Mini_Project_3.ipynb

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    "colab": {
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QoHaq46KzPeO",
        "outputId": "b8725ca8-911b-4493-8a92-6523f397ef95"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  clean_data.zip\n",
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          ]
        }
      ],
      "source": [
        "!unzip clean_data.zip -d data # unzip the downloaded dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "import numpy as np\n",
        "import cv2\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications.vgg19 import VGG19\n",
        "\n",
        "from tensorflow.keras.applications.vgg19 import preprocess_input\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, GlobalAveragePooling2D\n",
        "\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "from tensorflow.keras.saving import save_model\n",
        "\n",
        "print(tf.version.VERSION)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VAy2zoTmGal",
        "outputId": "f52b113d-62eb-4b3a-f0c7-6f7f704af8fa"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set the paths to the original and new data directories\n",
        "original_data_dir = '/content/data/process_data'\n",
        "train_dir = '/content/data/process_data/train'\n",
        "test_dir = '/content/data/process_data/test'\n",
        "valid_dir = '/content/data/process_data/valid'"
      ],
      "metadata": {
        "id": "ccaYZkhJnbVT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define data generators\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=20,  # rotate images by up to 20 degrees\n",
        "    width_shift_range=0.2,  # shift images horizontally by up to 20% of the image width\n",
        "    height_shift_range=0.2,  # shift images vertically by up to 20% of the image height\n",
        "    shear_range=0.2,  # apply shear transformation with a shear angle of up to 20 degrees\n",
        "    zoom_range=0.2,  # apply zoom transformation by up to 20%\n",
        "    horizontal_flip=True,  # flip images horizontally\n",
        "    fill_mode='nearest'  # fill any gaps created by the above transformations with the nearest pixel\n",
        ")\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size = (227,227),\n",
        "    batch_size=64,\n",
        "    class_mode='categorical')\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    valid_dir,\n",
        "    target_size = (227,227),\n",
        "    batch_size=64,\n",
        "    class_mode='categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9ZMzEocnKrA",
        "outputId": "ddc2fd59-d1b7-43ab-83db-5955eddc81c9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 280 images belonging to 40 classes.\n",
            "Found 120 images belonging to 40 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the weight decay value\n",
        "weight_decay = 0.00000\n",
        "\n",
        "# Create the AlexNet-like model\n",
        "model = tf.keras.Sequential([\n",
        "    # Conv1 + Relu with weight decay\n",
        "    tf.keras.layers.Conv2D(96, (11, 11), strides=(4, 4), input_shape=(227, 227, 3), padding='valid',\n",
        "                           kernel_regularizer=regularizers.l2(weight_decay)),\n",
        "    tf.keras.layers.ReLU(),\n",
        "    # Max Pooling\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid'),\n",
        "    # Norm\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    # Conv2 + Relu with weight decay\n",
        "    tf.keras.layers.Conv2D(256, (5, 5), strides=(1, 1), padding='same',\n",
        "                           kernel_regularizer=regularizers.l2(weight_decay)),\n",
        "    tf.keras.layers.ReLU(),\n",
        "    # Max Pooling\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid'),\n",
        "    # Norm\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    # Conv3 + Relu with weight decay\n",
        "    tf.keras.layers.Conv2D(384, (3, 3), strides=(1, 1), padding='same',\n",
        "                           kernel_regularizer=regularizers.l2(weight_decay)),\n",
        "    tf.keras.layers.ReLU(),\n",
        "    # Conv4 + Relu with weight decay\n",
        "    tf.keras.layers.Conv2D(384, (3, 3), strides=(1, 1), padding='same',\n",
        "                           kernel_regularizer=regularizers.l2(weight_decay)),\n",
        "    tf.keras.layers.ReLU(),\n",
        "    # Conv5 + Relu with weight decay\n",
        "    tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same',\n",
        "                           kernel_regularizer=regularizers.l2(weight_decay)),\n",
        "    tf.keras.layers.ReLU(),\n",
        "    # Max Pooling\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid'),\n",
        "    # Flatten\n",
        "    tf.keras.layers.Flatten(),\n",
        "    # FC6 + Relu with weight decay\n",
        "    tf.keras.layers.Dense(4096, activation='relu', kernel_regularizer=regularizers.l2(weight_decay)),\n",
        "    # Dropout\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    # FC7 + Relu with weight decay\n",
        "    tf.keras.layers.Dense(4096, activation='relu', kernel_regularizer=regularizers.l2(weight_decay)),\n",
        "    # Dropout\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    # FC8 + Softmax with weight decay\n",
        "    tf.keras.layers.Dense(40, activation='softmax', kernel_regularizer=regularizers.l2(weight_decay))\n",
        "])\n",
        "\n",
        "# Optimizer with initial learning rate of 0.01\n",
        "optimizer = tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.09)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Print the model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdLdmLMtnm30",
        "outputId": "c155120d-4afd-4822-b7f6-5894730fbb25"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 55, 55, 96)        34944     \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 55, 55, 96)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 27, 27, 96)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 27, 27, 96)       384       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 27, 27, 256)       614656    \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 27, 27, 256)       0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 13, 13, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 13, 13, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 13, 13, 384)       885120    \n",
            "                                                                 \n",
            " re_lu_2 (ReLU)              (None, 13, 13, 384)       0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 13, 13, 384)       1327488   \n",
            "                                                                 \n",
            " re_lu_3 (ReLU)              (None, 13, 13, 384)       0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 13, 13, 256)       884992    \n",
            "                                                                 \n",
            " re_lu_4 (ReLU)              (None, 13, 13, 256)       0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 6, 6, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 9216)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              37752832  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 40)                163880    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 58,446,632\n",
            "Trainable params: 58,445,928\n",
            "Non-trainable params: 704\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    epochs=20,  # specify the number of epochs for training\n",
        "    validation_data=val_generator\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRPvcGCKnw0u",
        "outputId": "d0aadb40-6f32-470b-c418-a66845d8b4d4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 63s 12s/step - loss: 3.9681 - accuracy: 0.0286 - val_loss: 3.6888 - val_accuracy: 0.0250\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 55s 11s/step - loss: 3.9732 - accuracy: 0.0143 - val_loss: 3.6884 - val_accuracy: 0.0333\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 59s 13s/step - loss: 3.9091 - accuracy: 0.0321 - val_loss: 3.6885 - val_accuracy: 0.0500\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 55s 12s/step - loss: 3.8744 - accuracy: 0.0214 - val_loss: 3.6885 - val_accuracy: 0.0417\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 59s 12s/step - loss: 3.8646 - accuracy: 0.0321 - val_loss: 3.6883 - val_accuracy: 0.0167\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 54s 11s/step - loss: 3.7722 - accuracy: 0.0286 - val_loss: 3.6875 - val_accuracy: 0.0417\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 59s 12s/step - loss: 3.7912 - accuracy: 0.0143 - val_loss: 3.6868 - val_accuracy: 0.0417\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 66s 13s/step - loss: 3.7640 - accuracy: 0.0321 - val_loss: 3.6874 - val_accuracy: 0.0167\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 55s 12s/step - loss: 3.7827 - accuracy: 0.0214 - val_loss: 3.6875 - val_accuracy: 0.0250\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 55s 11s/step - loss: 3.7290 - accuracy: 0.0571 - val_loss: 3.6878 - val_accuracy: 0.0583\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 55s 11s/step - loss: 3.7593 - accuracy: 0.0321 - val_loss: 3.6872 - val_accuracy: 0.0750\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 56s 11s/step - loss: 3.7294 - accuracy: 0.0321 - val_loss: 3.6873 - val_accuracy: 0.0667\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 60s 12s/step - loss: 3.7360 - accuracy: 0.0357 - val_loss: 3.6874 - val_accuracy: 0.0750\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 55s 11s/step - loss: 3.6794 - accuracy: 0.0893 - val_loss: 3.6873 - val_accuracy: 0.0583\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 61s 12s/step - loss: 3.7048 - accuracy: 0.0464 - val_loss: 3.6867 - val_accuracy: 0.0500\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 60s 11s/step - loss: 3.6790 - accuracy: 0.0429 - val_loss: 3.6859 - val_accuracy: 0.0583\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 56s 11s/step - loss: 3.6964 - accuracy: 0.0321 - val_loss: 3.6854 - val_accuracy: 0.0833\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 61s 12s/step - loss: 3.6788 - accuracy: 0.0393 - val_loss: 3.6855 - val_accuracy: 0.0917\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 56s 11s/step - loss: 3.6550 - accuracy: 0.0357 - val_loss: 3.6849 - val_accuracy: 0.0667\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 55s 12s/step - loss: 3.6765 - accuracy: 0.0357 - val_loss: 3.6849 - val_accuracy: 0.0500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot the training and validation accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot the training and validation loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "Tqqqlz1rrG8b",
        "outputId": "42225135-600e-49c5-fedb-a2d199dfa3aa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "filepath = '/content/models/Alexnet'\n",
        "\n",
        "save_model(\n",
        "    model, filepath, overwrite=True, save_format=None\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQj1ZW9xEAl1",
        "outputId": "6ad6ac15-2acc-42c4-e758-e2cf84e5a65c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Load VGG19 model without top layers\n",
        "base_model = VGG19(weights='imagenet', include_top=False)\n",
        "\n",
        "# Add top layers\n",
        "x = base_model.output\n",
        "x1 = GlobalAveragePooling2D()(x)\n",
        "x2 = Dense(4096, activation='relu')(x1) # Keep the fully connected layer with 4096 units\n",
        "x3 = Dense(4096, activation='relu')(x2) # Keep the fully connected layer with 4096 units\n",
        "predictions = Dense(40, activation='softmax')(x3) # Change to 40 units for 40-class classifier\n",
        "\n",
        "# Create a new model with the added top layers\n",
        "model_1 = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# Freeze base model layers\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Compile the model\n",
        "model_1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Print the model summary\n",
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnjRiJG_Oi9r",
        "outputId": "e7d8db58-c6c3-49e7-975e-2a32714d9223"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, None, None, 3)]   0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, None, None, 64)    1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, None, None, 64)    36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, None, None, 64)    0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, None, None, 128)   73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, None, None, 128)   147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, None, None, 128)   0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, None, None, 256)   295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, None, None, 256)   590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, None, None, 256)   590080    \n",
            "                                                                 \n",
            " block3_conv4 (Conv2D)       (None, None, None, 256)   590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, None, None, 256)   0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, None, None, 512)   1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block4_conv4 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block5_conv4 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 512)              0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4096)              2101248   \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 40)                163880    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 39,070,824\n",
            "Trainable params: 19,046,440\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history_1 = model_1.fit(\n",
        "    train_generator,\n",
        "    epochs=10,  # specify the number of epochs for training\n",
        "    validation_data=val_generator\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3ycIC7GVRnt",
        "outputId": "d763e017-f301-4ef2-e9d7-35990745de3b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "5/5 [==============================] - 371s 86s/step - loss: 4.9513 - accuracy: 0.0071 - val_loss: 3.7194 - val_accuracy: 0.0250\n",
            "Epoch 2/10\n",
            "5/5 [==============================] - 306s 64s/step - loss: 3.7290 - accuracy: 0.0179 - val_loss: 3.6836 - val_accuracy: 0.0750\n",
            "Epoch 3/10\n",
            "5/5 [==============================] - 356s 76s/step - loss: 3.6889 - accuracy: 0.0357 - val_loss: 3.6656 - val_accuracy: 0.0250\n",
            "Epoch 4/10\n",
            "5/5 [==============================] - 356s 84s/step - loss: 3.6694 - accuracy: 0.0321 - val_loss: 3.6412 - val_accuracy: 0.0500\n",
            "Epoch 5/10\n",
            "5/5 [==============================] - 356s 77s/step - loss: 3.6270 - accuracy: 0.0786 - val_loss: 3.5849 - val_accuracy: 0.1167\n",
            "Epoch 6/10\n",
            "5/5 [==============================] - 356s 84s/step - loss: 3.5569 - accuracy: 0.0929 - val_loss: 3.4774 - val_accuracy: 0.1500\n",
            "Epoch 7/10\n",
            "5/5 [==============================] - 357s 77s/step - loss: 3.4466 - accuracy: 0.1143 - val_loss: 3.3267 - val_accuracy: 0.1417\n",
            "Epoch 8/10\n",
            "5/5 [==============================] - 356s 77s/step - loss: 3.3176 - accuracy: 0.1250 - val_loss: 3.1764 - val_accuracy: 0.1750\n",
            "Epoch 9/10\n",
            "5/5 [==============================] - 356s 77s/step - loss: 3.2614 - accuracy: 0.0821 - val_loss: 3.0845 - val_accuracy: 0.1667\n",
            "Epoch 10/10\n",
            "5/5 [==============================] - 357s 77s/step - loss: 3.1197 - accuracy: 0.1214 - val_loss: 3.0583 - val_accuracy: 0.1583\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training and validation accuracy\n",
        "plt.plot(history_1.history['accuracy'])\n",
        "plt.plot(history_1.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot the training and validation loss\n",
        "plt.plot(history_1.history['loss'])\n",
        "plt.plot(history_1.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "VTPunrdWjq-0",
        "outputId": "202b531c-1da9-4d27-fb8c-9cd8553ace29"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "filepath = '/content/models/VGG19'\n",
        "\n",
        "save_model(\n",
        "    model, filepath, overwrite=True, save_format=None\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84nUp5X5zHMp",
        "outputId": "809d1c42-5e0c-4885-b810-8b6c12fdff29"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r models.zip models/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hz6lvdAWK3K3",
        "outputId": "bd5f18cf-22a2-43d7-d974-68a437824f37"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: models/ (stored 0%)\n",
            "  adding: models/Alexnet/ (stored 0%)\n",
            "  adding: models/Alexnet/keras_metadata.pb (deflated 93%)\n",
            "  adding: models/Alexnet/variables/ (stored 0%)\n",
            "  adding: models/Alexnet/variables/variables.index (deflated 65%)\n",
            "  adding: models/Alexnet/variables/variables.data-00000-of-00001 (deflated 9%)\n",
            "  adding: models/Alexnet/saved_model.pb (deflated 89%)\n",
            "  adding: models/Alexnet/assets/ (stored 0%)\n",
            "  adding: models/Alexnet/fingerprint.pb (stored 0%)\n",
            "  adding: models/VGG19/ (stored 0%)\n",
            "  adding: models/VGG19/keras_metadata.pb (deflated 93%)\n",
            "  adding: models/VGG19/variables/ (stored 0%)\n",
            "  adding: models/VGG19/variables/variables.index (deflated 65%)\n",
            "  adding: models/VGG19/variables/variables.data-00000-of-00001 (deflated 9%)\n",
            "  adding: models/VGG19/saved_model.pb (deflated 89%)\n",
            "  adding: models/VGG19/assets/ (stored 0%)\n",
            "  adding: models/VGG19/fingerprint.pb (stored 0%)\n"
          ]
        }
      ]
    }
  ]
}