TF_accessing_y_pred_during_training.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TF_accessing_y_pred_during_training.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMW3Tr7yHqvNHLJYVbiMXBl",
      "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/TF_accessing_y_pred_during_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j89WFkhP7ZTP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "0a486003-d1c2-4991-b477-2b79f2fe8b91"
      },
      "source": [
        "!pip install tensorflow==2.2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "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",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BTZV7JU7mim",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "328ccb6c-801d-4634-be7c-529ecdcb1ae5"
      },
      "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",
        "from tensorflow.keras import backend \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(\"\\nY_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": 4,
      "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",
            "\n",
            "Y_pred from backend\n",
            " [[0.1181308  0.07973022 0.07437905 0.11143345 0.12857349 0.10558275\n",
            "  0.11315574 0.09159117 0.10190973 0.07551365]\n",
            " [0.11348061 0.08115198 0.07756394 0.10230396 0.12429447 0.10910256\n",
            "  0.10675646 0.09932543 0.11057273 0.07544786]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08837869763374329, 0.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvnX3ZHR71Tg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}