Using MobileNet to make a Monkey Breed Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using MobileNet for our family Classifer\n",
    "\n",
    "### Loading the MobileNet Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Freeze all layers except the top 4, as we'll only be training the top 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 InputLayer False\n",
      "1 ZeroPadding2D False\n",
      "2 Conv2D False\n",
      "3 BatchNormalization False\n",
      "4 ReLU False\n",
      "5 DepthwiseConv2D False\n",
      "6 BatchNormalization False\n",
      "7 ReLU False\n",
      "8 Conv2D False\n",
      "9 BatchNormalization False\n",
      "10 ReLU False\n",
      "11 ZeroPadding2D False\n",
      "12 DepthwiseConv2D False\n",
      "13 BatchNormalization False\n",
      "14 ReLU False\n",
      "15 Conv2D False\n",
      "16 BatchNormalization False\n",
      "17 ReLU False\n",
      "18 DepthwiseConv2D False\n",
      "19 BatchNormalization False\n",
      "20 ReLU False\n",
      "21 Conv2D False\n",
      "22 BatchNormalization False\n",
      "23 ReLU False\n",
      "24 ZeroPadding2D False\n",
      "25 DepthwiseConv2D False\n",
      "26 BatchNormalization False\n",
      "27 ReLU False\n",
      "28 Conv2D False\n",
      "29 BatchNormalization False\n",
      "30 ReLU False\n",
      "31 DepthwiseConv2D False\n",
      "32 BatchNormalization False\n",
      "33 ReLU False\n",
      "34 Conv2D False\n",
      "35 BatchNormalization False\n",
      "36 ReLU False\n",
      "37 ZeroPadding2D False\n",
      "38 DepthwiseConv2D False\n",
      "39 BatchNormalization False\n",
      "40 ReLU False\n",
      "41 Conv2D False\n",
      "42 BatchNormalization False\n",
      "43 ReLU False\n",
      "44 DepthwiseConv2D False\n",
      "45 BatchNormalization False\n",
      "46 ReLU False\n",
      "47 Conv2D False\n",
      "48 BatchNormalization False\n",
      "49 ReLU False\n",
      "50 DepthwiseConv2D False\n",
      "51 BatchNormalization False\n",
      "52 ReLU False\n",
      "53 Conv2D False\n",
      "54 BatchNormalization False\n",
      "55 ReLU False\n",
      "56 DepthwiseConv2D False\n",
      "57 BatchNormalization False\n",
      "58 ReLU False\n",
      "59 Conv2D False\n",
      "60 BatchNormalization False\n",
      "61 ReLU False\n",
      "62 DepthwiseConv2D False\n",
      "63 BatchNormalization False\n",
      "64 ReLU False\n",
      "65 Conv2D False\n",
      "66 BatchNormalization False\n",
      "67 ReLU False\n",
      "68 DepthwiseConv2D False\n",
      "69 BatchNormalization False\n",
      "70 ReLU False\n",
      "71 Conv2D False\n",
      "72 BatchNormalization False\n",
      "73 ReLU False\n",
      "74 ZeroPadding2D False\n",
      "75 DepthwiseConv2D False\n",
      "76 BatchNormalization False\n",
      "77 ReLU False\n",
      "78 Conv2D False\n",
      "79 BatchNormalization False\n",
      "80 ReLU False\n",
      "81 DepthwiseConv2D False\n",
      "82 BatchNormalization False\n",
      "83 ReLU False\n",
      "84 Conv2D False\n",
      "85 BatchNormalization False\n",
      "86 ReLU False\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import MobileNet\n",
    "\n",
    "# MobileNet was designed to work on 224 x 224 pixel input images sizes\n",
    "img_rows, img_cols = 224, 224 \n",
    "\n",
    "# Re-loads the MobileNet model without the top or FC layers\n",
    "MobileNet = MobileNet(weights = 'imagenet', \n",
    "                 include_top = False, \n",
    "                 input_shape = (img_rows, img_cols, 3))\n",
    "\n",
    "# Here we freeze the last 4 layers \n",
    "# Layers are set to trainable as True by default\n",
    "for layer in MobileNet.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "# Let's print our layers \n",
    "for (i,layer) in enumerate(MobileNet.layers):\n",
    "    print(str(i) + \" \"+ layer.__class__.__name__, layer.trainable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's make a function that returns our FC Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lw(bottom_model, num_classes):\n",
    "    \"\"\"creates the top or head of the model that will be \n",
    "    placed ontop of the bottom layers\"\"\"\n",
    "\n",
    "    top_model = bottom_model.output\n",
    "    top_model = GlobalAveragePooling2D()(top_model)\n",
    "    top_model = Dense(1024,activation='relu')(top_model)\n",
    "    top_model = Dense(1024,activation='relu')(top_model)\n",
    "    top_model = Dense(512,activation='relu')(top_model)\n",
    "    top_model = Dense(num_classes,activation='softmax')(top_model)\n",
    "    return top_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's add our FC Head back onto MobileNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
      "_________________________________________________________________\n",
      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
      "_________________________________________________________________\n",
      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
      "_________________________________________________________________\n",
      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
      "_________________________________________________________________\n",
      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
      "_________________________________________________________________\n",
      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
      "_________________________________________________________________\n",
      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
      "_________________________________________________________________\n",
      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_3 ( (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 512)               524800    \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 4)                 2052      \n",
      "=================================================================\n",
      "Total params: 5,854,916\n",
      "Trainable params: 2,626,052\n",
      "Non-trainable params: 3,228,864\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, GlobalAveragePooling2D\n",
    "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.models import Model\n",
    "\n",
    "# Set our class number to 3 (Young, Middle, Old)\n",
    "num_classes = 4\n",
    "\n",
    "FC_Head = lw(MobileNet, num_classes)\n",
    "\n",
    "model = Model(inputs = MobileNet.input, outputs = FC_Head)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading our FAMILY DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 387 images belonging to 4 classes.\n",
      "Found 86 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_data_dir = 'family/family train/'\n",
    "validation_data_dir = 'family/family validate/'\n",
    "\n",
    "# Let's use some data augmentaiton \n",
    "train_datagen = ImageDataGenerator(\n",
    "      rescale=1./255,\n",
    "      rotation_range=45,\n",
    "      width_shift_range=0.3,\n",
    "      height_shift_range=0.3,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')\n",
    " \n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    " \n",
    "# set our batch size (typically on most mid tier systems we'll use 16-32)\n",
    "batch_size = 32\n",
    " \n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_data_dir,\n",
    "        target_size=(img_rows, img_cols),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical')\n",
    " \n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        validation_data_dir,\n",
    "        target_size=(img_rows, img_cols),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training out Model\n",
    "- Note we're using checkpointing and early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "24/24 [==============================] - 162s 7s/step - loss: 2.6505 - accuracy: 0.4521 - val_loss: 2.9048 - val_accuracy: 0.1600\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 2.90477, saving model to family.h5\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - 157s 7s/step - loss: 0.9983 - accuracy: 0.7321 - val_loss: 3.4860 - val_accuracy: 0.1929\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 2.90477\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - 148s 6s/step - loss: 0.5048 - accuracy: 0.7930 - val_loss: 4.9448 - val_accuracy: 0.1714\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 2.90477\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - 151s 6s/step - loss: 0.7723 - accuracy: 0.7803 - val_loss: 2.1917 - val_accuracy: 0.1600\n",
      "\n",
      "Epoch 00004: val_loss improved from 2.90477 to 2.19173, saving model to family.h5\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - 146s 6s/step - loss: 0.3066 - accuracy: 0.9093 - val_loss: 6.0521 - val_accuracy: 0.1857\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 2.19173\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import RMSprop\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "                     \n",
    "checkpoint = ModelCheckpoint(\"family.h5\",\n",
    "                             monitor=\"val_loss\",\n",
    "                             mode=\"min\",\n",
    "                             save_best_only = True,\n",
    "                             verbose=1)\n",
    "\n",
    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
    "                          min_delta = 0, \n",
    "                          patience = 3,\n",
    "                          verbose = 1,\n",
    "                          restore_best_weights = True)\n",
    "\n",
    "# we put our call backs into a callback list\n",
    "callbacks = [earlystop, checkpoint]\n",
    "\n",
    "# We use a very small learning rate \n",
    "model.compile(loss = 'categorical_crossentropy',\n",
    "              optimizer = RMSprop(lr = 0.001),\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "# Enter the number of training and validation samples here\n",
    "nb_train_samples = 387\n",
    "nb_validation_samples = 86\n",
    "\n",
    "# We only train 5 EPOCHS \n",
    "epochs = 5\n",
    "batch_size = 16\n",
    "\n",
    "history = model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch = nb_train_samples // batch_size,\n",
    "    epochs = epochs,\n",
    "    callbacks = callbacks,\n",
    "    validation_data = validation_generator,\n",
    "    validation_steps = nb_validation_samples // batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading our classifer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "classifier = load_model('family.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Testing our classifer on some test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class - reetu\n",
      "Class - bitto\n",
      "Class - baua \n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "family_dict = {\"[0]\": \"baua\", \n",
    "                      \"[1]\": \"bitto\",\n",
    "                      \"[2]\": \"pm\",\n",
    "                      \"[3]\": \"reetu\",\n",
    "                      }\n",
    "\n",
    "family_dict_n = {\"baua\": \"baua \", \n",
    "                      \"bitto\": \"bitto\",\n",
    "                      \"pm\": \"pm\",\n",
    "                      \"reetu\": \"reetu\"\n",
    "                      }\n",
    "\n",
    "def draw_test(name, pred, im):\n",
    "    monkey = family_dict[str(pred)]\n",
    "    BLACK = [0,0,0]\n",
    "    expanded_image = cv2.copyMakeBorder(im, 80, 0, 0, 100 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
    "    cv2.putText(expanded_image, monkey, (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)\n",
    "    cv2.imshow(name, expanded_image)\n",
    "\n",
    "def getRandomImage(path):\n",
    "    \"\"\"function loads a random images from a random folder in our test path \"\"\"\n",
    "    folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path)))\n",
    "    random_directory = np.random.randint(0,len(folders))\n",
    "    path_class = folders[random_directory]\n",
    "    print(\"Class - \" + family_dict_n[str(path_class)])\n",
    "    file_path = path + path_class\n",
    "    file_names = [f for f in listdir(file_path) if isfile(join(file_path, f))]\n",
    "    random_file_index = np.random.randint(0,len(file_names))\n",
    "    image_name = file_names[random_file_index]\n",
    "    return cv2.imread(file_path+\"/\"+image_name)    \n",
    "\n",
    "for i in range(0,3):\n",
    "    input_im = getRandomImage(\"family/family validate/\")\n",
    "    input_original = input_im.copy()\n",
    "    input_original = cv2.resize(input_original, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
    "    \n",
    "    input_im = cv2.resize(input_im, (224, 224), interpolation = cv2.INTER_LINEAR)\n",
    "    input_im = input_im / 255.\n",
    "    input_im = input_im.reshape(1,224,224,3) \n",
    "    \n",
    "    # Get Prediction\n",
    "    res = np.argmax(classifier.predict(input_im, 1, verbose = 0), axis=1)\n",
    "    \n",
    "    # Show image with predicted class\n",
    "    draw_test(\"Prediction\", res, input_original) \n",
    "    cv2.waitKey(0)\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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