transfer_learning_CNN_adam_128model.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",
    "%matplotlib notebook\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning - define neural network architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "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",
      "13/13 [==============================] - 27s 2s/step - loss: 0.8702 - acc: 0.6051 - val_loss: 0.4580 - val_acc: 0.8280\n",
      "Epoch 2/75\n",
      "13/13 [==============================] - 29s 2s/step - loss: 0.5805 - acc: 0.7633 - val_loss: 0.3399 - val_acc: 0.8728\n",
      "Epoch 3/75\n",
      "13/13 [==============================] - 31s 2s/step - loss: 0.4744 - acc: 0.8002 - val_loss: 0.3175 - val_acc: 0.8692\n",
      "Epoch 4/75\n",
      "13/13 [==============================] - 32s 2s/step - loss: 0.4387 - acc: 0.8185 - val_loss: 0.2842 - val_acc: 0.8925\n",
      "Epoch 5/75\n",
      "13/13 [==============================] - 30s 2s/step - loss: 0.3971 - acc: 0.8513 - val_loss: 0.2529 - val_acc: 0.8871\n",
      "Epoch 6/75\n",
      "13/13 [==============================] - 32s 2s/step - loss: 0.3851 - acc: 0.8299 - val_loss: 0.2538 - val_acc: 0.9032\n",
      "Epoch 7/75\n",
      "13/13 [==============================] - 34s 3s/step - loss: 0.3642 - acc: 0.8498 - val_loss: 0.2428 - val_acc: 0.8961\n",
      "Epoch 8/75\n",
      "13/13 [==============================] - 34s 3s/step - loss: 0.3713 - acc: 0.8288 - val_loss: 0.2444 - val_acc: 0.8943\n",
      "Epoch 9/75\n",
      "13/13 [==============================] - 33s 3s/step - loss: 0.3108 - acc: 0.8773 - val_loss: 0.2412 - val_acc: 0.9032\n",
      "Epoch 10/75\n",
      "13/13 [==============================] - 32s 2s/step - loss: 0.3045 - acc: 0.8890 - val_loss: 0.2522 - val_acc: 0.8817\n",
      "Epoch 11/75\n",
      "13/13 [==============================] - 35s 3s/step - loss: 0.3095 - acc: 0.8738 - val_loss: 0.2392 - val_acc: 0.8978\n",
      "Epoch 12/75\n",
      "13/13 [==============================] - 33s 3s/step - loss: 0.3146 - acc: 0.8697 - val_loss: 0.2586 - val_acc: 0.8853\n",
      "Epoch 13/75\n",
      "13/13 [==============================] - 32s 2s/step - loss: 0.3609 - acc: 0.8684 - val_loss: 0.2454 - val_acc: 0.8961\n",
      "Epoch 14/75\n",
      "13/13 [==============================] - 33s 3s/step - loss: 0.3351 - acc: 0.8545 - val_loss: 0.2522 - val_acc: 0.8996\n",
      "Epoch 15/75\n",
      "13/13 [==============================] - 32s 2s/step - loss: 0.3023 - acc: 0.8775 - val_loss: 0.2504 - val_acc: 0.8907\n",
      "\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 16/75\n",
      "13/13 [==============================] - 33s 3s/step - loss: 0.3143 - acc: 0.8589 - val_loss: 0.2602 - val_acc: 0.9014\n",
      "Epoch 17/75\n",
      "13/13 [==============================] - 34s 3s/step - loss: 0.2650 - acc: 0.9051 - val_loss: 0.2440 - val_acc: 0.9032\n",
      "Epoch 18/75\n",
      "13/13 [==============================] - 36s 3s/step - loss: 0.2697 - acc: 0.8870 - val_loss: 0.2359 - val_acc: 0.9014\n",
      "Epoch 19/75\n",
      "13/13 [==============================] - 35s 3s/step - loss: 0.2609 - acc: 0.8942 - val_loss: 0.2506 - val_acc: 0.9086\n",
      "Epoch 20/75\n",
      "13/13 [==============================] - 33s 3s/step - loss: 0.3266 - acc: 0.8598 - val_loss: 0.2379 - val_acc: 0.9032\n",
      "Epoch 21/75\n",
      "13/13 [==============================] - 38s 3s/step - loss: 0.2687 - acc: 0.8954 - val_loss: 0.2415 - val_acc: 0.9068\n",
      "Epoch 22/75\n",
      "13/13 [==============================] - 36s 3s/step - loss: 0.2377 - acc: 0.8901 - val_loss: 0.2412 - val_acc: 0.8978\n",
      "\n",
      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 23/75\n",
      "13/13 [==============================] - 36s 3s/step - loss: 0.2815 - acc: 0.8913 - val_loss: 0.2429 - val_acc: 0.9050\n",
      "Epoch 24/75\n",
      "13/13 [==============================] - 39s 3s/step - loss: 0.2669 - acc: 0.8900 - val_loss: 0.2408 - val_acc: 0.9068\n",
      "Epoch 25/75\n",
      "13/13 [==============================] - 38s 3s/step - loss: 0.2476 - acc: 0.8919 - val_loss: 0.2406 - val_acc: 0.9014\n",
      "Epoch 26/75\n",
      "13/13 [==============================] - 40s 3s/step - loss: 0.2676 - acc: 0.8824 - val_loss: 0.2460 - val_acc: 0.9014\n",
      "\n",
      "Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 27/75\n",
      "13/13 [==============================] - 41s 3s/step - loss: 0.3049 - acc: 0.8818 - val_loss: 0.2449 - val_acc: 0.8925\n",
      "Epoch 28/75\n",
      "13/13 [==============================] - 40s 3s/step - loss: 0.2749 - acc: 0.8903 - val_loss: 0.2444 - val_acc: 0.8996\n",
      "Epoch 29/75\n",
      "13/13 [==============================] - 38s 3s/step - loss: 0.2678 - acc: 0.8949 - val_loss: 0.2435 - val_acc: 0.8943\n",
      "Restoring model weights from the end of the best epoch\n",
      "Epoch 00029: 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 = 128\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='adam', 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": 11,
   "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      0.89       236\n",
      "  Messy_Room       0.86      0.81      0.83       162\n",
      "\n",
      "    accuracy                           0.90       558\n",
      "   macro avg       0.91      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": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2899626ec88>"
      ]
     },
     "execution_count": 12,
     "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, 29) # 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": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2899664b3c8>"
      ]
     },
     "execution_count": 14,
     "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, 29) # 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": 15,
   "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": 16,
   "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_adam_early128_model.h5')\n",
    "f = open('label_bin_trsf_learn_adam_early128.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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading network and label binarizer...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 29,
     "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('vgg_CNN_trsf_learn_adam_early128_model.h5')\n",
    "lb = pickle.loads(open(\"label_bin_trsf_learn_adam_early128.env\", \"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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb.classes_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Category')"
      ]
     },
     "execution_count": 18,
     "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
}