simple_keras_subclassed_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "simple_keras_subclassed_model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMejZ+jN41n1FNVwUciUTtx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jvishnuvardhan/Stackoverflow_Questions/blob/master/simple_keras_subclassed_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uCDIDc_JfmW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "4d3f2cc5-0048-48ac-a0ab-d93328da78cc"
      },
      "source": [
        "!pip install tensorflow==2.2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow==2.2 in /usr/local/lib/python3.6/dist-packages (2.2.0)\n",
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            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.2) (0.34.2)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorflow==2.2) (46.1.3)\n",
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            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (2.23.0)\n",
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            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (0.4.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.2.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (2020.4.5.1)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (2.9)\n",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (1.3.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2) (3.1.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1Gt8-wJKRs5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "272d5af6-49a3-4172-971f-5a1d82300227"
      },
      "source": [
        "import tensorflow as tf\n",
        "class Net(tf.keras.Model):\n",
        "  \"\"\"A simple linear model.\"\"\"\n",
        "\n",
        "  def __init__(self):\n",
        "    super(Net, self).__init__()\n",
        "    #self.l1 = tf.keras.layers.Dense(5)\n",
        "  def build(self,input_shape):\n",
        "    self.l1 = tf.keras.layers.Dense(5)\n",
        "    self.dummy = tf.Variable(trainable=True,initial_value=tf.keras.initializers.glorot_normal()(shape=(1,),dtype=tf.float32))\n",
        "    print('built layers')\n",
        "  def call(self, x):\n",
        "    return self.l1(x)\n",
        "\n",
        "net = Net()\n",
        "#net.build(input_shape=[1,])\n",
        "x_train = tf.random.normal(shape=(100,1),dtype=tf.float32)\n",
        "output=net.predict(x_train)\n",
        "net.save_weights('easy_checkpoint')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "built layers\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENaWbZ6pKcXe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "0a77250d-4f1d-449f-c8ef-e188b5d5f08a"
      },
      "source": [
        "net.summary()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"net\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                multiple                  10        \n",
            "=================================================================\n",
            "Total params: 11\n",
            "Trainable params: 11\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyXSYomVKdB5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "4905d76b-1e00-48f8-de63-49c06e972ac1"
      },
      "source": [
        "net.weights"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Variable 'net/dense/kernel:0' shape=(1, 5) dtype=float32, numpy=\n",
              " array([[0.5171616 , 0.98179245, 0.07099843, 0.3704896 , 0.29262972]],\n",
              "       dtype=float32)>,\n",
              " <tf.Variable 'net/dense/bias:0' shape=(5,) dtype=float32, numpy=array([0., 0., 0., 0., 0.], dtype=float32)>,\n",
              " <tf.Variable 'net/Variable:0' shape=(1,) dtype=float32, numpy=array([1.760339], dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUKEhsLaKj9x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "914cbb72-766f-4ff1-de91-e9eecbf9d427"
      },
      "source": [
        "print(output.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}