custom_loss_add_loss.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "custom_loss_add_loss.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNrtqoFXThwvqdsWSQia2D3",
      "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/custom_loss_add_loss.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnKiArMnP4Ve",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "outputId": "30c992da-f91c-43a9-895a-064b935e85aa"
      },
      "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/be/76/42a2abf70a6d7d50c63c515ee68a14cded571908ac6b6a5a78f68ead7148/tf_nightly-2.2.0.dev20200408-cp36-cp36m-manylinux2010_x86_64.whl (517.5MB)\n",
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            "Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2e/70/874df1f50c16f1203ac4044c8fdf061932a955cb825b27d74f1a9b4c66df/tb_nightly-2.3.0a20200408-py3-none-any.whl (3.0MB)\n",
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            "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",
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            "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",
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            "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: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->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-2.3.0a20200408 tf-estimator-nightly-2.3.0.dev2020040801 tf-nightly-2.2.0.dev20200408\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMnpf66NP624",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "a2db2e02-0687-44cf-ed10-44ff0e4a2721"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.losses import Loss\n",
        "import numpy as np\n",
        " \n",
        "inp = Input(shape=(1,), dtype=tf.float32)\n",
        "targets = Input(shape=(1,), dtype=tf.float32)\n",
        "w = Input(shape=(1,), dtype=tf.float32)\n",
        "out = Dense(1)(inp)\n",
        "m = Model(inputs=[inp, w, targets], outputs=out)\n",
        " \n",
        " \n",
        " \n",
        "def weighted_loss(y_true, y_pred, weights):\n",
        "      loss = tf.math.squared_difference(y_pred, y_true)\n",
        "      w_loss = tf.multiply(weights, loss)\n",
        "      return tf.reduce_mean(tf.reduce_sum(w_loss, axis=-1))\n",
        "  \n",
        "m.add_loss(weighted_loss(targets, out, w))\n",
        " \n",
        "m.compile(loss=None, optimizer='adam')\n",
        "x = np.ones((512,1))\n",
        "m.fit([x, x, x], epochs=10)\n",
        "m.save('test.h5')      \n",
        "m2 = tf.keras.models.load_model('test.h5')\n",
        "m2.fit([x, x, x], epochs=10)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 3.4332\n",
            "Epoch 2/10\n",
            "16/16 [==============================] - 0s 947us/step - loss: 3.3159\n",
            "Epoch 3/10\n",
            "16/16 [==============================] - 0s 942us/step - loss: 3.2014\n",
            "Epoch 4/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 3.0897\n",
            "Epoch 5/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.9810\n",
            "Epoch 6/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.8751\n",
            "Epoch 7/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.7721\n",
            "Epoch 8/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.6719\n",
            "Epoch 9/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.5744\n",
            "Epoch 10/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.4797\n",
            "Epoch 1/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.3876\n",
            "Epoch 2/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.2981\n",
            "Epoch 3/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.2112\n",
            "Epoch 4/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.1268\n",
            "Epoch 5/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 2.0449\n",
            "Epoch 6/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 1.9654\n",
            "Epoch 7/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 1.8883\n",
            "Epoch 8/10\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 1.8136\n",
            "Epoch 9/10\n",
            "16/16 [==============================] - 0s 981us/step - loss: 1.7411\n",
            "Epoch 10/10\n",
            "16/16 [==============================] - 0s 990us/step - loss: 1.6708\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f529605f748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    }
  ]
}