keras_plot_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "import IPython\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Dense(3, activation='relu', input_dim=3))\n",
    "model.add(keras.layers.Dense(5, activation='relu'))\n",
    "model.add(keras.layers.Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Option-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q6mTEiBESExMjBQUFUldXJ01NTfLJJ59IYmKizRO0rJUDkCeeeEK+//57aW1tldraWlm/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\n17dvX2RkZCAkJMSsvKOjA5mZmR6KioicicmayA9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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model, to_file='test_keras_plot_model.png', show_shapes=True)\n",
    "IPython.display.Image(\"test_keras_plot_model.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Option-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"264pt\" viewBox=\"0.00 0.00 217.00 264.00\" width=\"217pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 260)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-260 213,-260 213,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- 140154817062728 -->\n",
       "<g class=\"node\" id=\"node1\"><title>140154817062728</title>\n",
       "<polygon fill=\"none\" points=\"0,-219.5 0,-255.5 209,-255.5 209,-219.5 0,-219.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"104.5\" y=\"-233.8\">dense_4_input: InputLayer</text>\n",
       "</g>\n",
       "<!-- 140154817060992 -->\n",
       "<g class=\"node\" id=\"node2\"><title>140154817060992</title>\n",
       "<polygon fill=\"none\" points=\"40.5,-146.5 40.5,-182.5 168.5,-182.5 168.5,-146.5 40.5,-146.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"104.5\" y=\"-160.8\">dense_4: Dense</text>\n",
       "</g>\n",
       "<!-- 140154817062728&#45;&gt;140154817060992 -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>140154817062728-&gt;140154817060992</title>\n",
       "<path d=\"M104.5,-219.313C104.5,-211.289 104.5,-201.547 104.5,-192.569\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"108,-192.529 104.5,-182.529 101,-192.529 108,-192.529\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 140154817278920 -->\n",
       "<g class=\"node\" id=\"node3\"><title>140154817278920</title>\n",
       "<polygon fill=\"none\" points=\"40.5,-73.5 40.5,-109.5 168.5,-109.5 168.5,-73.5 40.5,-73.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"104.5\" y=\"-87.8\">dense_5: Dense</text>\n",
       "</g>\n",
       "<!-- 140154817060992&#45;&gt;140154817278920 -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>140154817060992-&gt;140154817278920</title>\n",
       "<path d=\"M104.5,-146.313C104.5,-138.289 104.5,-128.547 104.5,-119.569\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"108,-119.529 104.5,-109.529 101,-119.529 108,-119.529\" stroke=\"black\"/>\n",
       "</g>\n",
       "<!-- 140154817278976 -->\n",
       "<g class=\"node\" id=\"node4\"><title>140154817278976</title>\n",
       "<polygon fill=\"none\" points=\"40.5,-0.5 40.5,-36.5 168.5,-36.5 168.5,-0.5 40.5,-0.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"104.5\" y=\"-14.8\">dense_6: Dense</text>\n",
       "</g>\n",
       "<!-- 140154817278920&#45;&gt;140154817278976 -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>140154817278920-&gt;140154817278976</title>\n",
       "<path d=\"M104.5,-73.3129C104.5,-65.2895 104.5,-55.5475 104.5,-46.5691\" fill=\"none\" stroke=\"black\"/>\n",
       "<polygon fill=\"black\" points=\"108,-46.5288 104.5,-36.5288 101,-46.5289 108,-46.5288\" stroke=\"black\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.SVG(keras.utils.vis_utils.model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_keras_model(model, show_shapes=True, show_layer_names=True):\n",
    "    from IPython.display import SVG\n",
    "    from keras.utils.vis_utils import model_to_dot\n",
    "    return SVG(model_to_dot(model, show_shapes=show_shapes, show_layer_names=show_layer_names).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"304pt\" viewBox=\"0.00 0.00 256.00 304.00\" width=\"256pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 300)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-300 252,-300 252,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- 140154817062728 -->\n",
       "<g class=\"node\" id=\"node1\"><title>140154817062728</title>\n",
       "<polygon fill=\"none\" points=\"-1.42109e-14,-249.5 -1.42109e-14,-295.5 248,-295.5 248,-249.5 -1.42109e-14,-249.5\" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"48.5\" y=\"-268.8\">InputLayer</text>\n",
       "<polyline fill=\"none\" points=\"97,-249.5 97,-295.5 \" stroke=\"black\"/>\n",
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