LeNet_Mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOmiEIx3kYAVPfsSU090l+A",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/arssite/Datalysis/blob/main/LeNet_Mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://medium.com/@siddheshb008/lenet-5-architecture-explained-3b559cb2d52b"
      ],
      "metadata": {
        "id": "nJQdOUWD0RtR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, AveragePooling2D, Flatten, Dense\n",
        "\n",
        "# Load MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "# Normalize pixel values to the range [0, 1]\n",
        "x_train = x_train.astype('float32') / 255.0\n",
        "x_test = x_test.astype('float32') / 255.0\n",
        "\n",
        "# Reshape data to fit the LeNet architecture (input shape: 28x28x1)\n",
        "x_train = x_train.reshape(-1, 28, 28, 1)\n",
        "x_test = x_test.reshape(-1, 28, 28, 1)\n",
        "\n",
        "# Define LeNet-5 architecture\n",
        "model = Sequential([\n",
        "    Conv2D(6, kernel_size=(5, 5), strides=(1, 1), activation='relu', input_shape=(28, 28, 1)),\n",
        "    AveragePooling2D(pool_size=(2, 2), strides=(2, 2)),\n",
        "    Conv2D(16, kernel_size=(5, 5), strides=(1, 1), activation='relu'),\n",
        "    AveragePooling2D(pool_size=(2, 2), strides=(2, 2)),\n",
        "    Flatten(),\n",
        "    Dense(120, activation='relu'),\n",
        "    Dense(84, activation='relu'),\n",
        "    Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))\n",
        "\n",
        "# Evaluate the model\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
        "print(f'Test accuracy: {test_acc}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uijJ2eCyp9r",
        "outputId": "e56307c2-15a9-4584-a4d6-f0ea501f5a20"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 7s 5ms/step - loss: 0.4113 - accuracy: 0.8777 - val_loss: 0.1471 - val_accuracy: 0.9555\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1211 - accuracy: 0.9629 - val_loss: 0.0873 - val_accuracy: 0.9729\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - accuracy: 0.9744 - val_loss: 0.0642 - val_accuracy: 0.9797\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0668 - accuracy: 0.9794 - val_loss: 0.0519 - val_accuracy: 0.9831\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 3s 6ms/step - loss: 0.0562 - accuracy: 0.9828 - val_loss: 0.0474 - val_accuracy: 0.9854\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0497 - accuracy: 0.9849 - val_loss: 0.0445 - val_accuracy: 0.9861\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0420 - accuracy: 0.9865 - val_loss: 0.0404 - val_accuracy: 0.9875\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0374 - accuracy: 0.9886 - val_loss: 0.0369 - val_accuracy: 0.9887\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0344 - accuracy: 0.9892 - val_loss: 0.0513 - val_accuracy: 0.9840\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0305 - accuracy: 0.9899 - val_loss: 0.0406 - val_accuracy: 0.9874\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0406 - accuracy: 0.9874\n",
            "Test accuracy: 0.9873999953269958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Select a random sample from the test set\n",
        "index = np.random.randint(0, len(x_test))\n",
        "sample = x_test[index].reshape(1, 28, 28, 1)\n",
        "\n",
        "# Perform prediction\n",
        "prediction = model.predict(sample)\n",
        "predicted_label = np.argmax(prediction)\n",
        "\n",
        "print(\"Predicted Label:\", predicted_label)\n",
        "print(\"Actual Label:\", y_test[index])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RABHx7_vz2I2",
        "outputId": "645e2fdc-1518-4133-c9f3-c540717e7a56"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 327ms/step\n",
            "Predicted Label: 8\n",
            "Actual Label: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Specify the index of the image\n",
        "index = 100\n",
        "\n",
        "# Select the image from the test set\n",
        "image = x_test[index]\n",
        "image = image.reshape(1, 28, 28, 1)\n",
        "\n",
        "# Perform prediction\n",
        "prediction = model.predict(image)\n",
        "predicted_label = np.argmax(prediction)\n",
        "\n",
        "# Plot the image\n",
        "plt.imshow(x_test[index].reshape(28, 28), cmap='gray')\n",
        "plt.title(f\"Predicted Label: {predicted_label}\")\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "N8TacFJ8z2sP",
        "outputId": "1ada1bee-38a2-4fc9-d97e-71622bc0b5ef"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 79ms/step\n"
          ]
        },
        {
          "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": [
        "from keras.utils import plot_model\n",
        "plot_model(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        },
        "id": "iaV3RAi30J6D",
        "outputId": "8633b21e-8ec2-4042-9af8-43793ee9e5fc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ip587MoR0lHL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}