BACH_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import applications\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "import keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# path to the model weights files.\n",
    "#weights_path = '../keras/examples/vgg16_weights.h5'\n",
    "#top_model_weights_path = 'fc_model.h5'\n",
    "# dimensions of our images.\n",
    "img_width, img_height = 150, 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_dir = 'data/train'\n",
    "validation_data_dir = 'data/validation'\n",
    "nb_train_samples = 361\n",
    "nb_validation_samples = 39\n",
    "epochs = 10\n",
    "batch_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded.\n"
     ]
    }
   ],
   "source": [
    "# build the VGG16 network\n",
    "base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "print('Model loaded.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_6 (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",
      "sequential_3 (Sequential)    (None, 4)                 2098436   \n",
      "=================================================================\n",
      "Total params: 16,813,124\n",
      "Trainable params: 16,813,124\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "add_model = Sequential()\n",
    "add_model.add(Flatten(input_shape=base_model.output_shape[1:]))\n",
    "add_model.add(Dense(256, activation='relu'))\n",
    "add_model.add(Dense(4, activation='softmax'))\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=add_model(base_model.output))\n",
    "model.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 361 images belonging to 4 classes.\n",
      "Found 39 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# set the first 25 layers (up to the last conv block)\n",
    "# to non-trainable (weights will not be updated)\n",
    "#for layer in model.layers[:25]:\n",
    "#    layer.trainable = False\n",
    "\n",
    "# compile the model with a SGD/momentum optimizer\n",
    "# and a very slow learning rate.\n",
    "\n",
    "# prepare data augmentation configuration\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1. / 255,\n",
    "    horizontal_flip=True)\n",
    "\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='categorical')\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='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/administrator/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: The semantics of the Keras 2 argument `steps_per_epoch` is not the same as the Keras 1 argument `samples_per_epoch`. `steps_per_epoch` is the number of batches to draw from the generator at each epoch. Basically steps_per_epoch = samples_per_epoch/batch_size. Similarly `nb_val_samples`->`validation_steps` and `val_samples`->`steps` arguments have changed. Update your method calls accordingly.\n",
      "  import sys\n",
      "/home/administrator/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., epochs=10, validation_data=<keras.pre..., steps_per_epoch=22, validation_steps=39)`\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "22/22 [==============================] - 591s 27s/step - loss: 1.3785 - acc: 0.3063 - val_loss: 1.3482 - val_acc: 0.4359\n",
      "Epoch 2/10\n",
      "22/22 [==============================] - 580s 26s/step - loss: 1.2013 - acc: 0.5139 - val_loss: 1.2003 - val_acc: 0.4359\n",
      "Epoch 3/10\n",
      "22/22 [==============================] - 594s 27s/step - loss: 1.0925 - acc: 0.5550 - val_loss: 1.2108 - val_acc: 0.4359\n",
      "Epoch 4/10\n",
      "22/22 [==============================] - 601s 27s/step - loss: 0.9696 - acc: 0.5881 - val_loss: 1.1084 - val_acc: 0.4103\n",
      "Epoch 5/10\n",
      " 7/22 [========>.....................] - ETA: 4:27 - loss: 0.9139 - acc: 0.6329"
     ]
    }
   ],
   "source": [
    "# fine-tune the model\n",
    "model.fit_generator(\n",
    "    train_generator,\n",
    "    samples_per_epoch=nb_train_samples,\n",
    "    epochs=epochs,\n",
    "    validation_data=validation_generator,\n",
    "nb_val_samples=nb_validation_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "source": []
  }
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