notebooks/graph/graph_06.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import json\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, BatchNormalization, Concatenate\n",
    "from keras import backend as K\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def format_decimal(arr, places=6):\n",
    "    return [round(x * 10**places) / 10**places for x in arr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DATA = OrderedDict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### graph 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "random_seed = 10006\n",
    "data_in_shape = (8, 8, 2)\n",
    "\n",
    "input_layer_0 = Input(shape=data_in_shape)\n",
    "branch_0 = Conv2D(4, (3,3), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True,\n",
    "                         name='conv_0_0')(input_layer_0)\n",
    "\n",
    "input_layer_1 = Input(shape=data_in_shape)\n",
    "branch_1 = Conv2D(2, (1,1), activation='relu', padding='valid', strides=(1,1), data_format='channels_last', use_bias=True,\n",
    "                         name='conv_1_0')(input_layer_1)\n",
    "branch_1 = Conv2D(4, (3,3), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True,\n",
    "                         name='conv_1_1')(branch_1)\n",
    "branch_1 = Conv2D(2, (1,1), activation='relu', padding='valid', strides=(1,1), data_format='channels_last', use_bias=True,\n",
    "                         name='conv_1_2')(branch_1)\n",
    "\n",
    "input_layer_2 = Input(shape=data_in_shape)\n",
    "branch_2 = Conv2D(5, (3,3), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True,\n",
    "                         name='conv_2_0')(input_layer_2)\n",
    "branch_2 = Conv2D(3, (3,3), activation='relu', padding='same', strides=(1,1), data_format='channels_last', use_bias=True,\n",
    "                         name='conv_2_1')(branch_2)\n",
    "\n",
    "output_layer = Concatenate()([branch_0, branch_1, branch_2])\n",
    "model = Model(inputs=[input_layer_0, input_layer_1, input_layer_2], outputs=output_layer)\n",
    "\n",
    "data_in = []\n",
    "for i in range(3):\n",
    "    np.random.seed(random_seed + i)\n",
    "    data_in.append(np.expand_dims(2 * np.random.random(data_in_shape) - 1, axis=0))\n",
    "\n",
    "# set weights to random (use seed for reproducibility)\n",
    "weights = []\n",
    "for i, w in enumerate(model.get_weights()):\n",
    "    np.random.seed(random_seed + i)\n",
    "    weights.append(2 * np.random.random(w.shape) - 1)\n",
    "model.set_weights(weights)\n",
    "\n",
    "result = model.predict(data_in)\n",
    "data_out_shape = result[0].shape\n",
    "data_in_formatted = [format_decimal(data_in[i].ravel().tolist()) for i in range(3)]\n",
    "data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
    "\n",
    "DATA['graph_06'] = {\n",
    "    'inputs': [{'data': data_in_formatted[i], 'shape': data_in_shape} for i in range(3)],\n",
    "    'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights],\n",
    "    'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['input_2', 'conv_1_0', 'input_3', 'input_1', 'conv_1_1', 'conv_2_0', 'conv_0_0', 'conv_1_2', 'conv_2_1', 'concatenate_1']\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "config = json.loads(model.to_json())\n",
    "print([x['config']['name'] for x in config['config']['layers']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### export for Keras.js tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "filename = '../../test/data/graph/06.json'\n",
    "if not os.path.exists(os.path.dirname(filename)):\n",
    "    os.makedirs(os.path.dirname(filename))\n",
    "with open(filename, 'w') as f:\n",
    "    json.dump(DATA, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"graph_06\": {\"inputs\": [{\"data\": [0.87859, 0.511819, -0.499979, 0.899103, -0.273074, -0.448988, 0.778226, -0.725762, 0.533488, -0.254761, 0.563277, 0.149018, -0.800132, -0.323823, -0.687353, -0.853655, 0.344338, -0.804382, -0.339254, -0.814494, -0.931578, -0.554643, 0.526105, 0.197187, -0.423878, -0.86257, -0.805159, 0.875224, -0.45214, -0.480211, -0.152902, 0.569441, -0.211393, -0.317172, -0.578325, 0.08373, -0.290923, -0.917902, -0.79171, -0.507596, -0.688969, 0.13645, -0.227688, -0.984034, -0.12649, 0.788219, 0.613676, 0.486748, 0.810045, 0.79828, 0.258775, -0.365513, 0.780025, -0.883853, 0.036758, -0.106986, 0.678987, -0.117617, 0.719819, -0.904588, -0.003723, 0.744139, 0.344811, -0.832108, -0.69998, 0.185014, 0.256453, -0.047385, -0.870467, -0.492493, 0.739994, 0.674833, 0.475455, -0.095844, -0.920258, 0.895529, 0.933401, 0.086867, -0.084179, 0.058479, -0.698595, 0.109934, -0.497638, -0.759723, 0.184804, -0.097758, -0.482257, 0.052997, -0.120057, -0.599685, -0.324373, -0.776589, 0.197334, -0.039466, 0.911675, -0.34869, 0.506552, 0.716607, 0.930851, -0.101359, 0.28458, 0.811404, 0.177922, 0.483056, 0.348253, -0.838838, 0.761488, -0.285614, 0.177429, -0.170556, 0.726269, -0.285743, 0.532491, -0.26225, 0.773676, 0.539706, 0.911474, 0.962223, 0.343429, 0.446745, 0.643217, -0.443733, -0.897673, -0.237513, 0.051002, 0.698658, -0.294338, -0.358399], \"shape\": [8, 8, 2]}, {\"data\": [-0.719503, 0.396686, 0.710021, -0.058892, -0.78922, -0.74094, -0.421022, -0.808307, 0.800549, -0.334559, -0.186061, 0.852542, -0.759177, -0.565928, -0.439353, -0.566006, 0.739029, 0.513581, -0.267873, 0.743929, -0.767563, -0.052566, -0.04449, 0.68184, 0.572289, 0.515837, -0.674829, -0.345664, 0.702239, -0.638317, -0.083214, -0.666578, 0.712641, -0.391794, 0.056017, -0.858674, -0.008642, -0.159135, -0.00677, 0.331901, -0.257608, -0.824415, 0.571256, 0.741805, -0.75251, 0.468774, -0.784013, 0.681435, -0.23194, 0.101493, -0.128162, -0.603826, -0.49174, -0.065357, 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     ]
    }
   ],
   "source": [
    "print(json.dumps(DATA))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}