TrafficMain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # START"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading & Examining the DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "44\n",
      "44\n",
      "class  0  is  61\n",
      "class  1  is  720\n",
      "class  2  is  750\n",
      "class  3  is  450\n",
      "class  4  is  660\n",
      "class  5  is  630\n",
      "class  6  is  150\n",
      "class  7  is  450\n",
      "class  8  is  450\n",
      "class  9  is  480\n",
      "class  10  is  661\n",
      "class  11  is  420\n",
      "class  12  is  690\n",
      "class  13  is  720\n",
      "class  14  is  270\n",
      "class  15  is  210\n",
      "class  16  is  150\n",
      "class  17  is  360\n",
      "class  18  is  390\n",
      "class  19  is  60\n",
      "class  20  is  90\n",
      "class  21  is  90\n",
      "class  22  is  120\n",
      "class  23  is  150\n",
      "class  24  is  90\n",
      "class  25  is  480\n",
      "class  26  is  180\n",
      "class  27  is  60\n",
      "class  28  is  150\n",
      "class  29  is  90\n",
      "class  30  is  150\n",
      "class  31  is  270\n",
      "class  32  is  60\n",
      "class  33  is  210\n",
      "class  34  is  120\n",
      "class  35  is  390\n",
      "class  36  is  120\n",
      "class  37  is  60\n",
      "class  38  is  690\n",
      "class  39  is  90\n",
      "class  40  is  90\n",
      "class  41  is  60\n",
      "class  42  is  90\n"
     ]
    }
   ],
   "source": [
    "print(len(os.listdir('Traffic/traffic/')))\n",
    "print(len(os.listdir('Traffic/traffic/Train/')))\n",
    "print(len(os.listdir('Traffic/traffic/Test/')))\n",
    "\n",
    "\n",
    "'''\n",
    "# Info about the training set\n",
    "\n",
    "for i in range(0, 43):\n",
    "    a = str(i)\n",
    "    path = 'Traffic/traffic/Train/' + str(i) + \"/\"\n",
    "    print(\"class \" , i , \" is \" , len(os.listdir(path)))\n",
    "'''\n",
    "\n",
    "\n",
    "for i in range(0, 43):\n",
    "    a = str(i)\n",
    "    path = 'Traffic/traffic/Test/' + str(i) + \"/\"\n",
    "    print(\"class \" , i , \" is \" , len(os.listdir(path)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.DS_Store', 'Test', 'Train']\n",
      "Train\n",
      "['32', '35', '34', '33', '.DS_Store', '20', '18', '27', '9', '0', '11', '7', '29', '16', '42', '6', '28', '17', '1', '10', '19', '26', '8', '21', '38', '36', '31', '30', '37', '39', '41', '24', '23', '4', '15', '3', '12', '40', '2', '13', '5', '14', '22', '25']\n",
      "Test\n",
      "['32', '35', '34', '33', '.DS_Store', '20', '18', '27', '9', '0', '11', '7', '29', '16', '42', '6', '28', '17', '1', '10', '19', '26', '8', '21', '38', '36', '31', '30', '37', '39', '41', '24', '23', '4', '15', '3', '12', '40', '2', '13', '5', '14', '22', '25']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir('Traffic/traffic/'))\n",
    "\n",
    "print(\"Train\")\n",
    "print(os.listdir('Traffic/traffic/Train/'))\n",
    "\n",
    "print(\"Test\")\n",
    "print(os.listdir('Traffic/traffic/Test/'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = 'Traffic/traffic'\n",
    "\n",
    "train_dir = os.path.join(base_dir, 'Train')\n",
    "validation_dir = os.path.join(base_dir, 'Test')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "........."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                                      # MODEL ARCHITECTURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelV1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Arcitecture   ::::: model == modelV1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    \n",
    "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2), \n",
    "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'), \n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    # Flatten the results to feed into a DNN\n",
    "    tf.keras.layers.Flatten(), \n",
    "    # 512 neuron hidden layer\n",
    "    tf.keras.layers.Dense(512, activation='relu'), \n",
    "    \n",
    "    tf.keras.layers.Dense(43, activation='softmax')  \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 148, 148, 16)      448       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 74, 74, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 72, 72, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 36, 36, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 34, 34, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 17, 17, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 18496)             0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               9470464   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 43)                22059     \n",
      "=================================================================\n",
      "Total params: 9,516,107\n",
      "Trainable params: 9,516,107\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = \"categorical_crossentropy\", optimizer = \"adam\", metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelV2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Architecture\n",
    "\n",
    "\n",
    "V2 experiments with the dropout layer (changing the dropout rates)\n",
    "\n",
    "The dropout layer is placed before the last layer (softmax layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelV2 = tf.keras.models.Sequential([\n",
    "    \n",
    "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2), \n",
    "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'), \n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    # Flatten the results to feed into a DNN\n",
    "    tf.keras.layers.Flatten(), \n",
    "    # 512 neuron hidden layer\n",
    "    tf.keras.layers.Dense(512, activation='relu'), \n",
    "    \n",
    "    tf.keras.layers.Dropout(0.5),   # 0.55 dropout rate has been most successful till now\n",
    "    \n",
    "    tf.keras.layers.Dense(43, activation='softmax')  \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 148, 148, 16)      448       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 74, 74, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 72, 72, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 36, 36, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 34, 34, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 17, 17, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 18496)             0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               9470464   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 43)                22059     \n",
      "=================================================================\n",
      "Total params: 9,516,107\n",
      "Trainable params: 9,516,107\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "modelV2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelV2.compile(loss = \"categorical_crossentropy\", optimizer = \"adam\", metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Callback function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class myCallBack(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs = {}):\n",
    "        if(logs.get(\"accuracy\") > 0.998):\n",
    "            print(\"\\n Reached Desired Accuracy, so terminating training !! \")\n",
    "            self.model.stop_training = True\n",
    "            \n",
    "            \n",
    "callbacks= myCallBack()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 39209 images belonging to 43 classes.\n",
      "Found 12630 images belonging to 43 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nOutput : \\nFound 39209 images belonging to 43 classes.\\nFound 12630 images belonging to 43 classes.\\n'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# All images will be rescaled by 1./255.\n",
    "train_datagen = ImageDataGenerator( rescale = 1.0/255)\n",
    "test_datagen  = ImageDataGenerator( rescale = 1.0/255)\n",
    "\n",
    "# --------------------\n",
    "# Flow training images in batches of 20 using train_datagen generator\n",
    "# --------------------\n",
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                    #batch_size=20,\n",
    "                                                    class_mode='categorical',\n",
    "                                                    target_size=(150, 150))     \n",
    "# --------------------\n",
    "# Flow validation images in batches of 20 using test_datagen generator\n",
    "# --------------------\n",
    "validation_generator =  test_datagen.flow_from_directory(validation_dir,\n",
    "                                                         #batch_size=20,\n",
    "                                                         class_mode  = 'categorical',\n",
    "                                                         target_size = (150, 150))\n",
    "\n",
    "'''\n",
    "Output : \n",
    "Found 39209 images belonging to 43 classes.\n",
    "Found 12630 images belonging to 43 classes.\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FITTING & EVALUATING MODELS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelV1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1226/1226 [==============================] - 594s 485ms/step - loss: 0.5396 - accuracy: 0.8486 - val_loss: 0.3475 - val_accuracy: 0.9138\n",
      "Epoch 2/5\n",
      "1226/1226 [==============================] - 589s 480ms/step - loss: 0.0797 - accuracy: 0.9782 - val_loss: 0.3118 - val_accuracy: 0.9279\n",
      "Epoch 3/5\n",
      "1226/1226 [==============================] - 599s 489ms/step - loss: 0.0422 - accuracy: 0.9878 - val_loss: 0.4086 - val_accuracy: 0.9268\n",
      "Epoch 4/5\n",
      "1226/1226 [==============================] - 610s 498ms/step - loss: 0.0329 - accuracy: 0.9906 - val_loss: 0.4541 - val_accuracy: 0.9217\n",
      "Epoch 5/5\n",
      "1226/1226 [==============================] - 673s 549ms/step - loss: 0.0198 - accuracy: 0.9939 - val_loss: 0.3837 - val_accuracy: 0.9285\n"
     ]
    }
   ],
   "source": [
    "history = model.fit (\n",
    "                         train_generator,\n",
    "                         validation_data = validation_generator,\n",
    "                         epochs = 5,\n",
    "                         verbose = 1\n",
    ")\n",
    "\n",
    "'''\n",
    "history = model.fit (\n",
    "                         train_generator,\n",
    "                         validation_data = validation_generator,\n",
    "                         epochs = 5,\n",
    "                         callbacks = [callbacks],\n",
    "                         verbose = 1\n",
    ")\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1226/1226 [==============================] - 579s 473ms/step - loss: 0.8508 - accuracy: 0.7522 - val_loss: 0.3478 - val_accuracy: 0.8957\n",
      "Epoch 2/20\n",
      "1226/1226 [==============================] - 570s 465ms/step - loss: 0.1528 - accuracy: 0.9540 - val_loss: 0.2707 - val_accuracy: 0.9236\n",
      "Epoch 3/20\n",
      "1226/1226 [==============================] - 614s 501ms/step - loss: 0.0856 - accuracy: 0.9739 - val_loss: 0.2481 - val_accuracy: 0.9317\n",
      "Epoch 4/20\n",
      "1226/1226 [==============================] - 605s 493ms/step - loss: 0.0661 - accuracy: 0.9794 - val_loss: 0.2007 - val_accuracy: 0.9459\n",
      "Epoch 5/20\n",
      "1226/1226 [==============================] - 591s 482ms/step - loss: 0.0494 - accuracy: 0.9849 - val_loss: 0.2074 - val_accuracy: 0.9477\n",
      "Epoch 6/20\n",
      "1226/1226 [==============================] - 588s 479ms/step - loss: 0.0436 - accuracy: 0.9865 - val_loss: 0.1608 - val_accuracy: 0.9574\n",
      "Epoch 7/20\n",
      "1226/1226 [==============================] - 590s 482ms/step - loss: 0.0418 - accuracy: 0.9879 - val_loss: 0.2615 - val_accuracy: 0.9394\n",
      "Epoch 8/20\n",
      "1226/1226 [==============================] - 588s 480ms/step - loss: 0.0325 - accuracy: 0.9902 - val_loss: 0.2518 - val_accuracy: 0.9459\n",
      "Epoch 9/20\n",
      "1226/1226 [==============================] - 585s 477ms/step - loss: 0.0320 - accuracy: 0.9905 - val_loss: 0.1899 - val_accuracy: 0.9565\n",
      "Epoch 10/20\n",
      "1226/1226 [==============================] - 1759s 1s/step - loss: 0.0278 - accuracy: 0.9921 - val_loss: 0.1998 - val_accuracy: 0.9583\n",
      "Epoch 11/20\n",
      "1226/1226 [==============================] - 590s 481ms/step - loss: 0.0271 - accuracy: 0.9921 - val_loss: 0.2725 - val_accuracy: 0.9454\n",
      "Epoch 12/20\n",
      "1226/1226 [==============================] - 10566s 9s/step - loss: 0.0295 - accuracy: 0.9915 - val_loss: 0.2128 - val_accuracy: 0.9533\n",
      "Epoch 13/20\n",
      "1226/1226 [==============================] - 571s 466ms/step - loss: 0.0199 - accuracy: 0.9943 - val_loss: 0.1902 - val_accuracy: 0.9643\n",
      "Epoch 14/20\n",
      "1226/1226 [==============================] - 570s 465ms/step - loss: 0.0227 - accuracy: 0.9942 - val_loss: 0.2141 - val_accuracy: 0.9609\n",
      "Epoch 15/20\n",
      "1226/1226 [==============================] - 570s 465ms/step - loss: 0.0286 - accuracy: 0.9917 - val_loss: 0.2554 - val_accuracy: 0.9542\n",
      "Epoch 16/20\n",
      "1226/1226 [==============================] - 570s 465ms/step - loss: 0.0240 - accuracy: 0.9937 - val_loss: 0.2035 - val_accuracy: 0.9666\n",
      "Epoch 17/20\n",
      "1226/1226 [==============================] - 603s 492ms/step - loss: 0.0237 - accuracy: 0.9940 - val_loss: 0.1949 - val_accuracy: 0.9644\n",
      "Epoch 18/20\n",
      "1226/1226 [==============================] - 645s 526ms/step - loss: 0.0177 - accuracy: 0.9951 - val_loss: 0.2183 - val_accuracy: 0.9612\n",
      "Epoch 19/20\n",
      "1226/1226 [==============================] - 660s 539ms/step - loss: 0.0201 - accuracy: 0.9946 - val_loss: 0.2414 - val_accuracy: 0.9607\n",
      "Epoch 20/20\n",
      "1226/1226 [==============================] - 618s 504ms/step - loss: 0.0238 - accuracy: 0.9946 - val_loss: 0.2259 - val_accuracy: 0.9577\n"
     ]
    }
   ],
   "source": [
    "# initiallly modelV2 had dropout rate 0.2\n",
    "# dropout rate of 0.4 performed better than 0.2 (taken 6 epochs)\n",
    "# dropout rate of 0.55 was best for 6 epochs, so going for more epochs\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# trying 20 epochs for 0.5 dropout\n",
    "historyV2 = modelV2.fit (\n",
    "                         train_generator,\n",
    "                         validation_data = validation_generator,\n",
    "                         epochs = 20,\n",
    "                         callbacks = [callbacks],\n",
    "                         verbose = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOTTING THE RESULTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Red represents Training set\n",
    "\n",
    "\n",
    "\n",
    "Blue represents Validation set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image  as mpimg\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelV1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and validation loss')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT LOSS AND ACCURACY\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------\n",
    "# Retrieve a list of list results on training and test data\n",
    "# sets for each training epoch\n",
    "#-----------------------------------------------------------\n",
    "acc=history.history['accuracy']\n",
    "val_acc=history.history['val_accuracy']\n",
    "loss=history.history['loss']\n",
    "val_loss=history.history['val_loss']\n",
    "\n",
    "epochs=range(len(acc)) # Get number of epochs\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training and validation accuracy per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot(epochs, acc, 'r', \"Training Accuracy\")\n",
    "plt.plot(epochs, val_acc, 'b', \"Validation Accuracy\")\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.figure()\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training and validation loss per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot(epochs, loss, 'r', \"Training Loss\")\n",
    "plt.plot(epochs, val_loss, 'b', \"Validation Loss\")\n",
    "\n",
    "\n",
    "plt.title('Training and validation loss')\n",
    "\n",
    "# Desired output. Charts with training and validation metrics. No crash :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and validation loss')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT LOSS AND ACCURACY\n",
    "%matplotlib inline\n",
    "\n",
    "#-----------------------------------------------------------\n",
    "# Retrieve a list of list results on training and test data\n",
    "# sets for each training epoch\n",
    "#-----------------------------------------------------------\n",
    "acc=historyV2.history['accuracy']\n",
    "val_acc=historyV2.history['val_accuracy']\n",
    "loss=historyV2.history['loss']\n",
    "val_loss=historyV2.history['val_loss']\n",
    "\n",
    "epochs=range(len(acc)) # Get number of epochs\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training and validation accuracy per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot(epochs, acc, 'r', \"Training Accuracy\")\n",
    "plt.plot(epochs, val_acc, 'b', \"Validation Accuracy\")\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.figure()\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training and validation loss per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot(epochs, loss, 'r', \"Training Loss\")\n",
    "plt.plot(epochs, val_loss, 'b', \"Validation Loss\")\n",
    "\n",
    "\n",
    "plt.title('Training and validation loss')\n",
    "\n",
    "# Desired output. Charts with training and validation metrics. No crash :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAVING & LOADING MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelV2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the model to the specified path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modelV2.save(\"modelV2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code for reloading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modelV2 = keras.models.load_model(\"modelV2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESTING for Individual Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from tensorflow import keras\n",
    "#from IPython.display import Image\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n",
      "  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "image_path = '03967.png'  # insert the path of the image to be tested\n",
    "\n",
    "# option 1\n",
    "\n",
    "\n",
    "# dimensions of our images\n",
    "img_width, img_height = 150, 150     # set as per earlier convention\n",
    "\n",
    "\n",
    "modelV2.compile(loss = \"categorical_crossentropy\", optimizer = \"adam\", metrics = [\"accuracy\"])\n",
    "\n",
    "# predicting images\n",
    "img = image.load_img(image_path, target_size = (img_width, img_height))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "\n",
    "images = np.vstack([x])\n",
    "classes = modelV2.predict_classes(images)\n",
    "print(classes[0])\n",
    "\n",
    "pred = modelV2.predict(images)\n",
    "print(pred)\n",
    "#print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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