1_Simple_ANN_Visualization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "1. Simple ANN Visualization",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyN+WSqRbml8Eu2FFBF0uFUe",
      "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/sarthak247/keras-examples/blob/master/1_Simple_ANN_Visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQKKdCfVxd_d",
        "colab_type": "text"
      },
      "source": [
        "# Install Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51IuAHwguNJN",
        "colab_type": "code",
        "outputId": "b69d80dc-12bb-4988-899b-a1120f66ebdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!pip3 install ann_visualizer\n",
        "!pip3 install graphviz"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ann_visualizer in /usr/local/lib/python3.6/dist-packages (2.5)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (0.10.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgqOFd9sxrlX",
        "colab_type": "text"
      },
      "source": [
        "# Check Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VC5Jt9R_xuoK",
        "colab_type": "code",
        "outputId": "0ee3c0df-77a9-4ece-ebe5-c575e4d7cc66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip3 list | grep \"keras\""
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "keras-vis                0.4.1          \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9ngZetcx5qZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip3 list | grep \"ann_visualizer\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QW7Zsxix6Ur",
        "colab_type": "code",
        "outputId": "446541c3-2c03-4ebc-87be-e732d303c740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip3 list | grep \"graphviz\""
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "graphviz                 0.10.1         \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVOZjkygxxzP",
        "colab_type": "text"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbuqrtvYxbUO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.utils import plot_model\n",
        "from ann_visualizer.visualize import ann_viz;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QxKPEObx_ao",
        "colab_type": "text"
      },
      "source": [
        "# Create ANN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xm5HrZRguscl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get Base Model\n",
        "model = Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stIbls3Gu7hL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add Layers to Model\n",
        "model.add(Dense(5, input_shape=(3,),activation='relu'))\n",
        "model.add(Dense(5, activation='relu'))\n",
        "model.add(Dense(5, activation='relu'))\n",
        "model.add(Dense(2, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkHaHuUxvVSR",
        "colab_type": "code",
        "outputId": "a737db0f-9c31-4306-b69c-bd17f278f218",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "# Get Summary of Model\n",
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_5 (Dense)              (None, 5)                 20        \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 5)                 30        \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 5)                 30        \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 2)                 12        \n",
            "=================================================================\n",
            "Total params: 92\n",
            "Trainable params: 92\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5KFJm0gyNUT",
        "colab_type": "text"
      },
      "source": [
        "# Visualize ANN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6t8jXHWv3Qx",
        "colab_type": "code",
        "outputId": "cf5da5dc-c9cf-48d0-a314-940a7a6e46ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "source": [
        "plot_model(model, to_file='model.png', show_shapes=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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EFStWGK0+S3fmzBlkZGQI/rvvUNU1/t3pd/CRyWQAgAcPHvRY7uE8FHFxcf1qMyAgAB9/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\nPowxxkyOkw9jjDGT4+TDGGPM5P4ff2FP0GxskhoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlmNiI7dwxkU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ann_viz(model, title=\"My First ANN Implementation\", filename='model')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}