Simple_Functional_model.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Simple_Functional_model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNAqZPNUfD+gFlO0y7vrQCg",
      "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/armeh429/A-journey-for-learning-advanced-tensorflow/blob/main/Simple_Functional_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "at this code i will show you how to define the functional API model. \n",
        "this simple code will show the syntax to you "
      ],
      "metadata": {
        "id": "vY2DHdvlu-hc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzZyJOh_nS8b",
        "outputId": "c32df1a2-3c5b-4939-afb1-db5cda3c70b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7554 sha256=5ef1970b389d0c3f52daf0a56295e533a87904d149791bb3f79b066b901f46f5\n",
            "  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3\n",
            "Successfully built gast\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, keras-applications, gast, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.7.0\n",
            "    Uninstalling tensorflow-estimator-2.7.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.7.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.7.0\n",
            "    Uninstalling tensorboard-2.7.0:\n",
            "      Successfully uninstalled tensorboard-2.7.0\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.4.0\n",
            "    Uninstalling gast-0.4.0:\n",
            "      Successfully uninstalled gast-0.4.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.7.0\n",
            "    Uninstalling tensorflow-2.7.0:\n",
            "      Successfully uninstalled tensorflow-2.7.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-probability 0.15.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow==2.0.0"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "oaOauS3Tsx4s"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Build_model_with_functional():\n",
        "  from tf.keras.models import Model\n",
        "  input_layer = tf.keras.Input((28,28))\n",
        "  fallaten_layer = tf.keras.layers.Flatten()(input_layer)\n",
        "  Dense_layer = tf.keras.layers.Dense(128,activation = tf.nn.relu)(fallaten_layer)\n",
        "  output_layer = tf.keras.layers.Dense(10, activation = tf.nn.softmax)(Dense_layer)\n",
        "  model = Model(inputs =input_layer , outputs = output_layer )\n",
        "  return Model"
      ],
      "metadata": {
        "id": "5-yG073WsQjP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NUtuFLzPtMDb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}