Functional_model_two_loss_with_weights.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Functional_model_two_loss_with_weights.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM8IeW0lySQW1XOfXc5726i",
      "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/Functional_model_two_loss_with_weights.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4FpWa9qo_Dc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "outputId": "085074c7-b0dd-44b2-963b-943e481cccb8"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rECAPXnr_XLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d2090e93-be11-4799-b693-2c4486269c65"
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.0-dev20200424'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYtxPUSC_erO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_r4RZcipBbh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_tags = 12  # Number of unique issue tags\n",
        "num_words = 10000  # Size of vocabulary obtained when preprocessing text data\n",
        "num_departments = 4  # Number of departments for predictions\n",
        "\n",
        "title_input = keras.Input(shape=(None,), name='title')  # Variable-length sequence of ints\n",
        "body_input = keras.Input(shape=(None,), name='body')  # Variable-length sequence of ints\n",
        "tags_input = keras.Input(shape=(num_tags,), name='tags')  # Binary vectors of size `num_tags`\n",
        "\n",
        "# Embed each word in the title into a 64-dimensional vector\n",
        "title_features = layers.Embedding(num_words, 64)(title_input)\n",
        "# Embed each word in the text into a 64-dimensional vector\n",
        "body_features = layers.Embedding(num_words, 64)(body_input)\n",
        "\n",
        "# Reduce sequence of embedded words in the title into a single 128-dimensional vector\n",
        "title_features = layers.LSTM(128)(title_features)\n",
        "# Reduce sequence of embedded words in the body into a single 32-dimensional vector\n",
        "body_features = layers.LSTM(32)(body_features)\n",
        "\n",
        "# Merge all available features into a single large vector via concatenation\n",
        "x = layers.concatenate([title_features, body_features, tags_input])\n",
        "\n",
        "# Stick a logistic regression for priority prediction on top of the features\n",
        "priority_pred = layers.Dense(1, name='priority')(x)\n",
        "# Stick a department classifier on top of the features\n",
        "department_pred = layers.Dense(num_departments, name='department')(x)\n",
        "\n",
        "# Instantiate an end-to-end model predicting both priority and department\n",
        "model = keras.Model(inputs=[title_input, body_input, tags_input],\n",
        "                    outputs=[priority_pred, department_pred])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWM8l5kH_nQi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "0f839f85-1bb6-484b-f0e4-c4c0d5deda74"
      },
      "source": [
        "keras.utils.plot_model(model, 'multi_input_and_output_model.png', show_shapes=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWbkHYbm_x_X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\n",
        "              loss=[keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "                    keras.losses.CategoricalCrossentropy(from_logits=True)],\n",
        "              loss_weights=[1., 0.2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvmU9KQy__tL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "c554d5aa-dd22-45f7-d5c6-d8a1ee4d5468"
      },
      "source": [
        "import numpy as np\n",
        "# Dummy input data\n",
        "title_data = np.random.randint(num_words, size=(1280, 10))\n",
        "body_data = np.random.randint(num_words, size=(1280, 100))\n",
        "tags_data = np.random.randint(2, size=(1280, num_tags)).astype('float32')\n",
        "\n",
        "# Dummy target data\n",
        "priority_targets = np.random.random(size=(1280, 1))\n",
        "dept_targets = np.random.randint(2, size=(1280, num_departments))\n",
        "\n",
        "model.fit({'title': title_data, 'body': body_data, 'tags': tags_data},\n",
        "          {'priority': priority_targets, 'department': dept_targets},\n",
        "          epochs=2,\n",
        "          batch_size=32)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "40/40 [==============================] - 2s 45ms/step - loss: 1.2723 - priority_loss: 0.7062 - department_loss: 2.8304\n",
            "Epoch 2/2\n",
            "40/40 [==============================] - 2s 46ms/step - loss: 1.2593 - priority_loss: 0.6995 - department_loss: 2.7993\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3d0620a0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4Z9ws_JADZz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad6b7557-1c0a-4d08-ffa0-755518ac4b4a"
      },
      "source": [
        "# check how the weights and two loss are used to get overall loss\n",
        "#(loss1*weight1+loss2*weight2)\n",
        "(0.7062*1.0+2.8304*0.2)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.27228"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeXAeKkWAhpv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}