Custom_loss_function_with_additionala_arguments2.ipynb

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  "metadata": {
    "colab": {
      "name": "Custom_loss_function_with_additionala_arguments.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM3lIqQIvTGW/X16vktDPAT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "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/Custom_loss_function_with_additionala_arguments2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4D4h1amfw4t",
        "colab_type": "code",
        "outputId": "80d7d536-031f-4f5a-f7f9-b559c0570027",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e6/4c/6c7fb51e50b44cce25df56926046be37b5303e7d0270b376ddb880cd10a7/tf_estimator_nightly-2.3.0.dev2020051201-py2.py3-none-any.whl (456kB)\n",
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            "Installing collected packages: tb-nightly, tf-estimator-nightly, tf-nightly\n",
            "Successfully installed tb-nightly-2.3.0a20200512 tf-estimator-nightly-2.3.0.dev2020051201 tf-nightly-2.2.0.dev20200508\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiGhCLQVf1tU",
        "colab_type": "code",
        "outputId": "ee726c55-88bd-47ad-bece-48374eeab689",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "from tensorflow import keras\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from functools import partial\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train = x_train[...,None] / 255 - 1\n",
        "x_test = x_test[...,None] / 255 - 1\n",
        "y_train = keras.utils.to_categorical(y_train)\n",
        "y_test = keras.utils.to_categorical(y_test)\n",
        "model = keras.models.Sequential(\n",
        "  [\n",
        "    keras.layers.Conv2D(6, (3,3), activation='relu'),\n",
        "    keras.layers.AveragePooling2D(),\n",
        "    keras.layers.Conv2D(16, (3,3), activation='relu'),\n",
        "    keras.layers.AveragePooling2D(),\n",
        "    keras.layers.Flatten(),\n",
        "    keras.layers.Dense(units=120, activation='relu'),\n",
        "    keras.layers.Dense(units=84, activation='relu'),\n",
        "    keras.layers.Dense(units=10, activation='softmax')\n",
        "  ]\n",
        ")\n",
        "\n",
        "def dummy_loss(a):\n",
        "  def loss(y_true,y_pred):\n",
        "    # print(tf.executing_eagerly()) This will be True when you selet \n",
        "    print(\"Y_true from backend\\n\", backend.get_value(y_true))\n",
        "    print(\"Y_pred from backend\\n\", backend.get_value(y_pred))\n",
        "    error = backend.square((y_pred - y_true))\n",
        "    mse_error = backend.mean(error, axis=-1)  \n",
        "    return mse_error\n",
        "  return loss\n",
        "\n",
        "model.compile(\n",
        "  loss=dummy_loss(a=2),\n",
        "  optimizer=keras.optimizers.Adam(), metrics={'output_1':'accuracy'},run_eagerly=True)\n",
        "model.train_on_batch(x_train[:2,...], y_train[:2,...]) # error here"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y_true from backend\n",
            " [[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "Y_pred from backend\n",
            " [[0.09316731 0.1004759  0.06735663 0.11302211 0.11352726 0.10840722\n",
            "  0.11945039 0.11090993 0.07175183 0.10193139]\n",
            " [0.0898835  0.10629914 0.06839891 0.10397421 0.11139363 0.11401121\n",
            "  0.12153649 0.10721816 0.07678661 0.10049805]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.09043955057859421, 0.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRRJtGfu1i4g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss=custom_loss(weight_building, weight_space),optimizer=keras.optimizers.Adam(), metrics=['accuracy'],run_eagerly=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txpikE4U1MCf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def custom_loss(weight_building, weight_space):\n",
        "  def loss(y_true, y_pred):\n",
        "    truth = backend.get_value(y_true)\n",
        "    error = backend.square((y_pred - y_true))\n",
        "    mse_error = backend.mean(error, axis=-1) \n",
        "    return mse_error\n",
        "  return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dG8A4jIHf3VW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model.fit(x_train,y_train,epochs=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTSFo0cNzm1p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}