alexnet.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSihtlpw95Fc",
        "outputId": "2b578cc0-3fa6-4f5a-eab5-5fb6e7e93dc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQvpRZRX-Lfp",
        "outputId": "8002bb10-b089-410a-b01e-e89f77aff510"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ],
      "source": [
        "from zipfile import ZipFile\n",
        "file_name = \"/content/drive/MyDrive/archive.zip\"\n",
        "\n",
        "with ZipFile(file_name,'r') as zip:\n",
        "  zip.extractall()\n",
        "  print('Done')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "rd5v18oQ-N-i"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "train_dir = \"/content/train\" # Directory containing the training data\n",
        "test_dir = \"/content/test\"  # Directory containing the validation data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1N963CqAIQYJ",
        "outputId": "2836082c-e316-47fc-8cca-40a1498e610b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting livelossplot\n",
            "  Downloading livelossplot-0.5.5-py3-none-any.whl (22 kB)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from livelossplot) (3.7.1)\n",
            "Requirement already satisfied: bokeh in /usr/local/lib/python3.10/dist-packages (from livelossplot) (2.4.3)\n",
            "Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from bokeh->livelossplot) (3.1.2)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.10/dist-packages (from bokeh->livelossplot) (1.22.4)\n",
            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.10/dist-packages (from bokeh->livelossplot) (23.1)\n",
            "Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.10/dist-packages (from bokeh->livelossplot) (8.4.0)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.10/dist-packages (from bokeh->livelossplot) (6.0)\n",
            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.10/dist-packages (from bokeh->livelossplot) (6.3.1)\n",
            "Requirement already satisfied: typing-extensions>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from bokeh->livelossplot) (4.6.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->livelossplot) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->livelossplot) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->livelossplot) (4.40.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->livelossplot) (1.4.4)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->livelossplot) (3.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->livelossplot) (2.8.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=2.9->bokeh->livelossplot) (2.1.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->livelossplot) (1.16.0)\n",
            "Installing collected packages: livelossplot\n",
            "Successfully installed livelossplot-0.5.5\n"
          ]
        }
      ],
      "source": [
        "! pip install livelossplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U1T9WmbDIU5v",
        "outputId": "1cbdfd0c-846a-4784-907b-8bd84d25d03a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensorflow version: 2.12.0\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Dense, Input, Dropout,Flatten, Conv2D\n",
        "from tensorflow.keras.layers import BatchNormalization, Activation, MaxPooling2D\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
        "\n",
        "from IPython.display import SVG, Image\n",
        "from livelossplot.tf_keras import PlotLossesCallback\n",
        "import tensorflow as tf\n",
        "print(\"Tensorflow version:\", tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "utuOCvmQWrFR"
      },
      "outputs": [],
      "source": [
        "%load_ext tensorboard\n",
        "from tqdm import tqdm\n",
        "\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPY7DQETNySV",
        "outputId": "86b6c988-b461-45eb-ac59-3af9f52d9ba0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 22968 images belonging to 7 classes.\n",
            "Found 1432 images belonging to 7 classes.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "    width_shift_range=0.1,        # Randomly shift the width of images by up to 10%\n",
        "    height_shift_range=0.1,       # Randomly shift the height of images by up to 10%\n",
        "    horizontal_flip=True,         # Flip images horizontally at random\n",
        "    validation_split=0.2,         # Set aside 20% of the data for validation\n",
        "    rescale=1./255                # Rescale pixel values to be between 0 and 1\n",
        ")\n",
        "\n",
        "validation_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,               # Rescale pixel values to be between 0 and 1\n",
        "    validation_split=0.2          # Set aside 20% of the data for validation\n",
        ")\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    directory = train_dir,           # Directory containing the training data\n",
        "    target_size = (224, 224),          # Resizes all images to 48x48 pixels\n",
        "    batch_size = 256,                 # Number of images per batch      # Converts the images to grayscale\n",
        "    class_mode = \"categorical\",      # Classifies the images into 7 categories\n",
        "    subset = \"training\"              # Uses the training subset of the data\n",
        ")\n",
        "\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "    directory = test_dir,            # Directory containing the validation data\n",
        "    target_size = (224, 224),          # Resizes all images to 48x48 pixels\n",
        "    batch_size = 256,                 # Number of images per batch      # Converts the images to grayscale\n",
        "    class_mode = \"categorical\",      # Classifies the images into 7 categories\n",
        "    subset = \"validation\"            # Uses the validation subset of the data\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqotMyksQVcj",
        "outputId": "9580fa20-9fb2-4f0d-829e-9e4dc32c2e13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 54, 54, 96)        34944     \n",
            "                                                                 \n",
            " activation (Activation)     (None, 54, 54, 96)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 27, 27, 96)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 17, 17, 256)       2973952   \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 17, 17, 256)       0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 8, 8, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 6, 6, 384)         885120    \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 6, 6, 384)         0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 4, 4, 384)         1327488   \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 4, 4, 384)         0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 2, 2, 256)         884992    \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 2, 2, 256)         0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 1, 1, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              1052672   \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 4096)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 4096)              0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1000)              4097000   \n",
            "                                                                 \n",
            " activation_7 (Activation)   (None, 1000)              0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 1000)              0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 7)                 7007      \n",
            "                                                                 \n",
            " activation_8 (Activation)   (None, 7)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 28,044,487\n",
            "Trainable params: 28,044,487\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "\n",
        "from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization\n",
        "\n",
        "import numpy as np\n",
        "np.random.seed(1000)\n",
        "\n",
        "#Instantiate an empty model\n",
        "model = Sequential()\n",
        "\n",
        "# 1st Convolutional Layer\n",
        "model.add(Conv2D(filters=96, input_shape=(224,224,3), kernel_size=(11,11), strides=(4,4), padding='valid'))\n",
        "model.add(Activation('relu'))\n",
        "# Max Pooling\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))\n",
        "\n",
        "# 2nd Convolutional Layer\n",
        "model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid'))\n",
        "model.add(Activation('relu'))\n",
        "# Max Pooling\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))\n",
        "\n",
        "# 3rd Convolutional Layer\n",
        "model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "# 4th Convolutional Layer\n",
        "model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "# 5th Convolutional Layer\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))\n",
        "model.add(Activation('relu'))\n",
        "# Max Pooling\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))\n",
        "\n",
        "# Passing it to a Fully Connected layer\n",
        "model.add(Flatten())\n",
        "# 1st Fully Connected Layer\n",
        "model.add(Dense(4096, input_shape=(224*224*3,)))\n",
        "model.add(Activation('relu'))\n",
        "# Add Dropout to prevent overfitting\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "# 2nd Fully Connected Layer\n",
        "model.add(Dense(4096))\n",
        "model.add(Activation('relu'))\n",
        "# Add Dropout\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "# 3rd Fully Connected Layer\n",
        "model.add(Dense(1000))\n",
        "model.add(Activation('relu'))\n",
        "# Add Dropout\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "# Output Layer\n",
        "model.add(Dense(7))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=keras.losses.categorical_crossentropy, optimizer='adam', metrics=[\"accuracy\"])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uorfjtbMYe-C",
        "outputId": "90dc824e-20cf-492e-e759-1127f4fdd0f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "89/89 [==============================] - 292s 3s/step - loss: 1.8876 - accuracy: 0.2377 - val_loss: 1.8163 - val_accuracy: 0.2484\n",
            "Epoch 2/5\n",
            "89/89 [==============================] - 261s 3s/step - loss: 1.8150 - accuracy: 0.2513 - val_loss: 1.8116 - val_accuracy: 0.2508\n",
            "Epoch 3/5\n",
            "89/89 [==============================] - 260s 3s/step - loss: 1.8130 - accuracy: 0.2514 - val_loss: 1.8150 - val_accuracy: 0.2461\n",
            "Epoch 4/5\n",
            "89/89 [==============================] - 262s 3s/step - loss: 1.8123 - accuracy: 0.2513 - val_loss: 1.8160 - val_accuracy: 0.2461\n",
            "Epoch 5/5\n",
            "89/89 [==============================] - 265s 3s/step - loss: 1.8133 - accuracy: 0.2513 - val_loss: 1.8167 - val_accuracy: 0.2453\n"
          ]
        }
      ],
      "source": [
        "# Training parameters\n",
        "EPOCHS = 5\n",
        "BATCH_SIZE = 256\n",
        "\n",
        "# Number of samples in your training and validation sets\n",
        "train_num = train_generator.samples\n",
        "valid_num = validation_generator.samples\n",
        "\n",
        "# Fit the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    epochs=5,\n",
        "    steps_per_epoch=train_num // BATCH_SIZE,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=valid_num // BATCH_SIZE,\n",
        "    verbose=1\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "aLrRf0QQZmHH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "1fc6b0ce-de12-465b-8362-4cf502aae020"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the train and validation loss\n",
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(train_loss) + 1)\n",
        "plt.plot(epochs, train_loss, label='Training loss')\n",
        "plt.plot(epochs, val_loss, label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "1vyQddPxZnFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "a36430d2-7cb4-44e0-eb8f-3186ee9c8cf5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the train and validation accuracy\n",
        "train_acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "plt.plot(epochs, train_acc, label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "m49t44NRZqi7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "945ff66b-cd9f-4e93-9000-8b66c7d75e3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 4s 711ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Get the true labels and predicted labels for the validation set\n",
        "validation_labels = validation_generator.classes\n",
        "validation_pred_probs = model.predict(validation_generator)\n",
        "validation_pred_labels = np.argmax(validation_pred_probs, axis=1)\n",
        "\n",
        "# Compute the confusion matrix\n",
        "confusion_mtx = confusion_matrix(validation_labels, validation_pred_labels)\n",
        "class_names = list(train_generator.class_indices.keys())\n",
        "sns.set()\n",
        "sns.heatmap(confusion_mtx, annot=True, fmt='d', cmap='YlGnBu',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vp-l3qBodVYt"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}