vgg16_Classifier.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/artificialcoder02/Vgg16_Image_Classifier/blob/main/vgg16_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGJXvLUiYLuL",
        "outputId": "4eb79332-ecab-4841-d744-965607f8e56d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWnrB9wifwSY",
        "outputId": "d68bd6ac-356a-4a3e-bb2e-adb9071259da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6icOTaoetHV",
        "outputId": "932ba49e-8cad-4d8c-8617-cadb58c82716"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.11.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGo_7V0ye8h1"
      },
      "source": [
        "#Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "kcG-2aGne7mn"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Input,Lambda,Dense,Flatten\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from tensorflow.keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIIBYFe2f1aD",
        "outputId": "babcad10-a99c-4ffa-cea2-f7df0f4ec8a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "At7SSQfxlzLp"
      },
      "outputs": [],
      "source": [
        "ROOT_PATH='/content/drive/MyDrive/cats_and_dogs'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjzCwbZUmEUp",
        "outputId": "4f16db6c-3dd1-4bf4-8241-42c1337f9e0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "GrMESrVvmGO_",
        "outputId": "0e735b74-b71d-444f-b25f-a9b2f453a66f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/drive/MyDrive/cats_and_dogs'"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os \n",
        "os.chdir(ROOT_PATH)\n",
        "os.getcwd()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6CUAelKmcFx"
      },
      "source": [
        "#Resizing all images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "GQf8CKghmWnC"
      },
      "outputs": [],
      "source": [
        "IMAGE_SIZE=[224,224]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "O27s-puAmmSa"
      },
      "outputs": [],
      "source": [
        "train_path='/content/drive/MyDrive/cats_and_dogs/cats_and_dogs/train'\n",
        "valid_path='/content/drive/MyDrive/cats_and_dogs/cats_and_dogs/test'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvFgPy51mzEI"
      },
      "source": [
        "#Importing VGG16 library and add preprocesing layer in front of the VGG16.\n",
        "#Here we will se ImageNet weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9X85mZNm-V-",
        "outputId": "8433194a-55c9-4d2d-b1d0-a8502dba6fc3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 3s 0us/step\n"
          ]
        }
      ],
      "source": [
        "vgg16=VGG16(input_shape=IMAGE_SIZE+[3],weights='imagenet',include_top=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ku6bOWfgr1p1"
      },
      "source": [
        "include_top= False means that we will cut off the last dense layer of pre-trained model of VGG16 as it was trained for 1000 output categories of imagenet whereas we only have 3 categories. We also cut off the 1st layer because the input image and the size can be of my choice."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaNaj-pknMDp",
        "outputId": "68d37dfe-c113-42a2-b52e-7c3925912422"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<keras.engine.input_layer.InputLayer object at 0x7fb6e8afb190>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8acbfa0>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6f072b850>\n",
            "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x7fb6e8a4d070>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8acb820>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8a4dee0>\n",
            "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x7fb6e8b54340>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8d0fa00>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8d0f820>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8cd8bb0>\n",
            "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x7fb6e8d0a8e0>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8d0ae20>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8cd8a60>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8d5e0d0>\n",
            "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x7fb6e89bee80>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6e8cf3610>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb74b19b700>\n",
            "<keras.layers.convolutional.conv2d.Conv2D object at 0x7fb6f0602ee0>\n",
            "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x7fb74b18ff10>\n"
          ]
        }
      ],
      "source": [
        "for layers in vgg16.layers:\n",
        "  print(layers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqPR4-3ordR7"
      },
      "source": [
        "###Setting layer.trainable to False moves all the layer's weights from trainable to non-trainable. This is called \"freezing\" the layer: the state of a frozen layer won't be updated during training (either when training with fit() or when training with any custom loop that relies on trainable_weights to apply gradient updates)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "inkzdXgJph_h"
      },
      "outputs": [],
      "source": [
        "for layer in vgg16.layers:\n",
        "  layer.trainable=False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eS7XhocSprfD",
        "outputId": "b3bedd79-e843-4dd7-fd04-c767e14a0be0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "input_1 False\n",
            "block1_conv1 False\n",
            "block1_conv2 False\n",
            "block1_pool False\n",
            "block2_conv1 False\n",
            "block2_conv2 False\n",
            "block2_pool False\n",
            "block3_conv1 False\n",
            "block3_conv2 False\n",
            "block3_conv3 False\n",
            "block3_pool False\n",
            "block4_conv1 False\n",
            "block4_conv2 False\n",
            "block4_conv3 False\n",
            "block4_pool False\n",
            "block5_conv1 False\n",
            "block5_conv2 False\n",
            "block5_conv3 False\n",
            "block5_pool False\n"
          ]
        }
      ],
      "source": [
        "for layer in vgg16.layers:\n",
        "  print(layer.name,layer.trainable)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lVdF3pSp7qB"
      },
      "source": [
        "#Vgg16 Model Summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc3WdCr4p0-U",
        "outputId": "37020d40-44f6-4df2-ac4a-876bf0c494b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 0\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "vgg16.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRq7gMh1sAfU"
      },
      "source": [
        "#Building the Final Model \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "0qtQZ54qp7LJ"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(vgg16)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256,activation='relu'))\n",
        "model.add(Dense(2,activation='Softmax'))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nehx2SYZsTRI"
      },
      "source": [
        "#View the structure of final model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jPiRE1eksZgx",
        "outputId": "3d7fc6b9-a10d-4f05-a982-f09fd0e6f872"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " vgg16 (Functional)          (None, 7, 7, 512)         14714688  \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 25088)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               6422784   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 514       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21,137,986\n",
            "Trainable params: 6,423,298\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pvgc8z2fsfVA"
      },
      "source": [
        "#Specifying the Cost and Optimization Methods"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "6GQA50IRsjeV"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics='accuracy'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vxw13OaltQFg"
      },
      "source": [
        "#Using the Image Data Generator to import images from dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "djJoLvKUtV43"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen= ImageDataGenerator(rescale=1./255,\n",
        "                                  shear_range =0.2,\n",
        "                                  zoom_range=0.2,\n",
        "                                  horizontal_flip=True)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6wW3vaPuxFp",
        "outputId": "5e81cb44-683a-4e53-f952-e74ac493caff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/cats_and_dogs\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Xj23Bmgu3F6",
        "outputId": "f6a4e998-e070-491f-f3c8-b585e9039177"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1000 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "training_set=train_datagen.flow_from_directory('/content/drive/MyDrive/cats_and_dogs/cats_and_dogs/validation',\n",
        "                                               target_size=(224,224),\n",
        "                                               batch_size=32,\n",
        "                                               class_mode='categorical')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFGDqWePva-i",
        "outputId": "d5525a17-15ce-445f-b7cd-f69973080578"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1000 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "test_set=train_datagen.flow_from_directory('/content/drive/MyDrive/cats_and_dogs/cats_and_dogs/validation',\n",
        "                                               target_size=(224,224),\n",
        "                                               batch_size=32,\n",
        "                                               class_mode='categorical')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "om06vOGavf5h"
      },
      "source": [
        "#Model Fitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_mK8tpyvhEc",
        "outputId": "54275fc3-5b9f-4275-de13-4543e858b591"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "32/32 [==============================] - 1343s 42s/step - loss: 1.7135 - accuracy: 0.6260 - val_loss: 0.3267 - val_accuracy: 0.8560\n",
            "Epoch 2/10\n",
            "32/32 [==============================] - 1328s 42s/step - loss: 0.3022 - accuracy: 0.8720 - val_loss: 0.2386 - val_accuracy: 0.9140\n",
            "Epoch 3/10\n",
            "32/32 [==============================] - 1327s 42s/step - loss: 0.2388 - accuracy: 0.9080 - val_loss: 0.1912 - val_accuracy: 0.9360\n",
            "Epoch 4/10\n",
            "32/32 [==============================] - 1385s 44s/step - loss: 0.1944 - accuracy: 0.9260 - val_loss: 0.1488 - val_accuracy: 0.9550\n",
            "Epoch 5/10\n",
            "32/32 [==============================] - 1487s 47s/step - loss: 0.1735 - accuracy: 0.9420 - val_loss: 0.1232 - val_accuracy: 0.9610\n",
            "Epoch 6/10\n",
            "32/32 [==============================] - 1276s 40s/step - loss: 0.1594 - accuracy: 0.9410 - val_loss: 0.1104 - val_accuracy: 0.9590\n",
            "Epoch 7/10\n",
            "32/32 [==============================] - 1323s 42s/step - loss: 0.1044 - accuracy: 0.9660 - val_loss: 0.0840 - val_accuracy: 0.9790\n",
            "Epoch 8/10\n",
            "32/32 [==============================] - 1276s 40s/step - loss: 0.0958 - accuracy: 0.9720 - val_loss: 0.0883 - val_accuracy: 0.9750\n",
            "Epoch 9/10\n",
            "32/32 [==============================] - 1323s 42s/step - loss: 0.0709 - accuracy: 0.9830 - val_loss: 0.0873 - val_accuracy: 0.9710\n",
            "Epoch 10/10\n",
            "32/32 [==============================] - 1279s 41s/step - loss: 0.0847 - accuracy: 0.9630 - val_loss: 0.0895 - val_accuracy: 0.9680\n"
          ]
        }
      ],
      "source": [
        "r= model.fit(\n",
        "    training_set,\n",
        "    validation_data=test_set,\n",
        "    epochs=10,\n",
        "    steps_per_epoch=len(training_set),\n",
        "    validation_steps=len(test_set)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rAligah5awy"
      },
      "source": [
        "#Plotting the Losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "040VS1XX5foz"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MKOuu9Xk5kmf",
        "outputId": "ab8948d3-af65-4254-da0d-e540805add04"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "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": [
        "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')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39QZe6GCDJb-"
      },
      "source": [
        "#Plotting Accuracy \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "91mmHaeADMdJ",
        "outputId": "de77cfe7-d8fe-4630-af46-c1546ddf2cb1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "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": [
        "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": "markdown",
      "metadata": {
        "id": "yStFlwRWHNK2"
      },
      "source": [
        "#Saving the File as .h5 Type\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "iDaGubcJHQ_d"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import load_model \n",
        "model.save('model_vgg16.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JF0z6LvHHcTd"
      },
      "source": [
        "#Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIU8dAvcHeYJ",
        "outputId": "983b77e0-b64f-4a04-f35b-8297723ab9b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "32/32 [==============================] - 646s 20s/step\n"
          ]
        }
      ],
      "source": [
        "y_pred=model.predict(test_set)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOCrPkdAHjgz",
        "outputId": "826980dd-a269-49e2-ed78-6a2266a4a886"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[9.9832577e-01, 1.6741697e-03],\n",
              "       [9.9874228e-01, 1.2578012e-03],\n",
              "       [8.6660334e-04, 9.9913335e-01],\n",
              "       ...,\n",
              "       [4.9327421e-03, 9.9506718e-01],\n",
              "       [1.8144242e-02, 9.8185569e-01],\n",
              "       [8.0379206e-05, 9.9991959e-01]], dtype=float32)"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "eXT2XuynHmRa"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "y_pred=np.argmax(y_pred,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w37eiO1dHtLb",
        "outputId": "4e892153-3d21-46d5-aed5-34d5b7df1161"
      },
      "outputs": [
        {
          "data": {
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              "       0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
              "       0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 1, 0, 0, 0, 1, 1, 1, 1, 1])"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "gnPrk5IMHt-K"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing import image\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "neSsdoFYH1Po"
      },
      "outputs": [],
      "source": [
        "model=load_model('model_vgg16.h5')\n",
        "img=image.load_img('/content/drive/MyDrive/cats_and_dogs/cats_and_dogs/validation/cats/cat.2000.jpg',target_size=(224,224))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "zBrjUrdMISG7"
      },
      "outputs": [],
      "source": [
        "x=image.img_to_array(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "ITsZQM7LIXA_"
      },
      "outputs": [],
      "source": [
        "Z=plt.imread('/content/drive/MyDrive/cats_and_dogs/cats_and_dogs/validation/cats/cat.2000.jpg')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "tzzDXmcyIevS",
        "outputId": "a5dece3f-c090-4a93-e63d-bf8d0345b21b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb6d96f8760>"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(Z)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s27coclVIj2o",
        "outputId": "c396d854-d818-42e5-f95c-18aef21ce380"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "hF6UqQkKm49N"
      },
      "outputs": [],
      "source": [
        "x=x/255"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "_w6wf6MoInPc"
      },
      "outputs": [],
      "source": [
        "from keras.applications.vgg16 import preprocess_input "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0690VJUIs2L",
        "outputId": "e6759fad-3218-4851-df02-0186ff74d52d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 224, 224, 3)"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x=np.expand_dims(x,axis=0)\n",
        "img_data=preprocess_input(x)\n",
        "img_data.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EddcOQ_jI3XU",
        "outputId": "33cc45fc-6de6-4aa6-bfe9-906b886dab96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 1s 693ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[9.9926478e-01, 7.3516247e-04]], dtype=float32)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.predict(img_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1w1bTK6xI-t6",
        "outputId": "8c50e4e9-119c-4d44-95b9-d68dcb8ced5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n"
          ]
        }
      ],
      "source": [
        "result=np.argmax(model.predict(img_data),axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UmNU4OYHJFj8",
        "outputId": "731d985c-1845-47a2-fc1b-fa98849090c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yFNHfYeyJGNc",
        "outputId": "ce07171b-b5e7-413f-d883-b5518517a272"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cat\n"
          ]
        }
      ],
      "source": [
        "if result[0] == 1:\n",
        "  prediction = \"Dog\"\n",
        "  print(prediction)\n",
        "else:\n",
        "  prediction=\"cat\"\n",
        "  print(prediction)\n",
        "  "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyO4dJQ0KDQhc+ffgdWt1khv",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}