{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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.convolutional import UpSampling3D\n",
    "from keras import backend as K"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "### UpSampling3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[convolutional.UpSampling3D.0] size 2x2x2 upsampling on 2x2x2x3 input, dim_ordering='tf'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "in shape: (2, 2, 2, 3)\n",
      "in: [-0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858]\n",
      "out shape: (4, 4, 4, 3)\n",
      "out: [-0.806777, -0.564841, -0.481331, -0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, 0.559626, 0.274958, -0.659222, -0.806777, -0.564841, -0.481331, -0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, 0.559626, 0.274958, -0.659222, -0.178541, 0.689453, -0.028873, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.053859, -0.446394, -0.53406, -0.178541, 0.689453, -0.028873, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.053859, -0.446394, -0.53406, -0.806777, -0.564841, -0.481331, -0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, 0.559626, 0.274958, -0.659222, -0.806777, -0.564841, -0.481331, -0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, 0.559626, 0.274958, -0.659222, -0.178541, 0.689453, -0.028873, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.053859, -0.446394, -0.53406, -0.178541, 0.689453, -0.028873, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.053859, -0.446394, -0.53406, 0.776897, -0.700858, -0.802179, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.616515, 0.718677, 0.303042, 0.776897, -0.700858, -0.802179, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.616515, 0.718677, 0.303042, -0.080606, -0.850593, -0.795971, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858, 0.860487, -0.90685, 0.89858, -0.080606, -0.850593, -0.795971, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858, 0.860487, -0.90685, 0.89858, 0.776897, -0.700858, -0.802179, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.616515, 0.718677, 0.303042, 0.776897, -0.700858, -0.802179, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.616515, 0.718677, 0.303042, -0.080606, -0.850593, -0.795971, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858, 0.860487, -0.90685, 0.89858, -0.080606, -0.850593, -0.795971, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858, 0.860487, -0.90685, 0.89858]\n"
     ]
    }
   ],
   "source": [
    "data_in_shape = (2, 2, 2, 3)\n",
    "L = UpSampling3D(size=(2, 2, 2), dim_ordering='tf')\n",
    "\n",
    "layer_0 = Input(shape=data_in_shape)\n",
    "layer_1 = L(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "# set weights to random (use seed for reproducibility)\n",
    "np.random.seed(260)\n",
    "data_in = 2 * np.random.random(data_in_shape) - 1\n",
    "print('')\n",
    "print('in shape:', data_in_shape)\n",
    "print('in:', format_decimal(data_in.ravel().tolist()))\n",
    "result = model.predict(np.array([data_in]))\n",
    "print('out shape:', result[0].shape)\n",
    "print('out:', format_decimal(result[0].ravel().tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[convolutional.UpSampling3D.0] size 2x2x2 upsampling on 2x2x2x3 input, dim_ordering='th'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "in shape: (2, 2, 2, 3)\n",
      "in: [-0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858]\n",
      "out shape: (2, 4, 4, 6)\n",
      "out: [-0.806777, -0.806777, -0.564841, -0.564841, -0.481331, -0.481331, -0.806777, -0.806777, -0.564841, -0.564841, -0.481331, -0.481331, 0.559626, 0.559626, 0.274958, 0.274958, -0.659222, -0.659222, 0.559626, 0.559626, 0.274958, 0.274958, -0.659222, -0.659222, -0.806777, -0.806777, -0.564841, -0.564841, -0.481331, -0.481331, -0.806777, -0.806777, -0.564841, -0.564841, -0.481331, -0.481331, 0.559626, 0.559626, 0.274958, 0.274958, -0.659222, -0.659222, 0.559626, 0.559626, 0.274958, 0.274958, -0.659222, -0.659222, -0.178541, -0.178541, 0.689453, 0.689453, -0.028873, -0.028873, -0.178541, -0.178541, 0.689453, 0.689453, -0.028873, -0.028873, 0.053859, 0.053859, -0.446394, -0.446394, -0.53406, -0.53406, 0.053859, 0.053859, -0.446394, -0.446394, -0.53406, -0.53406, -0.178541, -0.178541, 0.689453, 0.689453, -0.028873, -0.028873, -0.178541, -0.178541, 0.689453, 0.689453, -0.028873, -0.028873, 0.053859, 0.053859, -0.446394, -0.446394, -0.53406, -0.53406, 0.053859, 0.053859, -0.446394, -0.446394, -0.53406, -0.53406, 0.776897, 0.776897, -0.700858, -0.700858, -0.802179, -0.802179, 0.776897, 0.776897, -0.700858, -0.700858, -0.802179, -0.802179, -0.616515, -0.616515, 0.718677, 0.718677, 0.303042, 0.303042, -0.616515, -0.616515, 0.718677, 0.718677, 0.303042, 0.303042, 0.776897, 0.776897, -0.700858, -0.700858, -0.802179, -0.802179, 0.776897, 0.776897, -0.700858, -0.700858, -0.802179, -0.802179, -0.616515, -0.616515, 0.718677, 0.718677, 0.303042, 0.303042, -0.616515, -0.616515, 0.718677, 0.718677, 0.303042, 0.303042, -0.080606, -0.080606, -0.850593, -0.850593, -0.795971, -0.795971, -0.080606, -0.080606, -0.850593, -0.850593, -0.795971, -0.795971, 0.860487, 0.860487, -0.90685, -0.90685, 0.89858, 0.89858, 0.860487, 0.860487, -0.90685, -0.90685, 0.89858, 0.89858, -0.080606, -0.080606, -0.850593, -0.850593, -0.795971, -0.795971, -0.080606, -0.080606, -0.850593, -0.850593, -0.795971, -0.795971, 0.860487, 0.860487, -0.90685, -0.90685, 0.89858, 0.89858, 0.860487, 0.860487, -0.90685, -0.90685, 0.89858, 0.89858]\n"
     ]
    }
   ],
   "source": [
    "data_in_shape = (2, 2, 2, 3)\n",
    "L = UpSampling3D(size=(2, 2, 2), dim_ordering='th')\n",
    "\n",
    "layer_0 = Input(shape=data_in_shape)\n",
    "layer_1 = L(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "# set weights to random (use seed for reproducibility)\n",
    "np.random.seed(260)\n",
    "data_in = 2 * np.random.random(data_in_shape) - 1\n",
    "print('')\n",
    "print('in shape:', data_in_shape)\n",
    "print('in:', format_decimal(data_in.ravel().tolist()))\n",
    "result = model.predict(np.array([data_in]))\n",
    "print('out shape:', result[0].shape)\n",
    "print('out:', format_decimal(result[0].ravel().tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[convolutional.UpSampling2D.2] size 1x3x2 upsampling on 2x1x3x2 input, dim_ordering='tf'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "in shape: (2, 1, 3, 2)\n",
      "in: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022]\n",
      "out shape: (2, 3, 6, 2)\n",
      "out: [-0.989173, -0.133618, -0.989173, -0.133618, -0.505338, 0.023259, -0.505338, 0.023259, 0.503982, -0.303769, 0.503982, -0.303769, -0.989173, -0.133618, -0.989173, -0.133618, -0.505338, 0.023259, -0.505338, 0.023259, 0.503982, -0.303769, 0.503982, -0.303769, -0.989173, -0.133618, -0.989173, -0.133618, -0.505338, 0.023259, -0.505338, 0.023259, 0.503982, -0.303769, 0.503982, -0.303769, -0.436321, 0.793911, -0.436321, 0.793911, 0.416102, 0.806405, 0.416102, 0.806405, -0.098342, -0.738022, -0.098342, -0.738022, -0.436321, 0.793911, -0.436321, 0.793911, 0.416102, 0.806405, 0.416102, 0.806405, -0.098342, -0.738022, -0.098342, -0.738022, -0.436321, 0.793911, -0.436321, 0.793911, 0.416102, 0.806405, 0.416102, 0.806405, -0.098342, -0.738022, -0.098342, -0.738022]\n"
     ]
    }
   ],
   "source": [
    "data_in_shape = (2, 1, 3, 2)\n",
    "L = UpSampling3D(size=(1, 3, 2), dim_ordering='tf')\n",
    "\n",
    "layer_0 = Input(shape=data_in_shape)\n",
    "layer_1 = L(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "# set weights to random (use seed for reproducibility)\n",
    "np.random.seed(252)\n",
    "data_in = 2 * np.random.random(data_in_shape) - 1\n",
    "print('')\n",
    "print('in shape:', data_in_shape)\n",
    "print('in:', format_decimal(data_in.ravel().tolist()))\n",
    "result = model.predict(np.array([data_in]))\n",
    "print('out shape:', result[0].shape)\n",
    "print('out:', format_decimal(result[0].ravel().tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[convolutional.UpSampling2D.2] size 2x1x2 upsampling on 2x1x3x3 input, dim_ordering='th'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "in shape: (2, 1, 3, 3)\n",
      "in: [-0.47588, 0.366985, 0.040173, 0.015578, -0.906159, 0.241982, -0.771299, -0.443554, -0.56404, -0.17751, 0.541277, -0.233327, 0.024369, 0.858275, 0.496191, 0.980574, -0.59522, 0.480899]\n",
      "out shape: (2, 2, 3, 6)\n",
      "out: [-0.47588, -0.47588, 0.366985, 0.366985, 0.040173, 0.040173, 0.015578, 0.015578, -0.906159, -0.906159, 0.241982, 0.241982, -0.771299, -0.771299, -0.443554, -0.443554, -0.56404, -0.56404, -0.47588, -0.47588, 0.366985, 0.366985, 0.040173, 0.040173, 0.015578, 0.015578, -0.906159, -0.906159, 0.241982, 0.241982, -0.771299, -0.771299, -0.443554, -0.443554, -0.56404, -0.56404, -0.17751, -0.17751, 0.541277, 0.541277, -0.233327, -0.233327, 0.024369, 0.024369, 0.858275, 0.858275, 0.496191, 0.496191, 0.980574, 0.980574, -0.59522, -0.59522, 0.480899, 0.480899, -0.17751, -0.17751, 0.541277, 0.541277, -0.233327, -0.233327, 0.024369, 0.024369, 0.858275, 0.858275, 0.496191, 0.496191, 0.980574, 0.980574, -0.59522, -0.59522, 0.480899, 0.480899]\n"
     ]
    }
   ],
   "source": [
    "data_in_shape = (2, 1, 3, 3)\n",
    "L = UpSampling3D(size=(2, 1, 2), dim_ordering='th')\n",
    "\n",
    "layer_0 = Input(shape=data_in_shape)\n",
    "layer_1 = L(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "# set weights to random (use seed for reproducibility)\n",
    "np.random.seed(253)\n",
    "data_in = 2 * np.random.random(data_in_shape) - 1\n",
    "print('')\n",
    "print('in shape:', data_in_shape)\n",
    "print('in:', format_decimal(data_in.ravel().tolist()))\n",
    "result = model.predict(np.array([data_in]))\n",
    "print('out shape:', result[0].shape)\n",
    "print('out:', format_decimal(result[0].ravel().tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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