GRAPH_INSTALL_IN_GOOGLE_COOLAB.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GRAPH INSTALL IN GOOGLE COOLAB.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "VtQu11lYG1Dv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -q pydot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yIvymNpGG18Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2877
        },
        "outputId": "cdad3c58-120c-43c5-ed39-a65c89574195"
      },
      "cell_type": "code",
      "source": [
        "!apt-get install graphviz"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
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            "  libpangocairo-1.0-0 libpangoft2-1.0-0 libpathplan4 libpixman-1-0\n",
            "  libthai-data libthai0 libtiff5 libwebp6 libxaw7 libxcb-render0 libxcb-shm0\n",
            "  libxmu6 libxpm4 libxt6\n",
            "Suggested packages:\n",
            "  gsfonts graphviz-doc libgd-tools\n",
            "The following NEW packages will be installed:\n",
            "  fontconfig graphviz libcairo2 libcdt5 libcgraph6 libdatrie1 libgd3\n",
            "  libgraphite2-3 libgvc6 libgvpr2 libharfbuzz0b libjbig0 libltdl7\n",
            "  libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpathplan4\n",
            "  libpixman-1-0 libthai-data libthai0 libtiff5 libwebp6 libxaw7 libxcb-render0\n",
            "  libxcb-shm0 libxmu6 libxpm4 libxt6\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VfY-DjHvHIud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3b416907-1087-406d-865e-81dd9407b136"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, Activation\n",
        "import keras.backend as K\n",
        "from keras import optimizers\n",
        "import numpy as np\n",
        "import h5py\n",
        "from keras.layers import Input,Dense,Convolution2D,Activation,MaxPooling2D,Flatten,merge\n",
        "from keras.layers.merge import add\n",
        "from keras.models import Model, load_model\n",
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "from keras.utils import plot_model\n",
        "from keras.layers.convolutional import Deconv2D as Conv2DTranspose"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "w3Zpw15BHOMG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "678d2d6c-9d2b-4551-df2b-54a15dcbeb6f"
      },
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    Conv2D(64, kernel_size=(9,9), input_shape=(32,32,1), activation ='relu',padding='same'),\n",
        "    Conv2D(32, kernel_size=(1,1),activation='relu',padding='same'),\n",
        "    Conv2D(1, kernel_size=(5,5),activation='relu',padding='same'),\n",
        "])\n",
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 64)        5248      \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        2080      \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 32, 32, 1)         801       \n",
            "=================================================================\n",
            "Total params: 8,129\n",
            "Trainable params: 8,129\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "N1q7FDxAHRXN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "43937ee5-dfa6-40ba-d1ec-0cebc0d21eb5"
      },
      "cell_type": "code",
      "source": [
        "SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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