Prediction using ResNet50.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 2103,
     "status": "ok",
     "timestamp": 1630314041530,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "duai-xKaX2I4"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1630314041531,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "m3D7IQ-bMgOG",
    "outputId": "5ef73649-ed30-406c-9053-8560d06ef41a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Aug 30 09:00:40 2021       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 470.57.02    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   53C    P0    69W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi   #Checking the gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1630314041532,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "-GPzkPUebf8v",
    "outputId": "b5584145-bab5-44bd-f2cb-b4122f71590d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2.6.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the version of tensorflow\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 677,
     "status": "ok",
     "timestamp": 1630314042203,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "AkNC1MYxbqQ2"
   },
   "outputs": [],
   "source": [
    "#Importing the required libraries\n",
    "\n",
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1630314042203,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "kb502V3BoT8o",
    "outputId": "d4102368-f00c-4914-8174-492a69feb64b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive \n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1630314042203,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "TQJFX8yRp2sV"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('drive/MyDrive/plant_data')   #Changing the path directory to our dataset folder location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1630314042204,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "STzae-N_p6yq",
    "outputId": "7baa590a-8af0-4665-f970-f75b7f589717"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/MyDrive/plant_data\n"
     ]
    }
   ],
   "source": [
    "!pwd   #Checking the path directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1630314042204,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "D1Bxjt2Lb-lM"
   },
   "outputs": [],
   "source": [
    "#Re-size all the images to this\n",
    "IMAGE_SIZE = [224, 224]\n",
    "\n",
    "train_path = '/content/drive/My Drive/plant_data/train'   #Path of train dataset\n",
    "valid_path = '/content/drive/My Drive/plant_data/test'    #Path of test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 2447,
     "status": "ok",
     "timestamp": 1630314044648,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "ppo3q28ic6nH"
   },
   "outputs": [],
   "source": [
    "#Import the ResNet50 library as shown below and add preprocessing layer to the front\n",
    "#Here we will be using imagenet weights\n",
    "\n",
    "resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1630314044648,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "_KbCElVif4Ly"
   },
   "outputs": [],
   "source": [
    "#Don't train the existing weights as it is a pre-trained model and we will be training our last layer alone\n",
    "for layer in resnet.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1630314044649,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "Jd2CmryiiBVl"
   },
   "outputs": [],
   "source": [
    "#Getting the number of output classes\n",
    "folders = glob('/content/drive/My Drive/plant_data/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1630314044649,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "fsw4ZvlViRjh"
   },
   "outputs": [],
   "source": [
    "#We are flattening it so that we can add any number of layers in the last node\n",
    "x = Flatten()(resnet.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1630314044649,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "-lURg8Tgi6yq",
    "outputId": "26d7c94c-cd3e-40fd-f7df-61e1d3f9c477"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the length of folders\n",
    "len(folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1630314044649,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "vgCpvSCqip5p"
   },
   "outputs": [],
   "source": [
    "#Predicting the data using length of folders and activation function used is softmax as there are more than 2 categories\n",
    "prediction = Dense(len(folders), activation='softmax')(x)\n",
    "\n",
    "#Creating a model object\n",
    "model = Model(inputs=resnet.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1630314044650,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "hNvQ7lTFs8ru",
    "outputId": "9dc03019-6619-49ee-8235-1045099f216b",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
      "                                                                 conv2_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
      "                                                                 conv2_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
      "                                                                 conv2_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
      "                                                                 conv3_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
      "                                                                 conv3_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
      "                                                                 conv3_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
      "                                                                 conv3_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
      "                                                                 conv4_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
      "                                                                 conv4_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
      "                                                                 conv4_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
      "                                                                 conv4_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
      "                                                                 conv4_block5_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
      "                                                                 conv4_block6_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
      "                                                                 conv5_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
      "                                                                 conv5_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
      "                                                                 conv5_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 100352)       0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 4)            401412      flatten[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 23,989,124\n",
      "Trainable params: 401,412\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Checking the structure of the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1630314044650,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "dhVcNN9MtLHc"
   },
   "outputs": [],
   "source": [
    "#Compiling the model with loss, optimitizer and metrics value\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1630314044650,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "j7cUc06ntfMv"
   },
   "outputs": [],
   "source": [
    "#Use the Image Data Generator to import the images from the dataset\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "#Train data\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)\n",
    "\n",
    "#Test data\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 616,
     "status": "ok",
     "timestamp": 1630314045261,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "G4LJPmp5t26P",
    "outputId": "0dbeec60-5d2e-47de-e004-38f2fe08b975"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1951 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "#Make sure you provide the same target size as initialied for the image size\n",
    "#Reading the number of images in train dataset\n",
    "training_set = train_datagen.flow_from_directory('/content/drive/My Drive/plant_data/train',\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1630314045261,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "lNdsaYtluKOu",
    "outputId": "1282c9f2-cd74-4590-f4bf-bb499d8e26c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 18 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "#Reading the number of images in test dataset\n",
    "test_set = test_datagen.flow_from_directory('/content/drive/My Drive/plant_data/test',\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1595174,
     "status": "ok",
     "timestamp": 1630315640434,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "kjnBJTUsucQ2",
    "outputId": "2f653922-6366-4d47-eacf-85f029847ebe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "61/61 [==============================] - 711s 12s/step - loss: 2.9427 - accuracy: 0.4167 - val_loss: 1.6451 - val_accuracy: 0.5000\n",
      "Epoch 2/20\n",
      "61/61 [==============================] - 44s 716ms/step - loss: 0.9981 - accuracy: 0.5864 - val_loss: 1.0364 - val_accuracy: 0.5556\n",
      "Epoch 3/20\n",
      "61/61 [==============================] - 44s 715ms/step - loss: 1.0317 - accuracy: 0.5802 - val_loss: 0.7685 - val_accuracy: 0.7778\n",
      "Epoch 4/20\n",
      "61/61 [==============================] - 44s 715ms/step - loss: 0.9289 - accuracy: 0.6525 - val_loss: 0.7728 - val_accuracy: 0.7222\n",
      "Epoch 5/20\n",
      "61/61 [==============================] - 44s 717ms/step - loss: 1.0062 - accuracy: 0.6233 - val_loss: 0.8113 - val_accuracy: 0.6667\n",
      "Epoch 6/20\n",
      "61/61 [==============================] - 44s 719ms/step - loss: 0.6958 - accuracy: 0.7340 - val_loss: 0.8287 - val_accuracy: 0.7778\n",
      "Epoch 7/20\n",
      "61/61 [==============================] - 44s 720ms/step - loss: 1.0391 - accuracy: 0.6412 - val_loss: 0.7405 - val_accuracy: 0.7222\n",
      "Epoch 8/20\n",
      "61/61 [==============================] - 44s 718ms/step - loss: 0.8916 - accuracy: 0.6704 - val_loss: 0.9817 - val_accuracy: 0.6667\n",
      "Epoch 9/20\n",
      "61/61 [==============================] - 44s 715ms/step - loss: 0.7395 - accuracy: 0.7289 - val_loss: 1.0532 - val_accuracy: 0.7778\n",
      "Epoch 10/20\n",
      "61/61 [==============================] - 44s 717ms/step - loss: 0.7892 - accuracy: 0.7048 - val_loss: 1.2050 - val_accuracy: 0.7778\n",
      "Epoch 11/20\n",
      "61/61 [==============================] - 44s 716ms/step - loss: 0.7999 - accuracy: 0.6961 - val_loss: 0.8725 - val_accuracy: 0.6667\n",
      "Epoch 12/20\n",
      "61/61 [==============================] - 44s 716ms/step - loss: 0.6358 - accuracy: 0.7591 - val_loss: 0.7971 - val_accuracy: 0.7778\n",
      "Epoch 13/20\n",
      "61/61 [==============================] - 44s 718ms/step - loss: 0.8498 - accuracy: 0.6817 - val_loss: 1.4782 - val_accuracy: 0.4444\n",
      "Epoch 14/20\n",
      "61/61 [==============================] - 44s 723ms/step - loss: 0.8558 - accuracy: 0.6853 - val_loss: 0.6290 - val_accuracy: 0.8333\n",
      "Epoch 15/20\n",
      "61/61 [==============================] - 44s 718ms/step - loss: 0.6946 - accuracy: 0.7340 - val_loss: 0.5847 - val_accuracy: 0.7222\n",
      "Epoch 16/20\n",
      "61/61 [==============================] - 44s 719ms/step - loss: 0.7098 - accuracy: 0.7299 - val_loss: 0.4908 - val_accuracy: 0.8333\n",
      "Epoch 17/20\n",
      "61/61 [==============================] - 44s 720ms/step - loss: 0.7331 - accuracy: 0.7248 - val_loss: 1.3184 - val_accuracy: 0.6667\n",
      "Epoch 18/20\n",
      "61/61 [==============================] - 44s 718ms/step - loss: 0.7377 - accuracy: 0.7442 - val_loss: 1.3232 - val_accuracy: 0.7222\n",
      "Epoch 19/20\n",
      "61/61 [==============================] - 44s 722ms/step - loss: 0.7917 - accuracy: 0.7130 - val_loss: 1.2790 - val_accuracy: 0.6667\n",
      "Epoch 20/20\n",
      "61/61 [==============================] - 44s 722ms/step - loss: 0.6661 - accuracy: 0.7499 - val_loss: 0.7830 - val_accuracy: 0.7222\n"
     ]
    }
   ],
   "source": [
    "#Fit the model\n",
    "r = model.fit(training_set, validation_data=test_set, epochs=20, steps_per_epoch=len(training_set), validation_steps=len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "executionInfo": {
     "elapsed": 1492,
     "status": "ok",
     "timestamp": 1630316066639,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "2EAs2VTtvTJ9",
    "outputId": "2ca349f6-4c2e-4ea8-c357-f054224eb251"
   },
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the loss\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n",
    "\n",
    "#Plotting the accuracy\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1187,
     "status": "ok",
     "timestamp": 1630316120479,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "jDqYIy3taO8X",
    "outputId": "be8050c0-ee26-419b-a647-4331a2df8cdd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  category=CustomMaskWarning)\n"
     ]
    }
   ],
   "source": [
    "#Saving it as a h5 file\n",
    "from tensorflow.keras.models import load_model\n",
    "model.save('model_resnet50.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 8032,
     "status": "ok",
     "timestamp": 1630316236165,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "VgTYFcbSacKT"
   },
   "outputs": [],
   "source": [
    "#Prediction using test set\n",
    "y_pred = model.predict(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 440,
     "status": "ok",
     "timestamp": 1630316241596,
     "user": {
      "displayName": "Jerish B",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgTc7bBxPW3slAiahGLrTsd7-EPdSMHvc0rpWiyMR0=s64",
      "userId": "14544953125467163645"
     },
     "user_tz": -330
    },
    "id": "n9S4uXy-a2u1",
    "outputId": "29e1f5d7-e591-45e2-9a82-c4d5abfa098c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.87328458e-02, 9.28524077e-01, 8.07383377e-03, 4.46691923e-02],\n",
       "       [2.18672620e-04, 9.92814004e-01, 1.01449405e-05, 6.95710303e-03],\n",
       "       [2.43806513e-03, 6.83134854e-01, 3.84891629e-02, 2.75937885e-01],\n",
       "       [1.61348376e-02, 6.47874724e-04, 1.45161133e-02, 9.68701243e-01],\n",
       "       [2.93497345e-03, 7.00074388e-03, 9.88371074e-01, 1.69324409e-03],\n",
       "       [8.66644783e-04, 7.65896738e-01, 2.32852884e-02, 2.09951386e-01],\n",
       "       [5.07233664e-03, 2.03119516e-01, 4.05120142e-02, 7.51296103e-01],\n",
       "       [6.78987068e-04, 5.12903631e-01, 8.20409786e-03, 4.78213280e-01],\n",
       "       [2.40621448e-04, 9.92554247e-01, 4.14975034e-03, 3.05535109e-03],\n",
       "       [9.15624481e-03, 9.08165932e-01, 7.39347190e-02, 8.74308404e-03],\n",
       "       [4.58239723e-04, 9.76543367e-01, 9.08708479e-03, 1.39113562e-02],\n",
       "       [2.27992207e-01, 7.42670596e-01, 6.00193022e-03, 2.33352166e-02],\n",
       "       [9.94035363e-01, 2.06201184e-06, 5.20407083e-03, 7.58473470e-04],\n",
       "       [2.57993379e-04, 9.98635590e-01, 9.57889915e-06, 1.09678914e-03],\n",
       "       [3.41969746e-04, 6.78269203e-07, 9.99657154e-01, 2.33402886e-07],\n",
       "       [2.07706807e-06, 9.15028465e-07, 9.99975681e-01, 2.13111653e-05],\n",
       "       [3.09880404e-03, 2.51442753e-03, 9.94219899e-01, 1.66826459e-04],\n",
       "       [2.96072772e-04, 9.77001502e-04, 9.98270869e-01, 4.56014590e-04]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y2A4oFRPa56T"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyOpSCOBPJMRfAuqZQ3ISOQU",
   "collapsed_sections": [],
   "mount_file_id": "1j42eSBIyuaOxuDTPaSjbaW7-fPiC13tj",
   "name": "Prediction using ResNet50.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}