TF_Forum_24144.ipynb

tf_forum_24144.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPgjhBDdcpaSe9EuPw5lpVn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/kiransair/ca718d53b64f97f81eb504676568a222/tf_forum_24144.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "uPymGniNGgEC"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "inputs = tf.keras.Input(shape=(28, 28, 1))\n",
        "\n",
        "conv1 = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu')(inputs)\n",
        "\n",
        "pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)\n",
        "\n",
        "conv2 = tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu')(pool1)\n",
        "\n",
        "x = tf.keras.layers.GlobalAveragePooling2D()(conv2)\n",
        "\n",
        "x = tf.keras.layers.Dropout(0.5)(x)\n",
        "\n",
        "outputs = tf.keras.layers.Dense(10, activation='softmax')(x)\n",
        "\n",
        "model = tf.keras.Model(inputs=inputs, outputs=outputs)"
      ],
      "metadata": {
        "id": "ckXeQQp3HAh7"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)"
      ],
      "metadata": {
        "id": "6tMHuPatHDKz"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tflite_model = converter.convert()"
      ],
      "metadata": {
        "id": "Xdb_zaZNHIHY"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "ZdNqEgdMHNd1"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1=keras.Sequential([\n",
        "                        keras.Input(shape=(28,28,1)),\n",
        "                        keras.layers.Conv2D(32,kernel_size=(3,3),activation='relu',),\n",
        "                        keras.layers.MaxPooling2D(pool_size=(2,2)),\n",
        "                        keras.layers.Conv2D(64,kernel_size=(3,3),activation='relu'),\n",
        "                        keras.layers.GlobalAveragePooling2D(),\n",
        "                        keras.layers.Dropout(0.5),\n",
        "                        keras.layers.Dense(10,activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "zHQyXDilHKRi"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model1)"
      ],
      "metadata": {
        "id": "Ot5b9RagHRFs"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tflite_model = converter.convert()"
      ],
      "metadata": {
        "id": "Sp4xWbAeHUMz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UNcfKaoAHWRx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}