transfer_learning_CNN_SGD_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# goal:\n",
    "# To do transfer learning using pre-trained model VGG16\n",
    "\n",
    "# The lower level featured learned by VGG16 on imagenet are still applicable to other problems with natural images. \n",
    "# If we can preserve the lower-level features, we can just train a new model on those features.\n",
    "# That is, we load VGG16 model, snip off last layer, and replace it with our own final layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "# Import libraries and packages\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import SGD\n",
    "from imutils import paths\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import argparse\n",
    "import random\n",
    "import pickle\n",
    "import cv2\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading images...\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "# initialize the data and labels\n",
    "print(\"[INFO] loading images...\")\n",
    "images = []\n",
    "labels = []\n",
    "\n",
    "# grab the image paths and randomly shuffle them\n",
    "imagePaths = sorted(list(paths.list_images('dataset')))\n",
    "random.seed(42)\n",
    "random.shuffle(imagePaths)\n",
    "\n",
    "# loop over the input images\n",
    "for imagePath in imagePaths:\n",
    "    # load the image, resize it to 64x64 pixels (the required input spatial dimensions of SmallVGGNet), \n",
    "    # and store the image in the data list\n",
    "    image = cv2.imread(imagePath)\n",
    "    image = cv2.resize(image, (64, 64))   # we are not flattening our data for neural network, because it is convolutional\n",
    "    images.append(image)\n",
    "\n",
    "    # extract the class label from the image path and update the labels list\n",
    "    label = imagePath.split(os.path.sep)[-2]\n",
    "    labels.append(label)\n",
    "\n",
    "# scale the raw pixel intensities to the range [0, 1]\n",
    "images = np.array(images, dtype=\"float\") / 255.0\n",
    "labels = np.array(labels)\n",
    "\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# partition the data into 75% training and 25% validation\n",
    "(trainX, testX, trainY, testY) = train_test_split(images, labels, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "         [0.90196078, 0.94117647, 0.96862745],\n",
       "         ...,\n",
       "         [0.91372549, 0.89019608, 0.92156863],\n",
       "         [0.96862745, 0.94901961, 0.95686275],\n",
       "         [0.96078431, 0.94901961, 0.95686275]],\n",
       "\n",
       "        [[0.84705882, 0.90980392, 0.94509804],\n",
       "         [0.87058824, 0.92941176, 0.96470588],\n",
       "         [0.87843137, 0.91764706, 0.95686275],\n",
       "         ...,\n",
       "         [0.88235294, 0.86666667, 0.89803922],\n",
       "         [0.94901961, 0.9372549 , 0.94509804],\n",
       "         [0.93333333, 0.92156863, 0.92941176]]],\n",
       "\n",
       "\n",
       "       [[[0.0627451 , 0.09411765, 0.18431373],\n",
       "         [0.14117647, 0.23921569, 0.36862745],\n",
       "         [0.01960784, 0.1372549 , 0.22352941],\n",
       "         ...,\n",
       "         [0.64705882, 0.70980392, 0.96470588],\n",
       "         [0.84313725, 0.88627451, 0.99607843],\n",
       "         [0.26666667, 0.31372549, 0.38431373]],\n",
       "\n",
       "        [[0.09019608, 0.15686275, 0.29411765],\n",
       "         [0.2745098 , 0.35686275, 0.49019608],\n",
       "         [0.23137255, 0.27843137, 0.4       ],\n",
       "         ...,\n",
       "         [0.64705882, 0.76078431, 0.98039216],\n",
       "         [0.84705882, 0.90588235, 0.99607843],\n",
       "         [0.25490196, 0.30980392, 0.38431373]],\n",
       "\n",
       "        [[0.08235294, 0.23137255, 0.4       ],\n",
       "         [0.27058824, 0.36078431, 0.47843137],\n",
       "         [0.24313725, 0.30588235, 0.42745098],\n",
       "         ...,\n",
       "         [0.66666667, 0.80392157, 1.        ],\n",
       "         [0.85882353, 0.92941176, 0.99607843],\n",
       "         [0.23137255, 0.29411765, 0.36470588]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.18039216, 0.27058824, 0.4       ],\n",
       "         [0.18823529, 0.2745098 , 0.38431373],\n",
       "         [0.21568627, 0.30588235, 0.42352941],\n",
       "         ...,\n",
       "         [0.2745098 , 0.33333333, 0.42352941],\n",
       "         [0.04705882, 0.        , 0.        ],\n",
       "         [0.03921569, 0.00392157, 0.00392157]],\n",
       "\n",
       "        [[0.14117647, 0.2627451 , 0.39215686],\n",
       "         [0.19215686, 0.27843137, 0.41176471],\n",
       "         [0.18823529, 0.29803922, 0.41568627],\n",
       "         ...,\n",
       "         [0.14901961, 0.19607843, 0.28235294],\n",
       "         [0.05882353, 0.00392157, 0.00784314],\n",
       "         [0.02352941, 0.        , 0.        ]],\n",
       "\n",
       "        [[0.15686275, 0.2745098 , 0.40392157],\n",
       "         [0.19607843, 0.28627451, 0.40784314],\n",
       "         [0.14117647, 0.29411765, 0.42352941],\n",
       "         ...,\n",
       "         [0.12941176, 0.18431373, 0.25882353],\n",
       "         [0.05098039, 0.        , 0.00392157],\n",
       "         [0.02352941, 0.00392157, 0.00392157]]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1672, 64, 64, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Messy_Room', 'Clean_Room', 'Clean_Room', ..., 'Bad_Excuse',\n",
       "       'Messy_Room', 'Messy_Room'], dtype='<U10')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the labels from integers/categories to vectors \n",
    "# (for 2-class, binary classification you should use Keras' to_categorical function instead)\n",
    "lb = LabelBinarizer()\n",
    "trainY = lb.fit_transform(trainY)   # fit_transform = find all unique class labels + transform into one-hot encoded labels\n",
    "testY = lb.transform(testY)         # transform = perform the one-hot encoding (unique class labels already found)\n",
    "\n",
    "# This is the categorical vector after transformation\n",
    "# [1, 0, 0] # corresponds to cat\n",
    "# [0, 1, 0] # corresponds to dog\n",
    "# [0, 0, 1] # corresponds to panda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 0],\n",
       "       ...,\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning - define neural network architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.imagenet_utils import decode_predictions\n",
    "from keras.layers import Flatten, Dense, Dropout\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3733: 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": [
    "# note we exclude the final dense layers and add one back below, so that we could retrain it ourselves\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(64,64,3))   # data images are resized to 64x64\n",
    "\n",
    "# Freeze convolutional layers\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False    \n",
    "    \n",
    "x = base_model.output\n",
    "x = Flatten()(x) # flatten from convolution tensor output \n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(32, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(3, activation='softmax')(x) # should match number of classes predicted\n",
    "\n",
    "# this is the new model we will train\n",
    "model = Model(inputs=base_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] training network...\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\keras\\optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\regina\\Desktop\\Metis\\Metis Projects\\Spark\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Epoch 1/75\n",
      "52/52 [==============================] - 28s 537ms/step - loss: 0.9758 - acc: 0.5030 - val_loss: 0.5720 - val_acc: 0.8082\n",
      "Epoch 2/75\n",
      "52/52 [==============================] - 32s 614ms/step - loss: 0.6706 - acc: 0.6833 - val_loss: 0.4333 - val_acc: 0.8656\n",
      "Epoch 3/75\n",
      "52/52 [==============================] - 34s 652ms/step - loss: 0.5599 - acc: 0.7302 - val_loss: 0.4109 - val_acc: 0.8459\n",
      "Epoch 4/75\n",
      "52/52 [==============================] - 34s 648ms/step - loss: 0.5281 - acc: 0.7578 - val_loss: 0.3907 - val_acc: 0.8746\n",
      "Epoch 5/75\n",
      "52/52 [==============================] - 36s 693ms/step - loss: 0.4695 - acc: 0.8005 - val_loss: 0.3227 - val_acc: 0.8907\n",
      "Epoch 6/75\n",
      "52/52 [==============================] - 35s 673ms/step - loss: 0.4322 - acc: 0.8011 - val_loss: 0.3200 - val_acc: 0.8602\n",
      "Epoch 7/75\n",
      "52/52 [==============================] - 35s 674ms/step - loss: 0.4311 - acc: 0.7975 - val_loss: 0.2952 - val_acc: 0.8996\n",
      "Epoch 8/75\n",
      "52/52 [==============================] - 35s 669ms/step - loss: 0.3938 - acc: 0.8245 - val_loss: 0.2809 - val_acc: 0.8907\n",
      "Epoch 9/75\n",
      "52/52 [==============================] - 36s 692ms/step - loss: 0.3731 - acc: 0.8563 - val_loss: 0.2665 - val_acc: 0.8907\n",
      "Epoch 10/75\n",
      "52/52 [==============================] - 36s 699ms/step - loss: 0.3879 - acc: 0.8468 - val_loss: 0.2882 - val_acc: 0.8817\n",
      "Epoch 11/75\n",
      "52/52 [==============================] - 38s 728ms/step - loss: 0.3775 - acc: 0.8228 - val_loss: 0.2551 - val_acc: 0.8943\n",
      "Epoch 12/75\n",
      "52/52 [==============================] - 37s 719ms/step - loss: 0.3472 - acc: 0.8587 - val_loss: 0.2614 - val_acc: 0.8907\n",
      "Epoch 13/75\n",
      "52/52 [==============================] - 38s 740ms/step - loss: 0.3551 - acc: 0.8504 - val_loss: 0.2613 - val_acc: 0.8853\n",
      "Epoch 14/75\n",
      "52/52 [==============================] - 40s 770ms/step - loss: 0.3486 - acc: 0.8672 - val_loss: 0.2450 - val_acc: 0.9032\n",
      "Epoch 15/75\n",
      "52/52 [==============================] - 39s 749ms/step - loss: 0.3700 - acc: 0.8384 - val_loss: 0.2486 - val_acc: 0.8978\n",
      "Epoch 16/75\n",
      "52/52 [==============================] - 40s 774ms/step - loss: 0.3404 - acc: 0.8576 - val_loss: 0.2804 - val_acc: 0.8817\n",
      "Epoch 17/75\n",
      "52/52 [==============================] - 40s 764ms/step - loss: 0.3554 - acc: 0.8419 - val_loss: 0.2534 - val_acc: 0.8961\n",
      "Epoch 18/75\n",
      "52/52 [==============================] - 41s 786ms/step - loss: 0.3237 - acc: 0.8726 - val_loss: 0.2400 - val_acc: 0.9032\n",
      "Epoch 19/75\n",
      "52/52 [==============================] - 42s 815ms/step - loss: 0.3207 - acc: 0.8762 - val_loss: 0.2390 - val_acc: 0.9032\n",
      "Epoch 20/75\n",
      "52/52 [==============================] - 44s 838ms/step - loss: 0.3164 - acc: 0.8708 - val_loss: 0.2346 - val_acc: 0.9032\n",
      "Epoch 21/75\n",
      "52/52 [==============================] - 45s 867ms/step - loss: 0.3143 - acc: 0.8720 - val_loss: 0.2559 - val_acc: 0.8799\n",
      "Epoch 22/75\n",
      "52/52 [==============================] - 47s 899ms/step - loss: 0.2889 - acc: 0.8798 - val_loss: 0.2335 - val_acc: 0.9068\n",
      "Epoch 23/75\n",
      "52/52 [==============================] - 47s 911ms/step - loss: 0.3156 - acc: 0.8762 - val_loss: 0.2427 - val_acc: 0.9050\n",
      "Epoch 24/75\n",
      "52/52 [==============================] - 49s 942ms/step - loss: 0.3164 - acc: 0.8726 - val_loss: 0.2316 - val_acc: 0.9032\n",
      "Epoch 25/75\n",
      "52/52 [==============================] - 52s 1s/step - loss: 0.2955 - acc: 0.8816 - val_loss: 0.2312 - val_acc: 0.8978\n",
      "Epoch 26/75\n",
      "52/52 [==============================] - 53s 1s/step - loss: 0.2875 - acc: 0.8833 - val_loss: 0.2337 - val_acc: 0.8925\n",
      "Epoch 27/75\n",
      "52/52 [==============================] - 56s 1s/step - loss: 0.3117 - acc: 0.8744 - val_loss: 0.2327 - val_acc: 0.9068\n",
      "Epoch 28/75\n",
      "52/52 [==============================] - 53s 1s/step - loss: 0.3140 - acc: 0.8714 - val_loss: 0.2473 - val_acc: 0.8907\n",
      "Epoch 29/75\n",
      "52/52 [==============================] - 52s 1s/step - loss: 0.3035 - acc: 0.8678 - val_loss: 0.2439 - val_acc: 0.9122\n",
      "\n",
      "Epoch 00029: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
      "Epoch 30/75\n",
      "52/52 [==============================] - 52s 1s/step - loss: 0.2829 - acc: 0.8900 - val_loss: 0.2313 - val_acc: 0.9014\n",
      "Epoch 31/75\n",
      "52/52 [==============================] - 55s 1s/step - loss: 0.2798 - acc: 0.8912 - val_loss: 0.2346 - val_acc: 0.9050\n",
      "Epoch 32/75\n",
      "52/52 [==============================] - 57s 1s/step - loss: 0.2795 - acc: 0.8936 - val_loss: 0.2335 - val_acc: 0.8978\n",
      "Epoch 33/75\n",
      "52/52 [==============================] - 51s 983ms/step - loss: 0.2824 - acc: 0.8889 - val_loss: 0.2338 - val_acc: 0.8996\n",
      "\n",
      "Epoch 00033: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
      "Epoch 34/75\n",
      "52/52 [==============================] - 51s 972ms/step - loss: 0.2804 - acc: 0.8817 - val_loss: 0.2406 - val_acc: 0.9068\n",
      "Epoch 35/75\n",
      "52/52 [==============================] - 54s 1s/step - loss: 0.2690 - acc: 0.8882 - val_loss: 0.2331 - val_acc: 0.9014\n",
      "Epoch 36/75\n",
      "52/52 [==============================] - 57s 1s/step - loss: 0.2526 - acc: 0.8978 - val_loss: 0.2330 - val_acc: 0.9068\n",
      "Restoring model weights from the end of the best epoch\n",
      "Epoch 00036: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Construct & initialize the image data generator for data augmentation\n",
    "# Image augmentation allows us to construct “additional” training data from our existing training data \n",
    "# by randomly rotating, shifting, shearing, zooming, and flipping. This is to avoid overfitting.\n",
    "aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,\n",
    "                         height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,\n",
    "                         horizontal_flip=True, fill_mode=\"nearest\")\n",
    "\n",
    "# compile & train model\n",
    "# initialize our initial learning rate, # of epochs to train for, and batch size\n",
    "INIT_LR = 0.01\n",
    "EPOCHS = 75\n",
    "BS = 32\n",
    "\n",
    "# initialize the model and optimizer (you'll want to use binary_crossentropy for 2-class classification)\n",
    "print(\"[INFO] training network...\")\n",
    "opt = SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS, momentum=0.2)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n",
    "\n",
    "# train the network\n",
    "H = model.fit_generator( aug.flow(trainX, trainY, batch_size=BS),\n",
    "                        validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS, epochs=EPOCHS,\n",
    "                        callbacks=[keras.callbacks.EarlyStopping(patience=11, verbose=1, restore_best_weights=True),\n",
    "                                   keras.callbacks.ReduceLROnPlateau(factor=.5, patience=4, verbose=1)] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] evaluating network...\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Bad_Excuse       0.99      0.99      0.99       160\n",
      "  Clean_Room       0.87      0.90      0.88       236\n",
      "  Messy_Room       0.85      0.80      0.83       162\n",
      "\n",
      "    accuracy                           0.90       558\n",
      "   macro avg       0.90      0.90      0.90       558\n",
      "weighted avg       0.90      0.90      0.90       558\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# evaluate the network\n",
    "print(\"[INFO] evaluating network...\")\n",
    "predictions = model.predict(testX, batch_size=32)\n",
    "print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1), target_names=lb.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e50067cd88>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the training and validation loss\n",
    "# N = np.arange(0, EPOCHS) \n",
    "N = np.arange(0, 36) # based on EPOCH where early stopping occurs. otherwise take the EPOCH max\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure(figsize = [8,6])\n",
    "plt.plot(N, H.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(N, H.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.title(\"Training & Validation Loss (Transfer Learning with VGG16)\")\n",
    "plt.xlabel(\"Epoch #\", weight='bold')\n",
    "plt.ylabel(\"Loss\", weight='bold')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e500702c48>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the training and validation accuracy\n",
    "# N = np.arange(0, EPOCHS) \n",
    "N = np.arange(0, 36) # based on EPOCH where early stopping occurs. otherwise take the EPOCH max\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure(figsize = [8,6])\n",
    "plt.plot(N, H.history[\"acc\"], label=\"train_acc\")\n",
    "plt.plot(N, H.history[\"val_acc\"], label=\"val_acc\")\n",
    "plt.title(\"Training and Validation Accuracy (Transfer Learning with VGG16)\")\n",
    "plt.xlabel(\"Epoch #\", weight='bold')\n",
    "plt.ylabel(\"Accuracy\", weight='bold')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 64, 64, 3)         0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               524544    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                8224      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 99        \n",
      "=================================================================\n",
      "Total params: 15,247,555\n",
      "Trainable params: 532,867\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] serializing network and label binarizer...\n"
     ]
    }
   ],
   "source": [
    "# save the model and label binarizer to disk\n",
    "print(\"[INFO] serializing network and label binarizer...\")\n",
    "model.save('vgg_CNN_trsf_learn_early32_model.h5')\n",
    "f = open('label_bin_trsf_learn_early32.env', \"wb\")\n",
    "f.write(pickle.dumps(lb))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions on new data using transfering learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the necessary packages\n",
    "from keras.models import load_model\n",
    "import pickle\n",
    "import cv2\n",
    "import PIL\n",
    "\n",
    "# load the input image and resize it to the target spatial dimensions\n",
    "width = 64\n",
    "height = 64\n",
    "image = cv2.imread(\"images\\SGStove.jpg\")\n",
    "output = image.copy()\n",
    "image = cv2.resize(image, (width, height))\n",
    "image = np.expand_dims(image, axis=0)   # adjust to (1, 3, 64, 64) for generating keras prediction\n",
    "\n",
    "# # load the model and label binarizer\n",
    "# print(\"[INFO] loading network and label binarizer...\")\n",
    "# model = load_model('transfer_learning_CNN_model')\n",
    "# lb = pickle.loads(open(\"transfer_learning_CNN_model_label_bin\", \"rb\").read())\n",
    "\n",
    "# make a prediction on the image\n",
    "preds = model.predict(image)\n",
    "\n",
    "# find the class label index with the largest corresponding probability\n",
    "i = preds.argmax(axis=1)[0]\n",
    "label = lb.classes_[i]\n",
    "\n",
    "# draw the class label + probability on the output image\n",
    "text = \"{}: {:.1f}%\".format(label, preds[0][i] * 100)\n",
    "cv2.putText(output, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)\n",
    "\n",
    "# show the output image\n",
    "cv2.imshow(\"Image\", output)\n",
    "cv2.waitKey(0)   # Delay in milliseconds. 0 is the special value that means “forever”, until you close the image window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Clean_Room'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb.classes_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Category')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the prediction probability for each category\n",
    "plt.figure(figsize = [10,5])   # [width, height]\n",
    "\n",
    "x = [ lb.classes_[0], lb.classes_[1], lb.classes_[2] ]\n",
    "y = [ preds[0][0], preds[0][1], preds[0][2] ]\n",
    "plt.barh(x, y, color='orange')\n",
    "\n",
    "ticks_x = np.linspace(0, 1, 11)   # (start, end, number of ticks)\n",
    "plt.xticks(ticks_x, fontsize=10, family='fantasy', color='black')\n",
    "plt.yticks( size=15, color='black' )\n",
    "for i, v in enumerate(y):\n",
    "    plt.text(v, i, \"  \"+str(v.round(2)), color='grey', va='center', fontweight='bold')\n",
    "\n",
    "plt.title('Prediction Probability Transfer Learning with VGG16', family='serif', fontsize=15, style='italic', weight='bold', color='olive', loc='center', rotation=0)\n",
    "plt.xlabel('Probability', fontsize=12, weight='bold', color='blue')\n",
    "plt.ylabel('Category', fontsize=12, weight='bold', color='navy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Spark",
   "language": "python",
   "name": "spark"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}