Test_Transfer_Learning.ipynb

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    {
     "name": "stderr",
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/Mona/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "80142336/80134624 [==============================] - 6s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.vgg19 import (VGG19, preprocess_input, decode_predictions)\n",
    "\n",
    "HEIGHT = 300\n",
    "WIDTH = 300\n",
    "\n",
    "base_model = VGG19(weights='imagenet', \n",
    "                      include_top=False, \n",
    "                      input_shape=(HEIGHT, WIDTH, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Training'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-53592b29eca9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m train_generator = train_datagen.flow_from_directory(TRAIN_DIR, \n\u001b[1;32m     16\u001b[0m                                                     \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHEIGHT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWIDTH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m                                                     batch_size=BATCH_SIZE)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras_preprocessing/image/image_data_generator.py\u001b[0m in \u001b[0;36mflow_from_directory\u001b[0;34m(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation)\u001b[0m\n\u001b[1;32m    538\u001b[0m             \u001b[0mfollow_links\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_links\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m             \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    541\u001b[0m         )\n\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras_preprocessing/image/directory_iterator.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, dtype)\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0msubdir\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m                     \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Training'"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "TRAIN_DIR = \"Training\"\n",
    "HEIGHT = 300\n",
    "WIDTH = 300\n",
    "BATCH_SIZE = 8\n",
    "\n",
    "train_datagen =  ImageDataGenerator(\n",
    "      preprocessing_function=preprocess_input,\n",
    "      rotation_range=90,\n",
    "      horizontal_flip=True,\n",
    "      vertical_flip=True\n",
    "    )\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(TRAIN_DIR, \n",
    "                                                    target_size=(HEIGHT, WIDTH), \n",
    "                                                    batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Activation, Flatten, Dropout\n",
    "from keras.models import Sequential, Model\n",
    "\n",
    "def build_finetune_model(base_model, dropout, fc_layers, num_classes):\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "\n",
    "    x = base_model.output\n",
    "    x = Flatten()(x)\n",
    "    for fc in fc_layers:\n",
    "        # New FC layer, random init\n",
    "        x = Dense(fc, activation='relu')(x) \n",
    "        x = Dropout(dropout)(x)\n",
    "\n",
    "    # New softmax layer\n",
    "    predictions = Dense(num_classes, activation='softmax')(x) \n",
    "    \n",
    "    finetune_model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "    return finetune_model\n",
    "\n",
    "class_list = [\"Pizza\", \"Burger\", \"Taco\"]\n",
    "FC_LAYERS = [1024, 1024]\n",
    "dropout = 0.5\n",
    "\n",
    "finetune_model = build_finetune_model(base_model, \n",
    "                                      dropout=dropout, \n",
    "                                      fc_layers=FC_LAYERS, \n",
    "                                      num_classes=len(class_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.optimizers import SGD, Adam\n",
    "\n",
    "NUM_EPOCHS = 10\n",
    "BATCH_SIZE = 8\n",
    "num_train_images = 10000\n",
    "\n",
    "# adam = Adam(lr=0.00001)\n",
    "# finetune_model.compile(adam, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "filepath=\"./checkpoints/\" + \"ResNet50\" + \"_model_weights.h5\"\n",
    "checkpoint = ModelCheckpoint(filepath, monitor=[\"acc\"], verbose=1, mode='max')\n",
    "callbacks_list = [checkpoint]\n",
    "\n",
    "history = finetune_model.fit_generator(train_generator, epochs=NUM_EPOCHS, workers=8, \n",
    "                                       steps_per_epoch=num_train_images // BATCH_SIZE, \n",
    "                                       shuffle=True, callbacks=callbacks_list)\n",
    "\n",
    "\n",
    "plot_training(history)\n",
    "\n",
    "# Plot the training and validation loss + accuracy\n",
    "def plot_training(history):\n",
    "    acc = history.history['acc']\n",
    "    val_acc = history.history['val_acc']\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    epochs = range(len(acc))\n",
    "\n",
    "    plt.plot(epochs, acc, 'r.')\n",
    "    plt.plot(epochs, val_acc, 'r')\n",
    "    plt.title('Training and validation accuracy')\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, loss, 'r.')\n",
    "    plt.plot(epochs, val_loss, 'r-')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.show()\n",
    "\n",
    "    plt.savefig('acc_vs_epochs.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
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