Save_load_model_with_customeObjects.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Save_load_model_with_customeObjects.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMh16X/SSbJp9CnADux/tI4",
      "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.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40ejnLL6WHVp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "94979afc-fcc4-4d20-db80-80430250a473"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgf6ENXFWI9J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "728b689d-b497-4389-8a37-c0a1942ead98"
      },
      "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 Loss1 (for example) \n",
        "@tf.function() \n",
        "def customLoss1(yTrue,yPred):\n",
        "  return tf.reduce_mean(yTrue-yPred) \n",
        "\n",
        "# Custom Loss2 (for example) \n",
        "@tf.function() \n",
        "def customLoss2(yTrue, yPred):\n",
        "  return tf.reduce_mean(tf.square(tf.subtract(yTrue,yPred))) \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",
        "  model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy', customLoss1, customLoss2])\n",
        "  return model"
      ],
      "execution_count": 4,
      "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": "iLdZ00qwWVQe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "95cdd52a-4c51-4905-afdc-c98c83d8ff59"
      },
      "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)\n",
        "loss, acc,loss1, loss2 = model.evaluate(x_test, y_test,verbose=1)\n",
        "print(\"Original model, accuracy: {:5.2f}%\".format(100*acc))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2179 - accuracy: 0.9356 - customLoss1: 4.3539 - customLoss2: 27.3820\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0945 - accuracy: 0.9712 - customLoss1: 4.3539 - customLoss2: 27.3892\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0696 - accuracy: 0.9783 - customLoss1: 4.3539 - customLoss2: 27.3907\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0537 - accuracy: 0.9826 - customLoss1: 4.3539 - customLoss2: 27.3915\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0437 - accuracy: 0.9857 - customLoss1: 4.3539 - customLoss2: 27.3921\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0690 - accuracy: 0.9783 - customLoss1: 4.3429 - customLoss2: 27.3308\n",
            "Original model, accuracy: 97.83%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yoZO9DHLWZjI",
        "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": "8xJEQo8hWmrs",
        "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={customLoss1:customLoss1, customLoss2:customLoss2},compile=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08ZlMHMfWwT2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# compile the loaded model with the custome objects\n",
        "loaded_model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy', customLoss1, customLoss2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cvLk1YZXFU5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}