Resnet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cNaxtNnWe0M_"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.layers import Dense, Conv2D, BatchNormalization, Activation\n",
        "from keras.layers import AveragePooling2D, Input, Flatten\n",
        "from keras.optimizers import Adam\n",
        "from keras.regularizers import l2\n",
        "from keras.models import Model\n",
        "import cv2\n",
        "from glob import glob\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pathlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes=38"
      ],
      "metadata": {
        "id": "CWvcjQ1Re97j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/spMohanty/PlantVillage-Dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnd9fc2qe-ni",
        "outputId": "31ee10e6-92f8-46c2-d9a1-3ef3c95a420d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'PlantVillage-Dataset'...\n",
            "remote: Enumerating objects: 163229, done.\u001b[K\n",
            "remote: Total 163229 (delta 0), reused 0 (delta 0), pack-reused 163229\u001b[K\n",
            "Receiving objects: 100% (163229/163229), 2.00 GiB | 31.88 MiB/s, done.\n",
            "Resolving deltas: 100% (99/99), done.\n",
            "Updating files: 100% (182401/182401), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_folder_path = '/content/PlantVillage-Dataset/raw/color'\n",
        "# Get the list of class (species) folders\n",
        "train_class_folders = glob(train_folder_path + '/*')\n",
        "\n",
        "# Initialize lists to store images and labels\n",
        "images = []\n",
        "labels = []\n",
        "\n",
        "# Load images and labels from subfolders\n",
        "for class_folder in train_class_folders:\n",
        "    label = (class_folder.split('/')[-1])  # Adjust the index based on your folder structure\n",
        "    # Get the list of image file paths in the current class folder\n",
        "    image_files = glob(class_folder + '/*.JPG')\n",
        "    image_files2 = glob(class_folder + '/*.jpg')  # Update the extension based on your image format\n",
        "    # Load and resize images\n",
        "    for image_file in image_files:\n",
        "        img = cv2.imread(image_file)\n",
        "        img = cv2.resize(img, (60, 60))  # Adjust the size as needed\n",
        "        images.append(img)\n",
        "        labels.append(label)\n",
        "\n",
        "    for image_file in image_files2:\n",
        "        img = cv2.imread(image_file)\n",
        "        img = cv2.resize(img, (60, 60))  # Adjust the size as needed\n",
        "        images.append(img)\n",
        "        labels.append(label)\n",
        "\n",
        "# Convert lists to numpy arrays\n",
        "images = np.array(images)\n",
        "labels = np.array(labels)\n",
        "\n",
        "# Optionally, normalize pixel values to be in the range [0, 1]\n",
        "images = images.astype('float32') / 255.0\n",
        "\n",
        "# Print the shape of the data\n",
        "print(\"x_train shape:\", images.shape)\n",
        "print(\"y_train shape:\", labels.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdNuBABse_K7",
        "outputId": "442ccc39-c1f4-4409-8d2e-dc2c329fe3d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (54303, 60, 60, 3)\n",
            "y_train shape: (54303,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "# Label encoding\n",
        "label_encoder = LabelEncoder()\n",
        "labels1 = label_encoder.fit_transform(labels)\n",
        "\n",
        "# One-hot encoding\n",
        "labels = to_categorical(labels1,num_classes)\n",
        "\n",
        "# Display the results\n",
        "print(\"Integer Encoded Labels:\", labels1)\n",
        "print(\"One-Hot Encoded Labels:\\n\", labels)"
      ],
      "metadata": {
        "id": "3iuJHgW1gkel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f1dddb4-e5af-443b-a377-c9e92a98db07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Integer Encoded Labels: [23 23 23 ... 36 36 36]\n",
            "One-Hot Encoded Labels:\n",
            " [[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 1. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_temp, y_train, y_temp = train_test_split(images, labels, test_size=0.3, random_state=0)\n",
        "x_val, x_test, y_val, y_test=train_test_split(x_temp, y_temp, test_size=0.5,random_state=0)"
      ],
      "metadata": {
        "id": "RwAmstZygnpF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape = images.shape[1:]\n",
        "print(input_shape)"
      ],
      "metadata": {
        "id": "O9DH_0P1gpKo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "624f8fea-845b-4b7a-a9e9-dbc8350991b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 64, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_data(images[0])"
      ],
      "metadata": {
        "id": "xbJsiJ6zifEX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "6d24b158-97c5-4df4-9fc1-1351c00f13b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the ResNet-50 model\n",
        "model = keras.applications.ResNet50V2(\n",
        "    include_top=True,\n",
        "    weights=None,\n",
        "    input_tensor=None,\n",
        "    input_shape=(64, 64, 3), pooling='avg', classes=38,\n",
        "    classifier_activation=\"softmax\",\n",
        ")\n",
        "batch=32\n",
        "# Define early stopping\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=10,\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "                 optimizer=Adam(learning_rate=0.001),\n",
        "                 metrics=['accuracy'])\n",
        "\n"
      ],
      "metadata": {
        "id": "WOZG2haNe_FG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trained_model1 = model.fit(x_train, y_train,\n",
        "    epochs=50,\n",
        "    batch_size=batch,\n",
        "    verbose=1,\n",
        "    validation_data=(x_val, y_val),\n",
        "    callbacks=[early_stopping])"
      ],
      "metadata": {
        "id": "koJHtrYke_B9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c306cc3a-0fd6-4627-f8b5-87d4b8f12ed5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1188/1188 [==============================] - 110s 62ms/step - loss: 1.0454 - accuracy: 0.6917 - val_loss: 1.0608 - val_accuracy: 0.7002\n",
            "Epoch 2/50\n",
            "1188/1188 [==============================] - 70s 59ms/step - loss: 0.4823 - accuracy: 0.8470 - val_loss: 2.9227 - val_accuracy: 0.4478\n",
            "Epoch 3/50\n",
            "1188/1188 [==============================] - 67s 57ms/step - loss: 0.3511 - accuracy: 0.8862 - val_loss: 2.3686 - val_accuracy: 0.5569\n",
            "Epoch 4/50\n",
            "1188/1188 [==============================] - 68s 57ms/step - loss: 0.2725 - accuracy: 0.9112 - val_loss: 0.7716 - val_accuracy: 0.7737\n",
            "Epoch 5/50\n",
            "1188/1188 [==============================] - 69s 58ms/step - loss: 0.2059 - accuracy: 0.9316 - val_loss: 1.0584 - val_accuracy: 0.7352\n",
            "Epoch 6/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.1819 - accuracy: 0.9402 - val_loss: 0.5305 - val_accuracy: 0.8483\n",
            "Epoch 7/50\n",
            "1188/1188 [==============================] - 67s 57ms/step - loss: 0.1610 - accuracy: 0.9481 - val_loss: 0.4180 - val_accuracy: 0.8732\n",
            "Epoch 8/50\n",
            "1188/1188 [==============================] - 68s 57ms/step - loss: 0.1086 - accuracy: 0.9641 - val_loss: 0.7228 - val_accuracy: 0.8162\n",
            "Epoch 9/50\n",
            "1188/1188 [==============================] - 67s 57ms/step - loss: 0.0964 - accuracy: 0.9690 - val_loss: 0.7155 - val_accuracy: 0.8217\n",
            "Epoch 10/50\n",
            "1188/1188 [==============================] - 69s 58ms/step - loss: 0.1145 - accuracy: 0.9638 - val_loss: 0.3908 - val_accuracy: 0.8933\n",
            "Epoch 11/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0804 - accuracy: 0.9736 - val_loss: 1.1382 - val_accuracy: 0.7498\n",
            "Epoch 12/50\n",
            "1188/1188 [==============================] - 67s 57ms/step - loss: 0.0600 - accuracy: 0.9799 - val_loss: 0.3166 - val_accuracy: 0.9158\n",
            "Epoch 13/50\n",
            "1188/1188 [==============================] - 68s 57ms/step - loss: 0.0615 - accuracy: 0.9792 - val_loss: 1.8784 - val_accuracy: 0.6318\n",
            "Epoch 14/50\n",
            "1188/1188 [==============================] - 68s 57ms/step - loss: 0.0648 - accuracy: 0.9794 - val_loss: 0.2495 - val_accuracy: 0.9295\n",
            "Epoch 15/50\n",
            "1188/1188 [==============================] - 69s 58ms/step - loss: 0.0510 - accuracy: 0.9837 - val_loss: 0.3219 - val_accuracy: 0.9150\n",
            "Epoch 16/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0411 - accuracy: 0.9866 - val_loss: 0.4250 - val_accuracy: 0.8928\n",
            "Epoch 17/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0417 - accuracy: 0.9855 - val_loss: 0.3118 - val_accuracy: 0.9157\n",
            "Epoch 18/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0393 - accuracy: 0.9875 - val_loss: 0.4257 - val_accuracy: 0.8883\n",
            "Epoch 19/50\n",
            "1188/1188 [==============================] - 68s 58ms/step - loss: 0.0355 - accuracy: 0.9888 - val_loss: 0.3546 - val_accuracy: 0.9076\n",
            "Epoch 20/50\n",
            "1188/1188 [==============================] - 69s 58ms/step - loss: 0.0492 - accuracy: 0.9846 - val_loss: 0.2907 - val_accuracy: 0.9258\n",
            "Epoch 21/50\n",
            "1188/1188 [==============================] - 68s 57ms/step - loss: 0.0276 - accuracy: 0.9911 - val_loss: 0.1504 - val_accuracy: 0.9587\n",
            "Epoch 22/50\n",
            "1188/1188 [==============================] - 70s 59ms/step - loss: 0.0235 - accuracy: 0.9927 - val_loss: 0.2876 - val_accuracy: 0.9271\n",
            "Epoch 23/50\n",
            "1188/1188 [==============================] - 69s 58ms/step - loss: 0.0315 - accuracy: 0.9898 - val_loss: 0.1657 - val_accuracy: 0.9576\n",
            "Epoch 24/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0290 - accuracy: 0.9902 - val_loss: 0.6363 - val_accuracy: 0.8622\n",
            "Epoch 25/50\n",
            "1188/1188 [==============================] - 68s 57ms/step - loss: 0.0230 - accuracy: 0.9929 - val_loss: 0.2719 - val_accuracy: 0.9328\n",
            "Epoch 26/50\n",
            "1188/1188 [==============================] - 66s 56ms/step - loss: 0.0309 - accuracy: 0.9900 - val_loss: 0.5154 - val_accuracy: 0.8791\n",
            "Epoch 27/50\n",
            "1188/1188 [==============================] - 66s 56ms/step - loss: 0.0218 - accuracy: 0.9932 - val_loss: 0.4646 - val_accuracy: 0.8948\n",
            "Epoch 28/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0225 - accuracy: 0.9929 - val_loss: 0.2083 - val_accuracy: 0.9479\n",
            "Epoch 29/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0340 - accuracy: 0.9903 - val_loss: 0.1915 - val_accuracy: 0.9517\n",
            "Epoch 30/50\n",
            "1188/1188 [==============================] - 66s 56ms/step - loss: 0.0189 - accuracy: 0.9945 - val_loss: 0.8036 - val_accuracy: 0.8344\n",
            "Epoch 31/50\n",
            "1188/1188 [==============================] - 67s 56ms/step - loss: 0.0219 - accuracy: 0.9931 - val_loss: 0.4398 - val_accuracy: 0.9019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(x_test,y_test,verbose=1)"
      ],
      "metadata": {
        "id": "flFh8K3ve--w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c11490e3-5600-4962-8d03-a52cae7be5bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "255/255 [==============================] - 5s 19ms/step - loss: 0.1436 - accuracy: 0.9624\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.14364394545555115, 0.9624355435371399]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (20,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Train and Validation Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.plot(trained_model1.history['loss'],label=\"Train Loss\")\n",
        "plt.plot(trained_model1.history['val_loss'], label=\"Validation Loss\")\n",
        "plt.xlim(0,32)\n",
        "plt.ylim(0,10)\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Train and Validation Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(trained_model1.history['accuracy'], label=\"Train Accuracy\")\n",
        "plt.plot(trained_model1.history['val_accuracy'], label=\"Validation Accuracy\")\n",
        "plt.xlim(0,32)\n",
        "plt.ylim(0,1)\n",
        "plt.legend()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "XPrKnIEPe-5g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "702bedbe-bd6b-4cf4-9951-3ca998d20136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the ResNet-101 model\n",
        "model = keras.applications.ResNet101V2(\n",
        "    include_top=True,\n",
        "    weights=None,\n",
        "    input_tensor=None,\n",
        "    input_shape=(60, 60, 3), pooling='avg', classes=38,\n",
        "    classifier_activation=\"softmax\",\n",
        ")\n",
        "batch=32\n",
        "# Define early stopping\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=10,\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "                 optimizer=Adam(learning_rate=0.001),\n",
        "                 metrics=['accuracy'])\n",
        "\n"
      ],
      "metadata": {
        "id": "g5XTevZ-E53o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trained_model2 = model.fit(x_train, y_train,\n",
        "    epochs=50,\n",
        "    batch_size=batch,\n",
        "    verbose=1,\n",
        "    validation_data=(x_val, y_val),\n",
        "    callbacks=[early_stopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmPsYDQdE7lf",
        "outputId": "23059431-4fcc-4e45-d972-dd511c2c4402"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1188/1188 [==============================] - 184s 104ms/step - loss: 1.1651 - accuracy: 0.6598 - val_loss: 1.9225 - val_accuracy: 0.5476\n",
            "Epoch 2/50\n",
            "1188/1188 [==============================] - 122s 102ms/step - loss: 0.5435 - accuracy: 0.8288 - val_loss: 3.2997 - val_accuracy: 0.4000\n",
            "Epoch 3/50\n",
            "1188/1188 [==============================] - 122s 103ms/step - loss: 0.3886 - accuracy: 0.8731 - val_loss: 1.4044 - val_accuracy: 0.6258\n",
            "Epoch 4/50\n",
            "1188/1188 [==============================] - 118s 99ms/step - loss: 0.2963 - accuracy: 0.9033 - val_loss: 3.7757 - val_accuracy: 0.3449\n",
            "Epoch 5/50\n",
            "1188/1188 [==============================] - 123s 103ms/step - loss: 0.2447 - accuracy: 0.9209 - val_loss: 1.0957 - val_accuracy: 0.7084\n",
            "Epoch 6/50\n",
            "1188/1188 [==============================] - 118s 100ms/step - loss: 0.1994 - accuracy: 0.9348 - val_loss: 1.3395 - val_accuracy: 0.6913\n",
            "Epoch 7/50\n",
            "1188/1188 [==============================] - 119s 100ms/step - loss: 0.1593 - accuracy: 0.9466 - val_loss: 0.9764 - val_accuracy: 0.7488\n",
            "Epoch 8/50\n",
            "1188/1188 [==============================] - 122s 103ms/step - loss: 0.1401 - accuracy: 0.9546 - val_loss: 0.9599 - val_accuracy: 0.7778\n",
            "Epoch 9/50\n",
            "1188/1188 [==============================] - 118s 99ms/step - loss: 0.1228 - accuracy: 0.9594 - val_loss: 0.7300 - val_accuracy: 0.8098\n",
            "Epoch 10/50\n",
            "1188/1188 [==============================] - 122s 102ms/step - loss: 0.0963 - accuracy: 0.9670 - val_loss: 1.0348 - val_accuracy: 0.7400\n",
            "Epoch 11/50\n",
            "1188/1188 [==============================] - 122s 103ms/step - loss: 0.0779 - accuracy: 0.9738 - val_loss: 1.3052 - val_accuracy: 0.6918\n",
            "Epoch 12/50\n",
            "1188/1188 [==============================] - 118s 99ms/step - loss: 0.0814 - accuracy: 0.9735 - val_loss: 0.4020 - val_accuracy: 0.8780\n",
            "Epoch 13/50\n",
            "1188/1188 [==============================] - 122s 103ms/step - loss: 0.0821 - accuracy: 0.9735 - val_loss: 0.3028 - val_accuracy: 0.9158\n",
            "Epoch 14/50\n",
            "1188/1188 [==============================] - 118s 99ms/step - loss: 0.0468 - accuracy: 0.9840 - val_loss: 1.3777 - val_accuracy: 0.7269\n",
            "Epoch 15/50\n",
            "1188/1188 [==============================] - 122s 102ms/step - loss: 0.0509 - accuracy: 0.9831 - val_loss: 0.4932 - val_accuracy: 0.8743\n",
            "Epoch 16/50\n",
            "1188/1188 [==============================] - 122s 103ms/step - loss: 0.0534 - accuracy: 0.9831 - val_loss: 0.2406 - val_accuracy: 0.9322\n",
            "Epoch 17/50\n",
            "1188/1188 [==============================] - 120s 101ms/step - loss: 0.0425 - accuracy: 0.9858 - val_loss: 0.5354 - val_accuracy: 0.8681\n",
            "Epoch 18/50\n",
            "1188/1188 [==============================] - 120s 101ms/step - loss: 0.0438 - accuracy: 0.9859 - val_loss: 0.6269 - val_accuracy: 0.8519\n",
            "Epoch 19/50\n",
            "1188/1188 [==============================] - 116s 98ms/step - loss: 0.0330 - accuracy: 0.9895 - val_loss: 1.2579 - val_accuracy: 0.7905\n",
            "Epoch 20/50\n",
            "1188/1188 [==============================] - 116s 98ms/step - loss: 0.0438 - accuracy: 0.9860 - val_loss: 0.3012 - val_accuracy: 0.9214\n",
            "Epoch 21/50\n",
            "1188/1188 [==============================] - 116s 98ms/step - loss: 0.0269 - accuracy: 0.9913 - val_loss: 0.4534 - val_accuracy: 0.8972\n",
            "Epoch 22/50\n",
            "1188/1188 [==============================] - 121s 102ms/step - loss: 0.0299 - accuracy: 0.9903 - val_loss: 0.6343 - val_accuracy: 0.8669\n",
            "Epoch 23/50\n",
            "1188/1188 [==============================] - 121s 102ms/step - loss: 0.0358 - accuracy: 0.9897 - val_loss: 0.4798 - val_accuracy: 0.8890\n",
            "Epoch 24/50\n",
            "1188/1188 [==============================] - 121s 102ms/step - loss: 0.0266 - accuracy: 0.9915 - val_loss: 0.3356 - val_accuracy: 0.9161\n",
            "Epoch 25/50\n",
            "1188/1188 [==============================] - 117s 99ms/step - loss: 0.0236 - accuracy: 0.9928 - val_loss: 0.2526 - val_accuracy: 0.9365\n",
            "Epoch 26/50\n",
            "1188/1188 [==============================] - 118s 99ms/step - loss: 0.0321 - accuracy: 0.9902 - val_loss: 0.8937 - val_accuracy: 0.7980\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(x_test,y_test,verbose=1)"
      ],
      "metadata": {
        "id": "2YC3K5WSE9Vu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d398b62f-23de-42d6-fd3b-67f1893c69a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "255/255 [==============================] - 7s 29ms/step - loss: 0.2269 - accuracy: 0.9365\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.22694270312786102, 0.9365332722663879]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (20,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Train and Validation Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.plot(trained_model2.history['loss'],label=\"Train Loss\")\n",
        "plt.plot(trained_model2.history['val_loss'], label=\"Validation Loss\")\n",
        "plt.xlim(0,32)\n",
        "plt.ylim(0,10)\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Train and Validation Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(trained_model2.history['accuracy'], label=\"Train Accuracy\")\n",
        "plt.plot(trained_model2.history['val_accuracy'], label=\"Validation Accuracy\")\n",
        "plt.xlim(0,32)\n",
        "plt.ylim(0,1)\n",
        "plt.legend()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "A2pri0pXE-N1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "c106d461-53e5-4da6-e051-d564a9347adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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}