custom_loss_keras.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "custom_loss_keras.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMf91u7k8nKtqM3Gvdn6RSx",
      "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/Advanced-Deep-Learning-with-Keras/blob/master/custom_loss_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfrWzzxDDVk4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "6b4f5505-3242-46b5-a292-b5ef25eb847d"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM1eFR_MDYl1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1ad94f64-b4db-431d-d17a-ea3d6f6c7545"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras.layers as layers\n",
        "\n",
        "mnist = tf.keras.datasets.mnist\n",
        "(t_x, t_y), (v_x, v_y) = mnist.load_data()\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(128, activation=\"relu\"))\n",
        "model.add(layers.Dropout(0.2))\n",
        "model.add(layers.Dense(10))\n",
        "\n",
        "def myfunc(y_true, y_pred):\n",
        "    return lossFunc(y_true, y_pred)\n",
        "\n",
        "lossFunc = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "\n",
        "model.compile(optimizer='adam', loss=lossFunc,\n",
        "              metrics=['accuracy',myfunc])\n",
        "model.fit(t_x, t_y, epochs=5)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3763 - accuracy: 0.7471 - myfunc: 2.3763\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6045 - accuracy: 0.8451 - myfunc: 0.6045\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4766 - accuracy: 0.8739 - myfunc: 0.4766\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4257 - accuracy: 0.8862 - myfunc: 0.4257\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3930 - accuracy: 0.8965 - myfunc: 0.3930\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f61a00225f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lp4YU0ACD6d7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6acdbeba-b873-446c-b7be-1917cab37ea4"
      },
      "source": [
        "model2 = tf.keras.Sequential()\n",
        "model2.add(layers.Flatten())\n",
        "model2.add(layers.Dense(128, activation=\"relu\"))\n",
        "model2.add(layers.Dropout(0.2))\n",
        "model2.add(layers.Dense(10))\n",
        "\n",
        "lossFunc = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "\n",
        "model2.compile(optimizer='adam', loss=myfunc,\n",
        "              metrics=['accuracy'])\n",
        "model2.fit(t_x, t_y, epochs=5)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.5273 - accuracy: 0.7724\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5847 - accuracy: 0.8528\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4876 - accuracy: 0.8727\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4306 - accuracy: 0.8870\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3893 - accuracy: 0.8984\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f619ff9ecf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-76IOfG3Hf3U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}