Save_load_model_with_customeObjects_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Save_load_model_with_customeObjects_2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPoDg+Eb0nhNNMWV/tExehc",
      "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/Save_load_model_with_customeObjects_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhxFrjtvgkOJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "outputId": "28687bef-8ed1-4b7a-d0c5-5238eec732ca"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cf/29/eaa43f27d2045ed4813c72e6f52d73b58ed603c5f1817c599993373bdf67/tf_nightly-2.2.0.dev20200405-cp36-cp36m-manylinux2010_x86_64.whl (517.1MB)\n",
            "\u001b[K     |████████████████████████████████| 517.1MB 28kB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.34.2)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.10.0)\n",
            "Collecting tf-estimator-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/94/373b909bdc1cd30015118d0c9da1b2be368c391691764181deb653b3d0ce/tf_estimator_nightly-2.3.0.dev2020040501-py2.py3-none-any.whl (455kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 49.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.6.3)\n",
            "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (2.10.0)\n",
            "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.4.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.27.2)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.9.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.2.0)\n",
            "Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/59/0b/446c007433c44a2aab87b34fc272afe8574a1c47cee35dbc19f6394dada8/tb_nightly-2.3.0a20200405-py3-none-any.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 42.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.2.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.18.2)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.3.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.9.2->tf-nightly) (46.0.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.7.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.2.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.6.0.post2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (0.4.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2.21.0)\n",
            "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (4.0)\n",
            "Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (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->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2019.11.28)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2.8)\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->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (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->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.0)\n",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, tf-nightly\n",
            "Successfully installed tb-nightly-2.3.0a20200405 tf-estimator-nightly-2.3.0.dev2020040501 tf-nightly-2.2.0.dev20200405\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mG6cJXGqgoJS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "da4ee242-008c-4e6b-8364-6f093a6849c7"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "# Custom Loss (for example) \n",
        "@tf.function() \n",
        "def triplet_loss(y_true, y_pred, alpha = 0.3):\n",
        "  anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]\n",
        "  pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)\n",
        "  neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)\n",
        "  basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)\n",
        "  loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))\n",
        "  return loss\n",
        "  \n",
        "def create_model():\n",
        "  model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(512, activation=tf.nn.relu),  \n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "    ])\n",
        "\n",
        "  model.compile(optimizer='adam', loss=triplet_loss)\n",
        "  return model"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tQEqA8Dgzmx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "188b90c8-a58b-41f8-88c4-b31dc26ff947"
      },
      "source": [
        "# Create a basic model instance\n",
        "model=create_model()\n",
        "\n",
        "# Fit and evaluate model \n",
        "model.fit(x_train, y_train, epochs=5)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3004\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3002\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3000\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3000\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3143539668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yQEQWbMgz9b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save the model in *.h5 format\n",
        "model.save('./MyModel.h5',save_format='h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4S5qgvg0g2ZK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load the saved model (Don't compile while loading as custom objects are not serialized)\n",
        "loaded_model = tf.keras.models.load_model('./MyModel.h5', custom_objects={triplet_loss:triplet_loss},compile=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBt9JzVfg4xG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# compile the loaded model with the custome objects\n",
        "loaded_model.compile(optimizer='adam',loss=triplet_loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2pe1DbLg7Kt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}