LENET_Architecture_ (1).ipynb

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    "kernelspec": {
      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "EId83keIjD2h"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "# LENET Architecture disovered by Yaan Lecun"
      ],
      "metadata": {
        "id": "Dj2FVQX5jGYZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten"
      ],
      "metadata": {
        "id": "VvtB-jy9jYdk"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(Conv2D(6, kernel_size=(5,5), padding='valid', activation='tanh',input_shape=(32,32,1)))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='valid'))\n",
        "model.add(Conv2D(16, kernel_size=(5,5), padding='valid', activation='tanh'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='valid'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(120, activation='tanh'))\n",
        "model.add(Dense(84, activation='tanh'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "PhFhi22gY6yH"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuoFfXJWW7Bv",
        "outputId": "e2999d53-82a6-45ba-844a-69811a4caf21"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_10 (Conv2D)          (None, 28, 28, 6)         156       \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 14, 14, 6)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 10, 10, 16)        2416      \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 5, 5, 16)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_5 (Flatten)         (None, 400)               0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 120)               48120     \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 84)                10164     \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 10)                850       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 61,706\n",
            "Trainable params: 61,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fy92rAIhmVwv"
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      "execution_count": null,
      "outputs": []
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