{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers.core import Dense\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Dense"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[core.Dense.0] test 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (2,)\n",
      "out: [7.3, -0.21]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = Input(shape=(6,))\n",
    "layer_1 = Dense(2, activation='linear')(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
    "b = np.array([0.5, 0.7])\n",
    "model.set_weights([W, b])\n",
    "\n",
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "result = model.predict(np.array([arr_in]))\n",
    "arr_out = result[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[core.Dense.1] test 2 (with sigmoid activation)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (2,)\n",
      "out: [0.999325, 0.447692]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = Input(shape=(6,))\n",
    "layer_1 = Dense(2, activation='sigmoid')(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
    "b = np.array([0.5, 0.7])\n",
    "model.set_weights([W, b])\n",
    "\n",
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "result = model.predict(np.array([arr_in]))\n",
    "arr_out = result[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[core.Dense.2] test 3 (with softplus activation and no bias)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (2,)\n",
      "out: [6.801113, 0.338274]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = Input(shape=(6,))\n",
    "layer_1 = Dense(2, activation='softplus', bias=False)(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
    "model.set_weights([W])\n",
    "\n",
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "result = model.predict(np.array([arr_in]))\n",
    "arr_out = result[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[core.Dense.3] [GPU] test 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (2,)\n",
      "out: [7.3, -0.21]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = Input(shape=(6,))\n",
    "layer_1 = Dense(2, activation='linear')(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
    "b = np.array([0.5, 0.7])\n",
    "model.set_weights([W, b])\n",
    "\n",
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "result = model.predict(np.array([arr_in]))\n",
    "arr_out = result[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[core.Dense.4] [GPU] test 2 (with sigmoid activation)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (2,)\n",
      "out: [0.999325, 0.447692]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = Input(shape=(6,))\n",
    "layer_1 = Dense(2, activation='sigmoid')(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
    "b = np.array([0.5, 0.7])\n",
    "model.set_weights([W, b])\n",
    "\n",
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "result = model.predict(np.array([arr_in]))\n",
    "arr_out = result[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[core.Dense.5] [GPU] test 3 (with softplus activation and no bias)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (2,)\n",
      "out: [6.801113, 0.338274]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = Input(shape=(6,))\n",
    "layer_1 = Dense(2, activation='softplus', bias=False)(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
    "model.set_weights([W])\n",
    "\n",
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "result = model.predict(np.array([arr_in]))\n",
    "arr_out = result[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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