",
      "Epoch 10/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 0.0419 - accuracy: 0.9891 - val_loss: 0.3347 - val_accuracy: 0.9639\n",
      "Epoch 11/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 0.0774 - accuracy: 0.9836 - val_loss: 0.0011 - val_accuracy: 0.9880\n",
      "Epoch 12/100\n",
      "10/10 [==============================] - 3s 266ms/step - loss: 0.1058 - accuracy: 0.9836 - val_loss: 0.0477 - val_accuracy: 0.9639\n",
      "Epoch 13/100\n",
      "10/10 [==============================] - 3s 288ms/step - loss: 0.0506 - accuracy: 0.9891 - val_loss: 0.1906 - val_accuracy: 0.9700\n",
      "Epoch 14/100\n",
      "10/10 [==============================] - 3s 291ms/step - loss: 0.0651 - accuracy: 0.9891 - val_loss: 0.1717 - val_accuracy: 0.9759\n",
      "Epoch 15/100\n",
      "10/10 [==============================] - 3s 302ms/step - loss: 0.0877 - accuracy: 0.9750 - val_loss: 0.0210 - val_accuracy: 0.9880\n",
      "Epoch 16/100\n",
      "10/10 [==============================] - 3s 273ms/step - loss: 0.0718 - accuracy: 0.9836 - val_loss: 0.0064 - val_accuracy: 0.9518\n",
      "Epoch 17/100\n",
      "10/10 [==============================] - 3s 275ms/step - loss: 0.0618 - accuracy: 0.9945 - val_loss: 0.8565 - val_accuracy: 0.9518\n",
      "Epoch 18/100\n",
      "10/10 [==============================] - 3s 272ms/step - loss: 0.0464 - accuracy: 0.9836 - val_loss: 1.6784e-04 - val_accuracy: 0.9880\n",
      "Epoch 19/100\n",
      "10/10 [==============================] - 3s 257ms/step - loss: 0.0294 - accuracy: 0.9880 - val_loss: 7.0663e-04 - val_accuracy: 0.9700\n",
      "Epoch 20/100\n",
      "10/10 [==============================] - 3s 295ms/step - loss: 0.0258 - accuracy: 0.9850 - val_loss: 0.0031 - val_accuracy: 0.9759\n",
      "Epoch 21/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 0.0543 - accuracy: 0.9836 - val_loss: 0.4985 - val_accuracy: 0.9518\n",
      "Epoch 22/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 0.0295 - accuracy: 0.9836 - val_loss: 0.0018 - val_accuracy: 0.9880\n",
      "Epoch 23/100\n",
      "10/10 [==============================] - 3s 271ms/step - loss: 0.0226 - accuracy: 0.9836 - val_loss: 0.4675 - val_accuracy: 0.9639\n",
      "Epoch 24/100\n",
      "10/10 [==============================] - 3s 296ms/step - loss: 0.0327 - accuracy: 0.9850 - val_loss: 1.6576 - val_accuracy: 0.9759\n",
      "Epoch 25/100\n",
      "10/10 [==============================] - 3s 277ms/step - loss: 0.0109 - accuracy: 0.9945 - val_loss: 7.9311e-06 - val_accuracy: 0.9700\n",
      "Epoch 26/100\n",
      "10/10 [==============================] - 3s 266ms/step - loss: 0.0327 - accuracy: 0.9819 - val_loss: 0.1627 - val_accuracy: 0.9639\n",
      "Epoch 27/100\n",
      "10/10 [==============================] - 3s 295ms/step - loss: 0.0048 - accuracy: 0.9950 - val_loss: 7.2241e-08 - val_accuracy: 0.9880\n",
      "Epoch 28/100\n",
      "10/10 [==============================] - 3s 273ms/step - loss: 0.0429 - accuracy: 0.9781 - val_loss: 0.3333 - val_accuracy: 0.9518\n",
      "Epoch 29/100\n",
      "10/10 [==============================] - 3s 262ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.0737e-06 - val_accuracy: 0.9880\n",
      "Epoch 30/100\n",
      "10/10 [==============================] - 3s 271ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 1.1333e-04 - val_accuracy: 0.9639\n",
      "Epoch 31/100\n",
      "10/10 [==============================] - 3s 336ms/step - loss: 0.0060 - accuracy: 0.9950 - val_loss: 0.6902 - val_accuracy: 0.9700\n",
      "Epoch 32/100\n",
      "10/10 [==============================] - 3s 273ms/step - loss: 0.0142 - accuracy: 0.9891 - val_loss: 1.1587 - val_accuracy: 0.9639\n",
      "Epoch 33/100\n",
      "10/10 [==============================] - 3s 271ms/step - loss: 0.0534 - accuracy: 0.9836 - val_loss: 0.0063 - val_accuracy: 1.0000\n",
      "Epoch 34/100\n",
      "10/10 [==============================] - 3s 281ms/step - loss: 0.0080 - accuracy: 0.9945 - val_loss: 5.7921e-08 - val_accuracy: 0.9398\n",
      "Epoch 35/100\n",
      "10/10 [==============================] - 3s 283ms/step - loss: 0.0133 - accuracy: 0.9891 - val_loss: 0.4789 - val_accuracy: 0.9759\n",
      "Epoch 36/100\n",
      "10/10 [==============================] - 3s 284ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 3.0381e-09 - val_accuracy: 0.9759\n",
      "Epoch 37/100\n",
      "10/10 [==============================] - 3s 280ms/step - loss: 0.0060 - accuracy: 0.9945 - val_loss: 4.6972e-11 - val_accuracy: 0.9700\n",
      "Epoch 38/100\n",
      "10/10 [==============================] - 3s 278ms/step - loss: 0.0624 - accuracy: 0.9891 - val_loss: 0.6536 - val_accuracy: 0.9639\n",
      "Epoch 39/100\n",
      "10/10 [==============================] - 3s 294ms/step - loss: 0.0095 - accuracy: 0.9950 - val_loss: 0.8800 - val_accuracy: 0.9759\n",
      "Epoch 40/100\n",
      "10/10 [==============================] - 3s 282ms/step - loss: 0.0041 - accuracy: 0.9945 - val_loss: 1.0500 - val_accuracy: 0.9518\n",
      "Epoch 41/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 0.0141 - accuracy: 0.9945 - val_loss: 1.1926 - val_accuracy: 0.9759\n",
      "Epoch 42/100\n",
      "10/10 [==============================] - 3s 293ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 1.9997e-13 - val_accuracy: 0.9880\n",
      "Epoch 43/100\n",
      "10/10 [==============================] - 3s 284ms/step - loss: 7.4118e-05 - accuracy: 1.0000 - val_loss: 0.8382 - val_accuracy: 0.9700\n",
      "Epoch 44/100\n",
      "10/10 [==============================] - 3s 290ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 2.5192e-06 - val_accuracy: 0.9759\n",
      "Epoch 45/100\n",
      "10/10 [==============================] - 3s 288ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 5.6839e-08 - val_accuracy: 0.9639\n",
      "Epoch 46/100\n",
      "10/10 [==============================] - 3s 304ms/step - loss: 0.0088 - accuracy: 0.9945 - val_loss: 1.9879 - val_accuracy: 0.9759\n",
      "Epoch 47/100\n",
      "10/10 [==============================] - 3s 303ms/step - loss: 0.0064 - accuracy: 0.9945 - val_loss: 0.4041 - val_accuracy: 0.9518\n",
      "Epoch 48/100\n",
      "10/10 [==============================] - 3s 318ms/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 2.9825e-11 - val_accuracy: 0.9759\n",
      "Epoch 49/100\n",
      "10/10 [==============================] - 3s 323ms/step - loss: 0.0719 - accuracy: 0.9891 - val_loss: 0.0952 - val_accuracy: 0.9600\n",
      "Epoch 50/100\n",
      "10/10 [==============================] - 3s 313ms/step - loss: 0.0075 - accuracy: 0.9945 - val_loss: 6.5369e-04 - val_accuracy: 0.9759\n",
      "Epoch 51/100\n",
      "10/10 [==============================] - 3s 328ms/step - loss: 0.0179 - accuracy: 0.9950 - val_loss: 0.0094 - val_accuracy: 0.9518\n",
      "Epoch 52/100\n",
      "10/10 [==============================] - 3s 308ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 4.1116e-04 - val_accuracy: 0.9759\n",
      "Epoch 53/100\n",
      "10/10 [==============================] - 3s 285ms/step - loss: 0.0067 - accuracy: 0.9945 - val_loss: 0.0034 - val_accuracy: 0.9880\n",
      "Epoch 54/100\n",
      "10/10 [==============================] - 3s 278ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.4728e-09 - val_accuracy: 0.9639\n",
      "Epoch 55/100\n",
      "10/10 [==============================] - 3s 313ms/step - loss: 0.0071 - accuracy: 0.9945 - val_loss: 0.6602 - val_accuracy: 0.9700\n",
      "Epoch 56/100\n",
      "10/10 [==============================] - 3s 312ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.9739 - val_accuracy: 0.9639\n",
      "Epoch 57/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 3s 276ms/step - loss: 0.0079 - accuracy: 0.9945 - val_loss: 1.7982e-07 - val_accuracy: 0.9880\n",
      "Epoch 58/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 7.6726e-05 - accuracy: 1.0000 - val_loss: 2.2364e-14 - val_accuracy: 0.9639\n",
      "Epoch 59/100\n",
      "10/10 [==============================] - 3s 273ms/step - loss: 0.0074 - accuracy: 0.9945 - val_loss: 0.5803 - val_accuracy: 0.9639\n",
      "Epoch 60/100\n",
      "10/10 [==============================] - 3s 291ms/step - loss: 0.0353 - accuracy: 0.9900 - val_loss: 0.0687 - val_accuracy: 0.9759\n",
      "Epoch 61/100\n",
      "10/10 [==============================] - 3s 282ms/step - loss: 0.0123 - accuracy: 0.9945 - val_loss: 0.1344 - val_accuracy: 0.9600\n",
      "Epoch 62/100\n",
      "10/10 [==============================] - 3s 270ms/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 8.7148e-08 - val_accuracy: 0.9759\n",
      "Epoch 63/100\n",
      "10/10 [==============================] - 3s 270ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 8.9904e-04 - val_accuracy: 0.9518\n",
      "Epoch 64/100\n",
      "10/10 [==============================] - 3s 253ms/step - loss: 0.0418 - accuracy: 0.9880 - val_loss: 5.9772e-05 - val_accuracy: 0.9759\n",
      "Epoch 65/100\n",
      "10/10 [==============================] - 3s 292ms/step - loss: 1.6631e-04 - accuracy: 1.0000 - val_loss: 1.1890 - val_accuracy: 0.9518\n",
      "Epoch 66/100\n",
      "10/10 [==============================] - 3s 265ms/step - loss: 0.0109 - accuracy: 0.9891 - val_loss: 4.6070e-16 - val_accuracy: 0.9880\n",
      "Epoch 67/100\n",
      "10/10 [==============================] - 3s 290ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 1.2712e-16 - val_accuracy: 0.9700\n",
      "Epoch 68/100\n",
      "10/10 [==============================] - 3s 279ms/step - loss: 0.0142 - accuracy: 0.9945 - val_loss: 0.6216 - val_accuracy: 0.9639\n",
      "Epoch 69/100\n",
      "10/10 [==============================] - 3s 263ms/step - loss: 0.0040 - accuracy: 0.9940 - val_loss: 0.6728 - val_accuracy: 0.9639\n",
      "Epoch 70/100\n",
      "10/10 [==============================] - 3s 295ms/step - loss: 0.0081 - accuracy: 0.9950 - val_loss: 2.1289e-13 - val_accuracy: 0.9639\n",
      "Epoch 71/100\n",
      "10/10 [==============================] - 3s 264ms/step - loss: 0.0036 - accuracy: 0.9940 - val_loss: 3.2905e-07 - val_accuracy: 0.9759\n",
      "Epoch 72/100\n",
      "10/10 [==============================] - 3s 269ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 2.0552e-14 - val_accuracy: 0.9518\n",
      "Epoch 73/100\n",
      "10/10 [==============================] - 3s 301ms/step - loss: 0.0121 - accuracy: 0.9950 - val_loss: 3.4275e-20 - val_accuracy: 0.9700\n",
      "Epoch 74/100\n",
      "10/10 [==============================] - 3s 272ms/step - loss: 0.0166 - accuracy: 0.9945 - val_loss: 1.5337e-09 - val_accuracy: 0.9639\n",
      "Epoch 75/100\n",
      "10/10 [==============================] - 3s 272ms/step - loss: 0.0053 - accuracy: 0.9945 - val_loss: 1.0007 - val_accuracy: 0.9518\n",
      "Epoch 76/100\n",
      "10/10 [==============================] - 3s 275ms/step - loss: 7.1036e-05 - accuracy: 1.0000 - val_loss: 0.6683 - val_accuracy: 0.9759\n",
      "Epoch 77/100\n",
      "10/10 [==============================] - 3s 273ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.4260 - val_accuracy: 0.9759\n",
      "Epoch 78/100\n",
      "10/10 [==============================] - 3s 264ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 3.4863e-21 - val_accuracy: 0.9759\n",
      "Epoch 79/100\n",
      "10/10 [==============================] - 3s 277ms/step - loss: 1.3752e-05 - accuracy: 1.0000 - val_loss: 2.1432e-09 - val_accuracy: 0.9700\n",
      "Epoch 80/100\n",
      "10/10 [==============================] - 3s 292ms/step - loss: 0.0049 - accuracy: 0.9950 - val_loss: 1.4692 - val_accuracy: 0.9639\n",
      "Epoch 81/100\n",
      "10/10 [==============================] - 3s 253ms/step - loss: 0.0288 - accuracy: 0.9880 - val_loss: 0.7431 - val_accuracy: 0.9639\n",
      "Epoch 82/100\n",
      "10/10 [==============================] - 3s 293ms/step - loss: 0.0146 - accuracy: 0.9950 - val_loss: 0.9528 - val_accuracy: 0.9639\n",
      "Epoch 83/100\n",
      "10/10 [==============================] - 3s 259ms/step - loss: 7.9318e-05 - accuracy: 1.0000 - val_loss: 2.9623 - val_accuracy: 0.9639\n",
      "Epoch 84/100\n",
      "10/10 [==============================] - 3s 290ms/step - loss: 0.0068 - accuracy: 0.9950 - val_loss: 1.4097e-20 - val_accuracy: 0.9759\n",
      "Epoch 85/100\n",
      "10/10 [==============================] - 3s 276ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 2.7051 - val_accuracy: 0.9700\n",
      "Epoch 86/100\n",
      "10/10 [==============================] - 2s 249ms/step - loss: 4.8893e-05 - accuracy: 1.0000 - val_loss: 3.2690e-21 - val_accuracy: 0.9639\n",
      "Epoch 87/100\n",
      "10/10 [==============================] - 3s 295ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 2.4655 - val_accuracy: 0.9759\n",
      "Epoch 88/100\n",
      "10/10 [==============================] - 3s 273ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 1.4614 - val_accuracy: 0.9759\n",
      "Epoch 89/100\n",
      "10/10 [==============================] - 3s 270ms/step - loss: 0.0175 - accuracy: 0.9945 - val_loss: 0.4327 - val_accuracy: 0.9639\n",
      "Epoch 90/100\n",
      "10/10 [==============================] - 3s 272ms/step - loss: 0.0104 - accuracy: 0.9945 - val_loss: 2.2567e-22 - val_accuracy: 0.9759\n",
      "Epoch 91/100\n",
      "10/10 [==============================] - 3s 298ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.8507 - val_accuracy: 0.9700\n",
      "Epoch 92/100\n",
      "10/10 [==============================] - 3s 267ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 8.2393e-22 - val_accuracy: 0.9759\n",
      "Epoch 93/100\n",
      "10/10 [==============================] - 3s 274ms/step - loss: 0.1486 - accuracy: 0.9945 - val_loss: 1.3766 - val_accuracy: 0.9518\n",
      "Epoch 94/100\n",
      "10/10 [==============================] - 3s 273ms/step - loss: 9.2013e-05 - accuracy: 1.0000 - val_loss: 2.5038e-11 - val_accuracy: 0.9880\n",
      "Epoch 95/100\n",
      "10/10 [==============================] - 3s 276ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.9258 - val_accuracy: 0.9398\n",
      "Epoch 96/100\n",
      "10/10 [==============================] - 3s 287ms/step - loss: 0.0069 - accuracy: 0.9950 - val_loss: 0.0000e+00 - val_accuracy: 0.9639\n",
      "Epoch 97/100\n",
      "10/10 [==============================] - 3s 285ms/step - loss: 5.7950e-04 - accuracy: 1.0000 - val_loss: 1.1808 - val_accuracy: 0.9700\n",
      "Epoch 98/100\n",
      "10/10 [==============================] - 3s 271ms/step - loss: 0.0060 - accuracy: 0.9945 - val_loss: 3.2158 - val_accuracy: 0.9759\n",
      "Epoch 99/100\n",
      "10/10 [==============================] - 3s 283ms/step - loss: 0.0499 - accuracy: 0.9891 - val_loss: 1.0623e-13 - val_accuracy: 0.9759\n",
      "Epoch 100/100\n",
      "10/10 [==============================] - 3s 271ms/step - loss: 0.0035 - accuracy: 0.9945 - val_loss: 1.3025e-08 - val_accuracy: 0.9398\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x29f2327c240>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=nb_train_samples // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=nb_validation_samples // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.]]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.resize(cv2.imread(r'C:/Users/MOLE/Desktop/graduate/data/train/others/1.jpg'), (img_width, img_height)).astype(np.float32)\n",
    "\n",
    "#img = img.transpose((2,0,1))\n",
    "x = img_to_array(img)\n",
    "\n",
    "x = np.expand_dims(x, axis=0)\n",
    "\n",
    "#x = preprocess_input(x)\n",
    "\n",
    "score = model.predict(x)\n",
    "\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.]]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.resize(cv2.imread(r'C:/Users/MOLE/Desktop/graduate/data/train/myself/2.jpg'), (img_width, img_height)).astype(np.float32)\n",
    "\n",
    "#img = img.transpose((2,0,1))\n",
    "x = img_to_array(img)\n",
    "\n",
    "x = np.expand_dims(x, axis=0)\n",
    "\n",
    "#x = preprocess_input(x)\n",
    "\n",
    "score = model.predict(x)\n",
    "\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}