diff --git "a/12. Optimizers, Adaptive Learning Rate & Callbacks/12.3 Building a Fruit Classifer.ipynb" "b/12. Optimizers, Adaptive Learning Rate & Callbacks/12.3 Building a Fruit Classifer.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training our Fruit Classifer\n",
+    "### Experimenting with Callbacks\n",
+    "- Let's create our data generators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 41322 images belonging to 81 classes.\n",
+      "Found 13877 images belonging to 81 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "import tensorflow as tff\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "import os\n",
+    "\n",
+    "num_classes = 81\n",
+    "img_rows, img_cols = 32, 32\n",
+    "batch_size = 16\n",
+    "\n",
+    "\n",
+    "train_data_dir = './fruits-360/train'\n",
+    "validation_data_dir = './fruits-360/validation'\n",
+    "\n",
+    "# Let's use some data augmentaiton \n",
+    "train_datagen = ImageDataGenerator(\n",
+    "      rescale=1./255,\n",
+    "      rotation_range=30,\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",
+    "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",
+    "        shuffle=True)\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',\n",
+    "        shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's define our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
+      "_________________________________________________________________\n",
+      "activation (Activation)      (None, 32, 32, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_1 (Conv2D)            (None, 30, 30, 32)        9248      \n",
+      "_________________________________________________________________\n",
+      "activation_1 (Activation)    (None, 30, 30, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout (Dropout)            (None, 15, 15, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 15, 15, 64)        18496     \n",
+      "_________________________________________________________________\n",
+      "activation_2 (Activation)    (None, 15, 15, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_3 (Conv2D)            (None, 13, 13, 64)        36928     \n",
+      "_________________________________________________________________\n",
+      "activation_3 (Activation)    (None, 13, 13, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 6, 6, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 2304)              0         \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 512)               1180160   \n",
+      "_________________________________________________________________\n",
+      "activation_4 (Activation)    (None, 512)               0         \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 512)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 81)                41553     \n",
+      "_________________________________________________________________\n",
+      "activation_5 (Activation)    (None, 81)                0         \n",
+      "=================================================================\n",
+      "Total params: 1,287,281\n",
+      "Trainable params: 1,287,281\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "\n",
+    "# Padding = 'same'  results in padding the input such that\n",
+    "# the output has the same length as the original input\n",
+    "model.add(Conv2D(32, (3, 3), padding='same',\n",
+    "                 input_shape= (img_rows, img_cols, 3)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Conv2D(32, (3, 3)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Conv2D(64, (3, 3)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(512))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(num_classes))\n",
+    "model.add(Activation('softmax'))\n",
+    "\n",
+    "# initiate RMSprop optimizer and configure some parameters\n",
+    "#opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 2582 steps, validate for 867 steps\n",
+      "2581/2582 [============================>.] - ETA: 0s - loss: 0.6169 - accuracy: 0.8048\n",
+      "Epoch 00001: val_loss improved from inf to 0.42778, saving model to fruits_fresh_cnn_1.h5\n",
+      "2582/2582 [==============================] - 221s 86ms/step - loss: 0.6169 - accuracy: 0.8048 - val_loss: 0.4278 - val_accuracy: 0.8507\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.optimizers import RMSprop, SGD\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
+    "\n",
+    "                     \n",
+    "checkpoint = ModelCheckpoint(\"fruits_fresh_cnn_1.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",
+    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss',\n",
+    "                              factor = 0.2,\n",
+    "                              patience = 3,\n",
+    "                              verbose = 1,\n",
+    "                              min_delta = 0.0001)\n",
+    "\n",
+    "# we put our call backs into a callback list\n",
+    "callbacks = [earlystop, checkpoint, reduce_lr]\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",
+    "nb_train_samples = 41322\n",
+    "nb_validation_samples = 13877\n",
+    "epochs = 1\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": [
+    "### Displaying our Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix\n",
+      "[[103   0   0 ...   0   0   0]\n",
+      " [  0 118   7 ...   0   0   0]\n",
+      " [  0   0 152 ...   0   0   0]\n",
+      " ...\n",
+      " [  0   0   0 ... 164   0   0]\n",
+      " [  0   0   0 ...   0 110   0]\n",
+      " [  0   0   0 ...   0   0 175]]\n",
+      "Classification Report\n",
+      "                     precision    recall  f1-score   support\n",
+      "\n",
+      "     Apple Braeburn       0.45      0.63      0.52       164\n",
+      "     Apple Golden 1       0.89      0.72      0.80       164\n",
+      "     Apple Golden 2       0.96      0.93      0.94       164\n",
+      "     Apple Golden 3       0.60      1.00      0.75       161\n",
+      " Apple Granny Smith       1.00      1.00      1.00       164\n",
+      "        Apple Red 1       0.92      0.37      0.52       164\n",
+      "        Apple Red 2       1.00      0.59      0.74       164\n",
+      "        Apple Red 3       0.60      0.89      0.72       144\n",
+      "Apple Red Delicious       0.64      1.00      0.78       166\n",
+      "   Apple Red Yellow       1.00      0.91      0.96       164\n",
+      "            Apricot       1.00      0.30      0.47       164\n",
+      "            Avocado       1.00      1.00      1.00       143\n",
+      "       Avocado ripe       0.91      0.47      0.62       166\n",
+      "             Banana       0.95      1.00      0.98       166\n",
+      "         Banana Red       0.86      0.93      0.89       166\n",
+      "       Cactus fruit       0.99      0.99      0.99       166\n",
+      "       Cantaloupe 1       1.00      0.87      0.93       164\n",
+      "       Cantaloupe 2       1.00      0.99      1.00       164\n",
+      "          Carambula       0.88      0.60      0.71       166\n",
+      "           Cherry 1       0.58      1.00      0.73       164\n",
+      "           Cherry 2       0.92      0.05      0.09       246\n",
+      "     Cherry Rainier       0.84      0.69      0.76       246\n",
+      "   Cherry Wax Black       0.91      1.00      0.95       164\n",
+      "     Cherry Wax Red       0.89      1.00      0.94       164\n",
+      "  Cherry Wax Yellow       1.00      1.00      1.00       164\n",
+      "         Clementine       1.00      1.00      1.00       166\n",
+      "              Cocos       1.00      0.61      0.76       166\n",
+      "              Dates       0.86      1.00      0.93       166\n",
+      "         Granadilla       1.00      1.00      1.00       166\n",
+      "         Grape Pink       0.78      1.00      0.87       164\n",
+      "        Grape White       0.87      1.00      0.93       166\n",
+      "      Grape White 2       1.00      1.00      1.00       166\n",
+      "    Grapefruit Pink       0.92      1.00      0.96       166\n",
+      "   Grapefruit White       0.67      1.00      0.80       164\n",
+      "              Guava       1.00      1.00      1.00       166\n",
+      "        Huckleberry       1.00      1.00      1.00       166\n",
+      "               Kaki       1.00      1.00      1.00       166\n",
+      "               Kiwi       1.00      0.96      0.98       156\n",
+      "           Kumquats       1.00      1.00      1.00       166\n",
+      "              Lemon       1.00      0.43      0.60       164\n",
+      "        Lemon Meyer       0.70      1.00      0.83       166\n",
+      "              Limes       1.00      1.00      1.00       166\n",
+      "             Lychee       0.79      1.00      0.88       166\n",
+      "          Mandarine       1.00      0.43      0.60       166\n",
+      "              Mango       0.85      1.00      0.92       166\n",
+      "           Maracuja       1.00      0.90      0.95       166\n",
+      " Melon Piel de Sapo       0.99      0.88      0.93       246\n",
+      "           Mulberry       0.61      1.00      0.76       164\n",
+      "          Nectarine       1.00      0.35      0.52       164\n",
+      "             Orange       0.44      0.56      0.49       160\n",
+      "             Papaya       1.00      1.00      1.00       164\n",
+      "      Passion Fruit       0.69      1.00      0.82       166\n",
+      "              Peach       1.00      0.58      0.73       164\n",
+      "         Peach Flat       0.85      1.00      0.92       164\n",
+      "               Pear       0.93      0.65      0.76       164\n",
+      "         Pear Abate       0.94      0.81      0.87       166\n",
+      "       Pear Monster       0.88      0.97      0.93       166\n",
+      "      Pear Williams       0.97      0.69      0.81       166\n",
+      "             Pepino       0.72      1.00      0.84       166\n",
+      "           Physalis       0.99      1.00      1.00       164\n",
+      " Physalis with Husk       0.62      0.98      0.76       164\n",
+      "          Pineapple       0.68      0.96      0.80       166\n",
+      "     Pineapple Mini       1.00      0.58      0.73       163\n",
+      "       Pitahaya Red       0.45      1.00      0.62       166\n",
+      "               Plum       0.98      0.89      0.93       151\n",
+      "        Pomegranate       0.50      0.55      0.52       164\n",
+      "             Quince       0.78      1.00      0.88       166\n",
+      "           Rambutan       0.88      1.00      0.93       164\n",
+      "          Raspberry       1.00      1.00      1.00       166\n",
+      "              Salak       0.73      0.75      0.74       162\n",
+      "         Strawberry       1.00      0.87      0.93       164\n",
+      "   Strawberry Wedge       0.90      0.62      0.74       246\n",
+      "          Tamarillo       0.98      1.00      0.99       166\n",
+      "            Tangelo       1.00      0.99      1.00       166\n",
+      "           Tomato 1       1.00      1.00      1.00       246\n",
+      "           Tomato 2       1.00      0.90      0.95       225\n",
+      "           Tomato 3       0.93      1.00      0.96       246\n",
+      "           Tomato 4       0.98      1.00      0.99       160\n",
+      "  Tomato Cherry Red       1.00      1.00      1.00       164\n",
+      "      Tomato Maroon       1.00      0.87      0.93       127\n",
+      "             Walnut       1.00      0.70      0.83       249\n",
+      "\n",
+      "           accuracy                           0.85     13877\n",
+      "          macro avg       0.88      0.86      0.84     13877\n",
+      "       weighted avg       0.89      0.85      0.84     13877\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import numpy as np\n",
+    "\n",
+    "#Confution Matrix and Classification Report\n",
+    "Y_pred = model.predict(validation_generator)\n",
+    "y_pred = np.argmax(Y_pred, axis=1)\n",
+    "\n",
+    "print('Confusion Matrix')\n",
+    "print(confusion_matrix(validation_generator.classes, y_pred))\n",
+    "\n",
+    "# Create a dictionary of class labels\n",
+    "class_labels = validation_generator.class_indices\n",
+    "class_labels = {v: k for k, v in class_labels.items()}\n",
+    "\n",
+    "print('Classification Report')\n",
+    "target_names = list(class_labels.values())\n",
+    "\n",
+    "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import sklearn\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.models import load_model\n",
+    "\n",
+    "img_row, img_height, img_depth = 32,32,3\n",
+    "model = load_model('fruits_fresh_cnn_1.h5')\n",
+    "\n",
+    "class_labels = validation_generator.class_indices\n",
+    "class_labels = {v: k for k, v in class_labels.items()}\n",
+    "classes = list(class_labels.values())\n",
+    "\n",
+    "nb_train_samples = 41322\n",
+    "nb_validation_samples = 13877\n",
+    "\n",
+    "#Confution Matrix and Classification Report\n",
+    "Y_pred = model.predict(validation_generator)\n",
+    "y_pred = np.argmax(Y_pred, axis=1)\n",
+    "\n",
+    "target_names = list(class_labels.values())\n",
+    "\n",
+    "plt.figure(figsize=(20,20))\n",
+    "cnf_matrix = confusion_matrix(validation_generator.classes, y_pred)\n",
+    "\n",
+    "plt.imshow(cnf_matrix, interpolation='nearest')\n",
+    "plt.colorbar()\n",
+    "tick_marks = np.arange(len(classes))\n",
+    "_ = plt.xticks(tick_marks, classes, rotation=90)\n",
+    "_ = plt.yticks(tick_marks, classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing our fruit classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import re\n",
+    "\n",
+    "def draw_test(name, pred, im, true_label):\n",
+    "    BLACK = [0,0,0]\n",
+    "    expanded_image = cv2.copyMakeBorder(im, 160, 0, 0, 500 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
+    "    cv2.putText(expanded_image, \"predited - \"+ pred, (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)\n",
+    "    cv2.putText(expanded_image, \"true - \"+ true_label, (20, 120) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
+    "    cv2.imshow(name, expanded_image)\n",
+    "\n",
+    "\n",
+    "def getRandomImage(path, img_width, img_height):\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",
+    "    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",
+    "    final_path = file_path + \"/\" + image_name\n",
+    "    return image.load_img(final_path, target_size = (img_width, img_height)), final_path, path_class\n",
+    "\n",
+    "# dimensions of our images\n",
+    "img_width, img_height = 32, 32\n",
+    "\n",
+    "\n",
+    "files = []\n",
+    "predictions = []\n",
+    "true_labels = []\n",
+    "# predicting images\n",
+    "for i in range(0, 10):\n",
+    "    path = './fruits-360/validation/' \n",
+    "    img, final_path, true_label = getRandomImage(path, img_width, img_height)\n",
+    "    files.append(final_path)\n",
+    "    true_labels.append(true_label)\n",
+    "    x = image.img_to_array(img)\n",
+    "    x = x * 1./255\n",
+    "    x = np.expand_dims(x, axis=0)\n",
+    "    images = np.vstack([x])\n",
+    "    classes = model.predict_classes(images, batch_size = 10)\n",
+    "    predictions.append(classes)\n",
+    "    \n",
+    "for i in range(0, len(files)):\n",
+    "    image = cv2.imread((files[i]))\n",
+    "    draw_test(\"Prediction\", class_labels[predictions[i][0]], image, true_labels[i])\n",
+    "    cv2.waitKey(0)\n",
+    "\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
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