Basic_custom_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Basic_custom_model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPsngDOt8UuiyNXjK0j4Cae",
      "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/Basic_custom_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkQ_YV_LB6hs"
      },
      "source": [
        "###Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1JHUw7-BjjM"
      },
      "source": [
        "try: \n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers import concatenate\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.utils import plot_model"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMxRuYjZC4WV"
      },
      "source": [
        "###**Build the Model**\n",
        "\n",
        "Let's implement the wide and deep model as shown in class. As shown below, the Functional API is very flexible in implementing complex models.\n",
        "\n",
        " \n",
        "\n",
        "1.  You will specify the previous layer when you define a new layer.\n",
        "2.   When you define the Model, you will specify the inputs and output.\n",
        "\n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "nFSHemCvCK_E",
        "outputId": "6db8bd52-5dd2-43c2-e75a-f5ceeae76695"
      },
      "source": [
        "#define inputs\n",
        "input_a = Input(shape=[1] , name=\"wide_Input\")\n",
        "input_b = Input(shape=[1], name=\"Deep_Input\")\n",
        "\n",
        "#define deep path \n",
        "hidden_1 = Dense(30, activation=\"relu\")(input_b)\n",
        "hidden_2 = Dense(30 , activation=\"relu\")(hidden_1)\n",
        "\n",
        "#define merged path\n",
        "concat = concatenate([input_a , hidden_2])\n",
        "output = Dense(1 , name=\"output\")(concat)\n",
        "# define another output fro the deep path \n",
        "aux_output = Dense(1 , name=\"aux_output\")(hidden_2)\n",
        "\n",
        "# build the model \n",
        "model = Model(inputs=[input_a , input_b], outputs = [output , aux_output])\n",
        "\n",
        "#visualize the architecture \n",
        "plot_model(model)\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ti4h7FDJFzdY"
      },
      "source": [
        "###**Implement as a Class**\n",
        "\n",
        "Alternatively, you can also implement this same model as a class.\n",
        "\n",
        "\n",
        "> For that, you define a class that inherits from the Model class.\n",
        "Inheriting from the existing Model class lets you use the Model methods such as compile(), fit(), evaluate().\n",
        "\n",
        "\n",
        "When inheriting from Model, you will want to define at least two functions:\n",
        "\n",
        "\n",
        ">__init__(): you will initialize the instance attributes.\n",
        "call(): you will build the network and return the output layers.\n",
        "\n",
        "If you compare the two methods, the structure is very similar, except when using the class, you'll define all the layers in one function, init, and connect the layers together in another function, call."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETwHqRyZFrdG"
      },
      "source": [
        "#inherint from the Model base class\n",
        "class WideAndeDeepModel(Model):\n",
        "\n",
        "  def __init__(self,units=30 , activation='relu' , **kwargs):\n",
        "    '''initialize the instance attributes'''\n",
        "    super().__init__(**kwargs)\n",
        "    self.hidden_1 = Dense(units, activation=activation)\n",
        "    self.hidden_2 = Dense(units , activation = activation)\n",
        "    self.main_output = Dense(1)\n",
        "    self.aux_output = Dense(1)\n",
        "  \n",
        "  def call(self, inputs):\n",
        "    ''' defines the network architecture'''\n",
        "    input_A , input_B = inputs\n",
        "    hidden_1 = self.hidden_1(input_B)\n",
        "    hidden2 = self.hidden2(hidden1)\n",
        "    concat = concatenate([input_A, hidden2])\n",
        "    main_output = self.main_output(concat)\n",
        "    aux_output = self.aux_output(hidden2)\n",
        "        \n",
        "    return main_output, aux_output"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7U3I_RwIS8o"
      },
      "source": [
        "# create an instance of the model\n",
        "model = WideAndeDeepModel()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3yL_SAiIpMt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}