code/initial-model-comparison/inception-v3_finetune_only_fc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1d9539215c52a8206c18eaebb0d6045087c76d74"
   },
   "source": [
    "# **FINETUNING INCEPTION MODEL FOR DETERMINING RSC**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a3bae869c049172a16bd52f66438861590b9006a"
   },
   "source": [
    "## **Import libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# Libraries for file and data handling\n",
    "%matplotlib inline\n",
    "import os, sys\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import misc\n",
    "import imageio\n",
    "from IPython.display import display\n",
    "from IPython.display import Image as _Imgdis\n",
    "from PIL import Image\n",
    "import pickle\n",
    "from random import shuffle, seed\n",
    "seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "source": [
    "## **Data preparation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the lists detailing the train and test files\n",
    "with open('../input/rwis-data-split/split.pickle', 'rb') as handle:\n",
    "    split_dic = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images in train set:12600\n",
      "Images in test set:1400\n"
     ]
    }
   ],
   "source": [
    "# Obtain the lists\n",
    "train = split_dic['train']\n",
    "print('Images in train set:' + str(len(train)))\n",
    "test = split_dic['test']\n",
    "print('Images in test set:' + str(len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "13c71abcea5bdf919de0cbc0513ec403a6dccaeb"
   },
   "outputs": [],
   "source": [
    "# Image dimensions\n",
    "image_height = 299\n",
    "image_width = 299\n",
    "channels = 3\n",
    "display_images = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "50cb605979f1d34c7f82f744f052785b8c2ed35e"
   },
   "outputs": [],
   "source": [
    "label_names = ['Bare pavement', 'Partial snow coverage', 'Full snow coverage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that prints the total number of images per category in the given set\n",
    "def print_items(list_of_files):\n",
    "    cat1 = 0\n",
    "    cat2 = 0\n",
    "    cat3 = 0\n",
    "    for ifile in list_of_files:\n",
    "        if 'C1' in ifile:\n",
    "            cat1 += 1\n",
    "        elif 'C2' in ifile:\n",
    "            cat2 += 1\n",
    "        elif 'C3' in ifile:\n",
    "            cat3 += 1\n",
    "    total = cat1+cat2+cat3\n",
    "    print ('Total images: ' + str(total))\n",
    "    print (label_names[0] + ' ' + str(cat1) + ' Approx. ' + str(int(cat1/total*100)) + '%')\n",
    "    print (label_names[1] + ' ' + str(cat2) + ' Approx. ' + str(int(cat2/total*100)) + '%')\n",
    "    print (label_names[2] + ' ' + str(cat3) + ' Approx. ' + str(int(cat3/total*100)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Summary of images in the train set -\n",
      "Total images: 12600\n",
      "Bare pavement 5691 Approx. 45%\n",
      "Partial snow coverage 5114 Approx. 40%\n",
      "Full snow coverage 1795 Approx. 14%\n"
     ]
    }
   ],
   "source": [
    "print('- Summary of images in the train set -')\n",
    "print_items(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Summary of images in the test set -\n",
      "Total images: 1400\n",
      "Bare pavement 648 Approx. 46%\n",
      "Partial snow coverage 558 Approx. 39%\n",
      "Full snow coverage 194 Approx. 13%\n"
     ]
    }
   ],
   "source": [
    "print('- Summary of images in the test set -')\n",
    "print_items(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that outputs the list of all files in the directory tree \n",
    "def getListOfFiles(dirName):\n",
    "    # create a list of file and sub directories \n",
    "    # names in the given directory \n",
    "    listOfFile = os.listdir(dirName)\n",
    "    allFiles = list()\n",
    "    # Iterate over all the entries\n",
    "    for entry in listOfFile:\n",
    "        # Create full path\n",
    "        fullPath = os.path.join(dirName, entry)\n",
    "        # If entry is a directory then get the list of files in this directory \n",
    "        if os.path.isdir(fullPath):\n",
    "            allFiles = allFiles + getListOfFiles(fullPath)\n",
    "        else:\n",
    "            if '.jpg' in fullPath :\n",
    "                allFiles.append(fullPath)    \n",
    "    return allFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to use the list of image filenames and import images into numpy X and Y arrays\n",
    "def getArrays(folder, list_of_files):\n",
    "    \n",
    "    # Number of images in the list\n",
    "    numim = len(list_of_files)\n",
    "    # Prepare array for the images\n",
    "    images_array = np.ndarray(shape=(numim, image_height, image_width, channels), dtype=np.float16)\n",
    "    # Prepare array for the labels\n",
    "    labels_array = np.ndarray(shape = numim,dtype=np.uint8)\n",
    "    # Set some counters\n",
    "    idx_files = 0\n",
    "    completion = 20\n",
    "    \n",
    "    # Loop over the filenames\n",
    "    for ifile in list_of_files:\n",
    "        fullpath = folder + ifile\n",
    "        # From filename to numpy array\n",
    "        img_arr = imageio.imread(fullpath)\n",
    "        images_array[idx_files] = img_arr / 255.0\n",
    "        # Obtain label from filename\n",
    "        if '/C1/' in ifile:\n",
    "            label = 0\n",
    "        elif '/C2/' in ifile:\n",
    "            label = 1\n",
    "        elif '/C3/' in ifile:\n",
    "            label = 2\n",
    "        labels_array[idx_files] = label\n",
    "        idx_files += 1\n",
    "        \n",
    "        # Print % of completion\n",
    "        if (idx_files+1) % (numim//5) == 0:\n",
    "            print('Completed ' + str(completion) + '%')\n",
    "            completion += 20\n",
    "\n",
    "    return images_array, labels_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = \"../input/rwis299/rwis299/rwis299\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 20%\n",
      "Completed 40%\n",
      "Completed 60%\n",
      "Completed 80%\n",
      "Completed 100%\n",
      "X_train shape is (12600, 299, 299, 3)\n",
      "y_train shape is (12600,)\n"
     ]
    }
   ],
   "source": [
    "# Import the corresponding images into numpy arrays \n",
    "X_train, y_train = getArrays(folder, train)\n",
    "print('X_train shape is ' + str(X_train.shape))\n",
    "print('y_train shape is ' + str(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 20%\n",
      "Completed 40%\n",
      "Completed 60%\n",
      "Completed 80%\n",
      "Completed 100%\n",
      "X_test shape is (1400, 299, 299, 3)\n",
      "y_test shape is (1400,)\n"
     ]
    }
   ],
   "source": [
    "# Import the corresponding images into numpy arrays \n",
    "X_test, y_test = getArrays(folder, test)\n",
    "print('X_test shape is ' + str(X_test.shape))\n",
    "print('y_test shape is ' + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "5daef90de684ecfa27ed8ba4a5671289478cf95c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ER-14/C2/ER-Hwy401_Curry_Hill-p3-1515260700.jpg\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 299,
       "width": 299
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ER-32/C3/ER-Hwy416_Rideau_River-p3-1515841200.jpg\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 299,
       "width": 299
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display some images from the train set\n",
    "if display_images:    \n",
    "    for i in range(0, 2):\n",
    "        print(train[i])\n",
    "        display(_Imgdis(filename=folder+train[i], width = image_width, height = image_height))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "7066ebd841717f1ab687c34c0472d4bcafbc1e32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/CR-19/C2/CR-Hwy401_near_Newtonville_Rd-p1-1513948500.jpg\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 299,
       "width": 299
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/NER-14/C1/NER-Hwy11_Marten_River-p1-1515527100.jpg\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 299,
       "width": 299
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display some images from the test set\n",
    "if display_images:\n",
    "    for i in range(0, 2):\n",
    "        print(test[i])\n",
    "        display(_Imgdis(filename=folder+test[i], width = image_width, height = image_height))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fc0783ecde5a919142cf4fe58aa99069fe1ee6c5"
   },
   "source": [
    "## **Model preparation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "0625cf058cd9028498ca6145bb6b97292ae4e386"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import model specific libraries\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "02b9c493c03a60b67f95e61a25e2f530ecee9607"
   },
   "outputs": [],
   "source": [
    "# Import other Keras libraries\n",
    "from time import time\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Input\n",
    "from keras.constraints import maxnorm\n",
    "from keras import optimizers\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "9774c3a243fd80ebbf9b9f7b4bdc2363e3ef774f"
   },
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "ac3fb849084961bb762a3aca87d9d299b834a319"
   },
   "outputs": [],
   "source": [
    "# Convert class vectors to binary class matrices.\n",
    "num_classes = 3\n",
    "y_train = to_categorical(y_train, num_classes)\n",
    "y_test = to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "5e890731b227abf09f640194b84c8bd8e094538a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12600, 3)\n",
      "(1400, 3)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1d40bb5f260c834e62b34f545b07c634ec4cdebd"
   },
   "source": [
    "**Defining the CNN architecture**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "f5e906a3795d47dfe497565112c5dbaaadf84881"
   },
   "outputs": [],
   "source": [
    "#  Import the base model for transfer learning\n",
    "weights_source = '../input/inceptionv3weights/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "69a8d3bc79cb17f6641048b040813c6c11b5420d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/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"
     ]
    }
   ],
   "source": [
    "# create the base pre-trained model\n",
    "base_model = InceptionV3(weights=weights_source, include_top=False, input_shape=(image_height,image_width,channels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "f8adb9a96c69ee7d27c679f771be62035e41f6da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "# Use output from base model\n",
    "x = base_model.output\n",
    "# Use same \"head\" or classifier as the baseline model\n",
    "x = Dropout(0.5)(x)\n",
    "x = Flatten()(x)\n",
    "x = Dense(48, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(24, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(3, activation='softmax')(x)\n",
    "\n",
    "# this is the model we will train\n",
    "model = Model(inputs=base_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_model = False\n",
    "if view_model:\n",
    "    print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c7fbcf10e60c48fb76767c63cdd54817e6c1d1a4"
   },
   "source": [
    "## **Model training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "3de91cb2c51d5c9427e9599bea6455b6202879b3"
   },
   "outputs": [],
   "source": [
    "# In this case only train the \"head\", or classifier\n",
    "# Set all layers in the base model as not trainable\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# compile the model (should be done *after* setting layers to non-trainable)\n",
    "opt = optimizers.SGD(lr=0.001, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "6a9709e9b473a784320d14466991c08933f262e7"
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "7412eaa4056ce1decdb87d3e35b69e7574795fac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 10080 samples, validate on 2520 samples\n",
      "Epoch 1/50\n",
      "10080/10080 [==============================] - 64s 6ms/step - loss: 1.0532 - acc: 0.4470 - val_loss: 1.0120 - val_acc: 0.4484\n",
      "Epoch 2/50\n",
      "10080/10080 [==============================] - 62s 6ms/step - loss: 1.0070 - acc: 0.4461 - val_loss: 1.0056 - val_acc: 0.4540\n",
      "Epoch 3/50\n",
      "10080/10080 [==============================] - 62s 6ms/step - loss: 1.0012 - acc: 0.4537 - val_loss: 0.9909 - val_acc: 0.5238\n",
      "Epoch 4/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.9763 - acc: 0.5020 - val_loss: 0.9394 - val_acc: 0.5952\n",
      "Epoch 5/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.9486 - acc: 0.5314 - val_loss: 0.9432 - val_acc: 0.5484\n",
      "Epoch 6/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.9244 - acc: 0.5548 - val_loss: 0.8785 - val_acc: 0.6401\n",
      "Epoch 7/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.8981 - acc: 0.5770 - val_loss: 0.8824 - val_acc: 0.6202\n",
      "Epoch 8/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.8748 - acc: 0.5974 - val_loss: 0.9094 - val_acc: 0.6020\n",
      "Epoch 9/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.8538 - acc: 0.6108 - val_loss: 0.9277 - val_acc: 0.5750\n",
      "Epoch 10/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.8321 - acc: 0.6255 - val_loss: 0.8490 - val_acc: 0.6452\n",
      "Epoch 11/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.8202 - acc: 0.6317 - val_loss: 1.0092 - val_acc: 0.5619\n",
      "Epoch 12/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7986 - acc: 0.6389 - val_loss: 0.8381 - val_acc: 0.6516\n",
      "Epoch 13/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7946 - acc: 0.6486 - val_loss: 0.8997 - val_acc: 0.6278\n",
      "Epoch 14/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7926 - acc: 0.6440 - val_loss: 0.9111 - val_acc: 0.6167\n",
      "Epoch 15/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7835 - acc: 0.6539 - val_loss: 0.8728 - val_acc: 0.6349\n",
      "Epoch 16/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.7774 - acc: 0.6559 - val_loss: 0.9659 - val_acc: 0.5905\n",
      "Epoch 17/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7649 - acc: 0.6615 - val_loss: 0.7982 - val_acc: 0.6702\n",
      "Epoch 18/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7741 - acc: 0.6620 - val_loss: 0.8001 - val_acc: 0.6651\n",
      "Epoch 19/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7586 - acc: 0.6672 - val_loss: 0.8007 - val_acc: 0.6532\n",
      "Epoch 20/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7584 - acc: 0.6649 - val_loss: 0.8623 - val_acc: 0.6310\n",
      "Epoch 21/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7313 - acc: 0.6728 - val_loss: 0.8581 - val_acc: 0.6421\n",
      "Epoch 22/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7335 - acc: 0.6626 - val_loss: 0.7709 - val_acc: 0.6837\n",
      "Epoch 23/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.7157 - acc: 0.6654 - val_loss: 1.0815 - val_acc: 0.5909\n",
      "Epoch 24/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.7106 - acc: 0.6735 - val_loss: 1.0062 - val_acc: 0.6115\n",
      "Epoch 25/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.6950 - acc: 0.6748 - val_loss: 0.7940 - val_acc: 0.6750\n",
      "Epoch 26/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.6908 - acc: 0.6731 - val_loss: 0.9890 - val_acc: 0.6060\n",
      "Epoch 27/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.6871 - acc: 0.6728 - val_loss: 0.9398 - val_acc: 0.6282\n",
      "Epoch 28/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.6810 - acc: 0.6790 - val_loss: 0.8284 - val_acc: 0.6595\n",
      "Epoch 29/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.6847 - acc: 0.6726 - val_loss: 0.7792 - val_acc: 0.6810\n",
      "Epoch 30/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.6741 - acc: 0.6774 - val_loss: 0.7877 - val_acc: 0.6877\n",
      "Epoch 31/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.6726 - acc: 0.6762 - val_loss: 0.7257 - val_acc: 0.6984\n",
      "Epoch 32/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.6593 - acc: 0.6882 - val_loss: 0.7803 - val_acc: 0.6778\n",
      "Epoch 33/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.6535 - acc: 0.6846 - val_loss: 0.7182 - val_acc: 0.6944\n",
      "Epoch 34/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.6477 - acc: 0.6877 - val_loss: 0.8349 - val_acc: 0.6718\n",
      "Epoch 35/50\n",
      "10080/10080 [==============================] - 61s 6ms/step - loss: 0.6374 - acc: 0.6900 - val_loss: 0.7297 - val_acc: 0.7036\n",
      "Epoch 36/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.6370 - acc: 0.6887 - val_loss: 0.7189 - val_acc: 0.7083\n",
      "Epoch 37/50\n",
      "10080/10080 [==============================] - 60s 6ms/step - loss: 0.6355 - acc: 0.6889 - val_loss: 0.8337 - val_acc: 0.6687\n",
      "Epoch 38/50\n",
      " 7520/10080 [=====================>........] - ETA: 12s - loss: 0.6350 - acc: 0.6892"
     ]
    }
   ],
   "source": [
    "train_history = model.fit(x=X_train, y=y_train, batch_size=batch_size, \n",
    "                      epochs=epochs, verbose=1,\n",
    "                      validation_split = 0.2,\n",
    "                      shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1d81de93d2d68d345dd6cae77f1a4d42bf2e71e4"
   },
   "source": [
    "## **Evaluate results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "d55dedb470f835ed7035e4322d659ddfd12a8ebc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the values from the training history dictionary\n",
    "train_loss = train_history.history['loss'] \n",
    "val_loss = train_history.history['val_loss']\n",
    "\n",
    "# Plot the resulting loss\n",
    "plt.plot(range(1,1+len(train_loss)), train_loss)\n",
    "plt.plot(range(1,1+len(val_loss)), val_loss)\n",
    "plt.xticks(np.arange(1,1+len(train_loss),5))\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([1,len(train_loss)])\n",
    "axes.set_ylim([0,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "fce403719398d79f1b7b4ae6bbac23425684954e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the values from the training history dictionary\n",
    "train_acc = train_history.history['acc'] \n",
    "val_acc = train_history.history['val_acc']\n",
    "\n",
    "# Plot the resulting loss\n",
    "plt.plot(range(1,1+len(train_acc)), train_acc)\n",
    "plt.plot(range(1,1+len(val_acc)), val_acc)\n",
    "plt.xticks(np.arange(1,1+len(train_acc),5))\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([1,len(train_acc)])\n",
    "axes.set_ylim([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Evaluation over the train and validation datasets--\n",
      "Final train loss: 0.5618\n",
      "Final validation loss: 0.8879\n",
      "Final train accuracy:  72.71%\n",
      "Final validation accuracy:  68.65%\n"
     ]
    }
   ],
   "source": [
    "print(\"--Evaluation over the train and validation datasets--\")\n",
    "print(\"Final train loss: \"+'{:6.4f}'.format(train_loss[-1]))   \n",
    "print(\"Final validation loss: \"+'{:6.4f}'.format(val_loss[-1]))   \n",
    "print(\"Final train accuracy: \"+'{:6.2f}'.format(train_acc[-1]*100)+'%')\n",
    "print(\"Final validation accuracy: \"+'{:6.2f}'.format(val_acc[-1]*100)+'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "ffeb167fbd2d68ce727ef8fea4a10d3b012ee999"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Evaluation over the test dataset--\n",
      "Loss: 0.8257\n",
      "Accuracy:  69.14%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"--Evaluation over the test dataset--\")\n",
    "print(\"Loss: \"+'{:6.4f}'.format(scores[0]))   \n",
    "print(\"Accuracy: \"+'{:6.2f}'.format(scores[1]*100)+'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8e1409de9f31a89246dc65c90c2b2c1958e2f975"
   },
   "source": [
    "### Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "b26ca9da7ba2470026f28595d91ab559cb1cd49d"
   },
   "outputs": [],
   "source": [
    "inceptionv3history_dic = {'train_loss':train_loss,\n",
    "                    'val_loss':val_loss,\n",
    "                     'train_acc':train_acc,\n",
    "                      'val_acc':val_acc}\n",
    "\n",
    "with open('inceptionv3history_only_head.pickle', 'wb') as handle:\n",
    "    pickle.dump(inceptionv3history_dic, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  }
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