vgg19.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "HngQPGO0_2Xl"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "import cv2\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from keras.applications.vgg19 import preprocess_input\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow \n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import InputLayer, BatchNormalization, Dropout, Flatten, Dense, Activation, MaxPool2D, Conv2D\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.applications.vgg19 import VGG19\n",
        "from keras.preprocessing.image import load_img, img_to_array\n",
        "from keras.metrics import Recall,Precision\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "from keras import models, layers\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "from pathlib import Path\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = 'all'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "O8iujAw8AHJG"
      },
      "outputs": [],
      "source": [
        "root_path = Path(\"/content/Fish_Dataset/Fish_Dataset\")\n",
        "sub_paths = root_path.glob(r'**/*.png')\n",
        "#converting to list, will deal with generator later\n",
        "sub_paths = list(sub_paths)\n",
        "\n",
        "#creating temporary list to handle generator\n",
        "data = []\n",
        "for i in range(len(sub_paths)):\n",
        "    #get the 2nd from last directory name\n",
        "    label = os.path.split(os.path.split(sub_paths[i])[0])[1]\n",
        "    data.append([str(sub_paths[i]), label])\n",
        "    \n",
        "#Appending data to dataframe\n",
        "df = pd.DataFrame(data, columns=['path', 'label'])\n",
        "#Removing ground truths \n",
        "df = df[df['label'].apply(lambda x: x[-2:] != 'GT')].reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKAQwmxEDJVQ",
        "outputId": "54929152-b925-4dfd-e0f0-d6b55a84c691"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "path     7200\n",
              "label    7200\n",
              "dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "path     1800\n",
              "label    1800\n",
              "dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Splitting into training, validation, and testing data\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=2, shuffle=True)\n",
        "train_df.count()\n",
        "test_df.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdCy7aqsAJsL",
        "outputId": "33789847-f964-498d-aa9a-57c0a0ce1770"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 5760 validated image filenames belonging to 9 classes.\n",
            "Found 1440 validated image filenames belonging to 9 classes.\n",
            "Found 1800 validated image filenames belonging to 9 classes.\n"
          ]
        }
      ],
      "source": [
        "#Importing images using image data preprocessing provided from keras\n",
        "train_generator = ImageDataGenerator(validation_split=0.2)\n",
        "test_generator = ImageDataGenerator()\n",
        "\n",
        "train_imgs = train_generator.flow_from_dataframe(\n",
        "    dataframe = train_df,\n",
        "    x_col = \"path\",\n",
        "    y_col = \"label\",\n",
        "    target_size = (224, 224),\n",
        "    color_mode = \"rgb\",\n",
        "    class_mode = \"categorical\",\n",
        "    batch_size = 32,\n",
        "    shuffle = True,\n",
        "    subset = \"training\"\n",
        ")\n",
        "\n",
        "val_imgs = train_generator.flow_from_dataframe(\n",
        "    dataframe = train_df,\n",
        "    x_col = \"path\",\n",
        "    y_col = \"label\",\n",
        "    target_size = (224, 224),\n",
        "    color_mode = \"rgb\",\n",
        "    class_mode = \"categorical\",\n",
        "    batch_size = 32,\n",
        "    shuffle = True,\n",
        "    subset = \"validation\"\n",
        ")\n",
        "\n",
        "test_imgs = test_generator.flow_from_dataframe(\n",
        "    dataframe = test_df,\n",
        "    x_col = \"path\",\n",
        "    y_col = \"label\",\n",
        "    target_size = (224, 224),\n",
        "    color_mode = \"rgb\",\n",
        "    class_mode = \"categorical\",\n",
        "    batch_size = 32,\n",
        "    shuffle = False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "_8SKPsmBBkva"
      },
      "outputs": [],
      "source": [
        "# Model Initialization\n",
        "\n",
        "base_model = VGG19(input_shape=(224,224,3), \n",
        "                         include_top=False,\n",
        "                         weights=\"imagenet\")\n",
        "\n",
        "# Freezing Layers\n",
        "\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable=False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-nxDOw3BpK7",
        "outputId": "0313880b-976d-40e7-d9ed-3643dd21ae15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " vgg19 (Functional)          (None, 7, 7, 512)         20024384  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 25088)             0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 25088)            100352    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               12845568  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 512)              2048      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 9)                 4617      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 32,976,969\n",
            "Trainable params: 12,901,385\n",
            "Non-trainable params: 20,075,584\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Building Model\n",
        "model=Sequential()\n",
        "model.add(base_model)\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Flatten())\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(512,kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(9,activation='softmax'))\n",
        "\n",
        "# Summary\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zOJVsNx7BsbS",
        "outputId": "f8303305-a977-4f95-da79-be6b1d5528ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 10,
          "metadata": {
            "image/png": {
              "height": 200,
              "width": 400
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "4\n",
        "\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from IPython.display import SVG, Image\n",
        "plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)\n",
        "Image('model.png',width=400, height=200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QK3kOsj5BwZI",
        "outputId": "a0b84e41-3790-495b-83f6-2cb3d6b56522"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "# Model Compile \n",
        "\n",
        "OPT    = tensorflow.keras.optimizers.Adam(lr=0.001)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              metrics=['accuracy',Precision(),Recall()],\n",
        "              optimizer=OPT)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "1Ts7TnbHBymG"
      },
      "outputs": [],
      "source": [
        "# Defining Callbacks\n",
        "\n",
        "filepath = './best_weights.hdf5'\n",
        "\n",
        "earlystopping = EarlyStopping(monitor = 'val_accuracy', \n",
        "                              mode = 'max' , \n",
        "                              patience = 5,\n",
        "                              verbose = 1)\n",
        "\n",
        "checkpoint    = ModelCheckpoint(filepath, \n",
        "                                monitor = 'val_accuracy', \n",
        "                                mode='max', \n",
        "                                save_best_only=True, \n",
        "                                verbose = 1)\n",
        "\n",
        "\n",
        "callback_list = [earlystopping, checkpoint]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wOubIdCBz5y",
        "outputId": "87f25f92-57ae-4f15-8d7c-9b76bcde0d66"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "180/180 [==============================] - ETA: 0s - loss: 0.2516 - accuracy: 0.9222 - precision: 0.9454 - recall: 0.9047\n",
            "Epoch 1: val_accuracy improved from -inf to 0.99167, saving model to ./best_weights.hdf5\n",
            "180/180 [==============================] - 96s 495ms/step - loss: 0.2516 - accuracy: 0.9222 - precision: 0.9454 - recall: 0.9047 - val_loss: 0.0229 - val_accuracy: 0.9917 - val_precision: 0.9924 - val_recall: 0.9917\n",
            "Epoch 2/20\n",
            "180/180 [==============================] - ETA: 0s - loss: 0.0197 - accuracy: 0.9957 - precision: 0.9967 - recall: 0.9951\n",
            "Epoch 2: val_accuracy improved from 0.99167 to 0.99792, saving model to ./best_weights.hdf5\n",
            "180/180 [==============================] - 82s 457ms/step - loss: 0.0197 - accuracy: 0.9957 - precision: 0.9967 - recall: 0.9951 - val_loss: 0.0114 - val_accuracy: 0.9979 - val_precision: 0.9979 - val_recall: 0.9965\n",
            "Epoch 3/20\n",
            "180/180 [==============================] - ETA: 0s - loss: 0.0085 - accuracy: 0.9988 - precision: 0.9990 - recall: 0.9988\n",
            "Epoch 3: val_accuracy did not improve from 0.99792\n",
            "180/180 [==============================] - 78s 435ms/step - loss: 0.0085 - accuracy: 0.9988 - precision: 0.9990 - recall: 0.9988 - val_loss: 0.0112 - val_accuracy: 0.9972 - val_precision: 0.9986 - val_recall: 0.9965\n",
            "Epoch 4/20\n",
            "180/180 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993\n",
            "Epoch 4: val_accuracy did not improve from 0.99792\n",
            "180/180 [==============================] - 78s 433ms/step - loss: 0.0057 - accuracy: 0.9993 - precision: 0.9993 - recall: 0.9993 - val_loss: 0.0229 - val_accuracy: 0.9965 - val_precision: 0.9965 - val_recall: 0.9965\n",
            "Epoch 5/20\n",
            "180/180 [==============================] - ETA: 0s - loss: 0.0072 - accuracy: 0.9988 - precision: 0.9988 - recall: 0.9988\n",
            "Epoch 5: val_accuracy did not improve from 0.99792\n",
            "180/180 [==============================] - 78s 430ms/step - loss: 0.0072 - accuracy: 0.9988 - precision: 0.9988 - recall: 0.9988 - val_loss: 0.0363 - val_accuracy: 0.9924 - val_precision: 0.9924 - val_recall: 0.9924\n",
            "Epoch 6/20\n",
            "180/180 [==============================] - ETA: 0s - loss: 0.0080 - accuracy: 0.9972 - precision: 0.9974 - recall: 0.9972\n",
            "Epoch 6: val_accuracy did not improve from 0.99792\n",
            "180/180 [==============================] - 77s 430ms/step - loss: 0.0080 - accuracy: 0.9972 - precision: 0.9974 - recall: 0.9972 - val_loss: 0.0648 - val_accuracy: 0.9868 - val_precision: 0.9868 - val_recall: 0.9868\n",
            "Epoch 7/20\n",
            "180/180 [==============================] - ETA: 0s - loss: 0.0178 - accuracy: 0.9953 - precision: 0.9957 - recall: 0.9950\n",
            "Epoch 7: val_accuracy did not improve from 0.99792\n",
            "180/180 [==============================] - 78s 431ms/step - loss: 0.0178 - accuracy: 0.9953 - precision: 0.9957 - recall: 0.9950 - val_loss: 0.0432 - val_accuracy: 0.9917 - val_precision: 0.9924 - val_recall: 0.9917\n",
            "Epoch 7: early stopping\n"
          ]
        }
      ],
      "source": [
        "model_history=model.fit(train_imgs,\n",
        "                        validation_data=val_imgs,\n",
        "                        epochs = 20,\n",
        "                        callbacks = callback_list,\n",
        "                        verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Fiq66vQ1B1Jb"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "model.save('./best_weights.hdf5')\n",
        "#model = keras.models.load_model('./best_weights.hdf5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Zzm_o7joB3De",
        "outputId": "1edb78c2-85b7-418e-caf7-4d47529b36ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee61ee550>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee61eebd0>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'model accuracy')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'accuracy')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epoch')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ee61eec50>"
            ]
          },
          "execution_count": 15,
          "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"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee61431d0>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee6143790>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'train set loss')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epoch')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ee6138f50>"
            ]
          },
          "execution_count": 15,
          "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"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee60bdd90>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee60c9350>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, ' precision')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'precision')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epoch')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ee60ffa50>"
            ]
          },
          "execution_count": 15,
          "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"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee603e790>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ee603ed10>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, ' recall')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'recall')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epoch')"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ee603efd0>"
            ]
          },
          "execution_count": 15,
          "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.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title('train set loss')\n",
        "\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(model_history.history['precision'])\n",
        "plt.plot(model_history.history['val_precision'])\n",
        "plt.title(' precision')\n",
        "plt.ylabel('precision')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(model_history.history['recall'])\n",
        "plt.plot(model_history.history['val_recall'])\n",
        "plt.title(' recall')\n",
        "plt.ylabel('recall')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper right')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssUbyQ0fB4T4",
        "outputId": "e50d3b13-c813-47b5-f16d-78db1f72c72a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "57/57 [==============================] - 24s 418ms/step - loss: 0.0444 - accuracy: 0.9867 - precision: 0.9872 - recall: 0.9867\n",
            "Test Accuracy: 0.987\n",
            "Test Precision: 0.987\n",
            "Test Recall: 0.987\n",
            "Test loss: 0.044\n"
          ]
        }
      ],
      "source": [
        "loss, accuracy , precision,recall=model.evaluate(test_imgs)\n",
        "print('Test Accuracy: %.3f' % accuracy)\n",
        "print('Test Precision: %.3f' % precision)\n",
        "print('Test Recall: %.3f' % recall)\n",
        "print('Test loss: %.3f' % loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KvGs-2QOB5ak"
      },
      "outputs": [],
      "source": [
        "y_pred = model.predict(test_imgs)\n",
        "y_pred = np.argmax(y_pred,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OGWPI5bjB67-"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_imgs.labels,y_pred))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.6 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}