From 9b07e511bf384a08437d97e0c05da998ed6bebe8 Mon Sep 17 00:00:00 2001
From: Leon Chen <leon@md.ai>
Date: Fri, 26 Aug 2016 22:29:22 -0400
Subject: [PATCH] implement UpSampling1D/2D/3D layers, with tests

---
 index.html                                  |   6 +
 notebooks/convolutional/Convolution1D.ipynb |  10 +-
 notebooks/convolutional/UpSampling1D.ipynb  | 158 ++++++++++++
 notebooks/convolutional/UpSampling2D.ipynb  | 256 ++++++++++++++++++++
 notebooks/convolutional/UpSampling3D.ipynb  | 256 ++++++++++++++++++++
 src/layers/convolutional/UpSampling1D.js    |  36 +++
 src/layers/convolutional/UpSampling2D.js    |  58 +++++
 src/layers/convolutional/UpSampling3D.js    |  61 +++++
 src/layers/convolutional/index.js           |   8 +-
 test/convolutional/UpSampling1D.js          |  48 ++++
 test/convolutional/UpSampling2D.js          |  82 +++++++
 test/convolutional/UpSampling3D.js          |  82 +++++++
 test/convolutional/data_UpSampling1D.js     |  30 +++
 test/convolutional/data_UpSampling2D.js     |  50 ++++
 test/convolutional/data_UpSampling3D.js     |  50 ++++
 15 files changed, 1185 insertions(+), 6 deletions(-)
 create mode 100644 notebooks/convolutional/UpSampling1D.ipynb
 create mode 100644 notebooks/convolutional/UpSampling2D.ipynb
 create mode 100644 notebooks/convolutional/UpSampling3D.ipynb
 create mode 100644 src/layers/convolutional/UpSampling1D.js
 create mode 100644 src/layers/convolutional/UpSampling2D.js
 create mode 100644 src/layers/convolutional/UpSampling3D.js
 create mode 100644 test/convolutional/UpSampling1D.js
 create mode 100644 test/convolutional/UpSampling2D.js
 create mode 100644 test/convolutional/UpSampling3D.js
 create mode 100644 test/convolutional/data_UpSampling1D.js
 create mode 100644 test/convolutional/data_UpSampling2D.js
 create mode 100644 test/convolutional/data_UpSampling3D.js

diff --git a/index.html b/index.html
index 8e16960..8f1e023 100644
--- a/index.html
+++ b/index.html
@@ -49,6 +49,12 @@
   <script src="/test/convolutional/Convolution1D.js"></script>
   <script src="/test/convolutional/data_Convolution2D.js"></script>
   <script src="/test/convolutional/Convolution2D.js"></script>
+  <script src="/test/convolutional/data_UpSampling1D.js"></script>
+  <script src="/test/convolutional/UpSampling1D.js"></script>
+  <script src="/test/convolutional/data_UpSampling2D.js"></script>
+  <script src="/test/convolutional/UpSampling2D.js"></script>
+  <script src="/test/convolutional/data_UpSampling3D.js"></script>
+  <script src="/test/convolutional/UpSampling3D.js"></script>
 
   <script>
     // mocha.checkLeaks();
diff --git a/notebooks/convolutional/Convolution1D.ipynb b/notebooks/convolutional/Convolution1D.ipynb
index dbe3d9b..ae560f7 100644
--- a/notebooks/convolutional/Convolution1D.ipynb
+++ b/notebooks/convolutional/Convolution1D.ipynb
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -216,12 +216,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**[convolutional.Convolution1D.4] 2 length 7 filters on 8x3 input, activation='tanh', border_mode='same', subsample_length=1, bias=True**"
+    "**[convolutional.Convolution1D.3] 2 length 7 filters on 8x3 input, activation='tanh', border_mode='same', subsample_length=1, bias=True**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
diff --git a/notebooks/convolutional/UpSampling1D.ipynb b/notebooks/convolutional/UpSampling1D.ipynb
new file mode 100644
index 0000000..c855f9c
--- /dev/null
+++ b/notebooks/convolutional/UpSampling1D.ipynb
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.convolutional import UpSampling1D\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": [
+    "### UpSampling1D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.UpSampling1D.0] length 2 upsampling on 3x5 input**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (3, 5)\n",
+      "in: [0.262, 0.764609, -0.482897, -0.371755, 0.871769, 0.490033, -0.986894, -0.960468, 0.373039, 0.911356, 0.00298, 0.270652, 0.749006, 0.692235, 0.471778]\n",
+      "out shape: (6, 5)\n",
+      "out: [0.262, 0.764609, -0.482897, -0.371755, 0.871769, 0.262, 0.764609, -0.482897, -0.371755, 0.871769, 0.490033, -0.986894, -0.960468, 0.373039, 0.911356, 0.490033, -0.986894, -0.960468, 0.373039, 0.911356, 0.00298, 0.270652, 0.749006, 0.692235, 0.471778, 0.00298, 0.270652, 0.749006, 0.692235, 0.471778]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (3, 5)\n",
+    "L = UpSampling1D(length=2)\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(230)\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.UpSampling1D.0] length 3 upsampling on 4x4 input**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (4, 4)\n",
+      "in: [0.562988, 0.168418, -0.14658, -0.369311, 0.653777, 0.806859, -0.922124, 0.830445, -0.878989, -0.638546, -0.855401, -0.082476, 0.416718, -0.03353, -0.949107, -0.866195]\n",
+      "out shape: (12, 4)\n",
+      "out: [0.562988, 0.168418, -0.14658, -0.369311, 0.562988, 0.168418, -0.14658, -0.369311, 0.562988, 0.168418, -0.14658, -0.369311, 0.653777, 0.806859, -0.922124, 0.830445, 0.653777, 0.806859, -0.922124, 0.830445, 0.653777, 0.806859, -0.922124, 0.830445, -0.878989, -0.638546, -0.855401, -0.082476, -0.878989, -0.638546, -0.855401, -0.082476, -0.878989, -0.638546, -0.855401, -0.082476, 0.416718, -0.03353, -0.949107, -0.866195, 0.416718, -0.03353, -0.949107, -0.866195, 0.416718, -0.03353, -0.949107, -0.866195]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (4, 4)\n",
+    "L = UpSampling1D(length=3)\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(231)\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": []
+  }
+ ],
+ "metadata": {
+  "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/convolutional/UpSampling2D.ipynb b/notebooks/convolutional/UpSampling2D.ipynb
new file mode 100644
index 0000000..e7e9494
--- /dev/null
+++ b/notebooks/convolutional/UpSampling2D.ipynb
@@ -0,0 +1,256 @@
+{
+ "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 UpSampling2D\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": [
+    "### UpSampling2D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.UpSampling2D.0] size 2x2 upsampling on 3x3x3 input, dim_ordering='tf'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (3, 3, 3)\n",
+      "in: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094]\n",
+      "out shape: (6, 6, 3)\n",
+      "out: [-0.570441, -0.454673, -0.285321, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.160547, -0.332203, 0.546391, -0.570441, -0.454673, -0.285321, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, 0.753553, -0.031788, 0.915329, 0.272735, 0.010827, -0.763164, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.162138, 0.565201, -0.492094, -0.738844, 0.269075, 0.434091, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.162138, 0.565201, -0.492094]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (3, 3, 3)\n",
+    "L = UpSampling2D(size=(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(250)\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.0] size 2x2 upsampling on 3x3x3 input, dim_ordering='th'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (3, 3, 3)\n",
+      "in: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094]\n",
+      "out shape: (3, 6, 6)\n",
+      "out: [-0.570441, -0.570441, -0.454673, -0.454673, -0.285321, -0.285321, -0.570441, -0.570441, -0.454673, -0.454673, -0.285321, -0.285321, 0.237249, 0.237249, 0.282682, 0.282682, 0.428035, 0.428035, 0.237249, 0.237249, 0.282682, 0.282682, 0.428035, 0.428035, 0.160547, 0.160547, -0.332203, -0.332203, 0.546391, 0.546391, 0.160547, 0.160547, -0.332203, -0.332203, 0.546391, 0.546391, 0.272735, 0.272735, 0.010827, 0.010827, -0.763164, -0.763164, 0.272735, 0.272735, 0.010827, 0.010827, -0.763164, -0.763164, -0.442696, -0.442696, 0.381948, 0.381948, -0.676994, -0.676994, -0.442696, -0.442696, 0.381948, 0.381948, -0.676994, -0.676994, 0.753553, 0.753553, -0.031788, -0.031788, 0.915329, 0.915329, 0.753553, 0.753553, -0.031788, -0.031788, 0.915329, 0.915329, -0.738844, -0.738844, 0.269075, 0.269075, 0.434091, 0.434091, -0.738844, -0.738844, 0.269075, 0.269075, 0.434091, 0.434091, 0.991585, 0.991585, -0.944288, -0.944288, 0.258834, 0.258834, 0.991585, 0.991585, -0.944288, -0.944288, 0.258834, 0.258834, 0.162138, 0.162138, 0.565201, 0.565201, -0.492094, -0.492094, 0.162138, 0.162138, 0.565201, 0.565201, -0.492094, -0.492094]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (3, 3, 3)\n",
+    "L = UpSampling2D(size=(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(250)\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 3x2 upsampling on 4x2x2 input, dim_ordering='tf'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (4, 2, 2)\n",
+      "in: [0.275222, -0.793967, -0.468107, -0.841484, -0.295362, 0.78175, 0.068787, -0.261747, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.99869, -0.963008]\n",
+      "out shape: (12, 4, 2)\n",
+      "out: [0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, 0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, 0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (4, 2, 2)\n",
+    "L = UpSampling2D(size=(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(251)\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.3] size 1x3 upsampling on 4x3x2 input, dim_ordering='th'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (4, 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, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111]\n",
+      "out shape: (4, 3, 6)\n",
+      "out: [-0.989173, -0.989173, -0.989173, -0.133618, -0.133618, -0.133618, -0.505338, -0.505338, -0.505338, 0.023259, 0.023259, 0.023259, 0.503982, 0.503982, 0.503982, -0.303769, -0.303769, -0.303769, -0.436321, -0.436321, -0.436321, 0.793911, 0.793911, 0.793911, 0.416102, 0.416102, 0.416102, 0.806405, 0.806405, 0.806405, -0.098342, -0.098342, -0.098342, -0.738022, -0.738022, -0.738022, -0.982676, -0.982676, -0.982676, 0.805073, 0.805073, 0.805073, 0.741244, 0.741244, 0.741244, -0.941634, -0.941634, -0.941634, -0.253526, -0.253526, -0.253526, -0.136544, -0.136544, -0.136544, -0.295772, -0.295772, -0.295772, 0.207565, 0.207565, 0.207565, -0.517246, -0.517246, -0.517246, -0.686963, -0.686963, -0.686963, -0.176235, -0.176235, -0.176235, -0.354111, -0.354111, -0.354111]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (4, 3, 2)\n",
+    "L = UpSampling2D(size=(1, 3), 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(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": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/convolutional/UpSampling3D.ipynb b/notebooks/convolutional/UpSampling3D.ipynb
new file mode 100644
index 0000000..9803915
--- /dev/null
+++ b/notebooks/convolutional/UpSampling3D.ipynb
@@ -0,0 +1,256 @@
+{
+ "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": []
+  }
+ ],
+ "metadata": {
+  "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/src/layers/convolutional/UpSampling1D.js b/src/layers/convolutional/UpSampling1D.js
new file mode 100644
index 0000000..3e28b7e
--- /dev/null
+++ b/src/layers/convolutional/UpSampling1D.js
@@ -0,0 +1,36 @@
+import Layer from '../../engine/Layer'
+import Tensor from '../../Tensor'
+import ops from 'ndarray-ops'
+
+/**
+ * UpSampling1D layer class
+ */
+export default class UpSampling1D extends Layer {
+  /**
+   * Creates a UpSampling1D activation layer
+   * @param {number} length - upsampling factor
+   */
+  constructor (length = 2, attrs = {}) {
+    super(attrs)
+    this.length = length
+  }
+
+  /**
+   * Method for layer computational logic
+   * @param {Tensor} x
+   * @returns {Tensor} x
+   */
+  call = x => {
+    const inputShape = x.tensor.shape
+    const outputShape = [inputShape[0] * this.length, inputShape[1]]
+    let y = new Tensor([], outputShape)
+    for (let i = 0; i < this.length; i++) {
+      ops.assign(
+        y.tensor.lo(i, 0).step(this.length, 1),
+        x.tensor
+      )
+    }
+    x.tensor = y.tensor
+    return x
+  }
+}
diff --git a/src/layers/convolutional/UpSampling2D.js b/src/layers/convolutional/UpSampling2D.js
new file mode 100644
index 0000000..0794e6c
--- /dev/null
+++ b/src/layers/convolutional/UpSampling2D.js
@@ -0,0 +1,58 @@
+import Layer from '../../engine/Layer'
+import Tensor from '../../Tensor'
+import ops from 'ndarray-ops'
+
+/**
+ * UpSampling2D layer class
+ */
+export default class UpSampling2D extends Layer {
+  /**
+   * Creates a UpSampling2D activation layer
+   * @param {number} size - upsampling factor
+   */
+  constructor (size = [2, 2], attrs = {}) {
+    super(attrs)
+    const {
+      dimOrdering = 'tf'
+    } = attrs
+
+    this.size = size
+    this.dimOrdering = dimOrdering
+  }
+
+  /**
+   * Method for layer computational logic
+   * @param {Tensor} x
+   * @returns {Tensor} x
+   */
+  call = x => {
+    // convert to tf ordering
+    if (this.dimOrdering === 'th') {
+      x.tensor = x.tensor.transpose(1, 2, 0)
+    }
+
+    const inputShape = x.tensor.shape
+    const outputShape = [
+      inputShape[0] * this.size[0],
+      inputShape[1] * this.size[1],
+      inputShape[2]
+    ]
+    let y = new Tensor([], outputShape)
+    for (let i = 0; i < this.size[0]; i++) {
+      for (let j = 0; j < this.size[1]; j++) {
+        ops.assign(
+          y.tensor.lo(i, j, 0).step(this.size[0], this.size[1], 1),
+          x.tensor
+        )
+      }
+    }
+    x.tensor = y.tensor
+
+    // convert back to th ordering if necessary
+    if (this.dimOrdering === 'th') {
+      x.tensor = x.tensor.transpose(2, 0, 1)
+    }
+
+    return x
+  }
+}
diff --git a/src/layers/convolutional/UpSampling3D.js b/src/layers/convolutional/UpSampling3D.js
new file mode 100644
index 0000000..edea9af
--- /dev/null
+++ b/src/layers/convolutional/UpSampling3D.js
@@ -0,0 +1,61 @@
+import Layer from '../../engine/Layer'
+import Tensor from '../../Tensor'
+import ops from 'ndarray-ops'
+
+/**
+ * UpSampling3D layer class
+ */
+export default class UpSampling3D extends Layer {
+  /**
+   * Creates a UpSampling3D activation layer
+   * @param {number} size - upsampling factor
+   */
+  constructor (size = [2, 2, 2], attrs = {}) {
+    super(attrs)
+    const {
+      dimOrdering = 'tf'
+    } = attrs
+
+    this.size = size
+    this.dimOrdering = dimOrdering
+  }
+
+  /**
+   * Method for layer computational logic
+   * @param {Tensor} x
+   * @returns {Tensor} x
+   */
+  call = x => {
+    // convert to tf ordering
+    if (this.dimOrdering === 'th') {
+      x.tensor = x.tensor.transpose(1, 2, 3, 0)
+    }
+
+    const inputShape = x.tensor.shape
+    const outputShape = [
+      inputShape[0] * this.size[0],
+      inputShape[1] * this.size[1],
+      inputShape[2] * this.size[2],
+      inputShape[3]
+    ]
+    let y = new Tensor([], outputShape)
+    for (let i = 0; i < this.size[0]; i++) {
+      for (let j = 0; j < this.size[1]; j++) {
+        for (let k = 0; k < this.size[2]; k++) {
+          ops.assign(
+            y.tensor.lo(i, j, k, 0).step(this.size[0], this.size[1], this.size[2], 1),
+            x.tensor
+          )
+        }
+      }
+    }
+    x.tensor = y.tensor
+
+    // convert back to th ordering if necessary
+    if (this.dimOrdering === 'th') {
+      x.tensor = x.tensor.transpose(3, 0, 1, 2)
+    }
+
+    return x
+  }
+}
diff --git a/src/layers/convolutional/index.js b/src/layers/convolutional/index.js
index 4896e7c..872d0e1 100644
--- a/src/layers/convolutional/index.js
+++ b/src/layers/convolutional/index.js
@@ -1,7 +1,13 @@
 import Convolution1D from './Convolution1D'
 import Convolution2D from './Convolution2D'
+import UpSampling1D from './UpSampling1D'
+import UpSampling2D from './UpSampling2D'
+import UpSampling3D from './UpSampling3D'
 
 export {
   Convolution1D,
-  Convolution2D
+  Convolution2D,
+  UpSampling1D,
+  UpSampling2D,
+  UpSampling3D
 }
diff --git a/test/convolutional/UpSampling1D.js b/test/convolutional/UpSampling1D.js
new file mode 100644
index 0000000..f099a06
--- /dev/null
+++ b/test/convolutional/UpSampling1D.js
@@ -0,0 +1,48 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: UpSampling1D', function () {
+  const assert = chai.assert
+  const styles = testGlobals.styles
+  const logTime = testGlobals.logTime
+  const stringifyCondensed = testGlobals.stringifyCondensed
+  const approxEquals = KerasJS.testUtils.approxEquals
+  const layers = KerasJS.layers
+
+  before(function () {
+    console.log('\n%cconvolutional layer: UpSampling1D', styles.h1)
+  })
+
+  it(`[convolutional.UpSampling1D.0] length 2 upsampling on 3x5 input`, function () {
+    const key = `convolutional.UpSampling1D.0`
+    console.log(`\n%c[${key}] length 2 upsampling on 3x5 input`, styles.h3)
+    let testLayer = new layers.UpSampling1D(2)
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.UpSampling1D.1] length 3 upsampling on 4x4 input`, function () {
+    const key = `convolutional.UpSampling1D.1`
+    console.log(`\n%c[${key}] length 3 upsampling on 4x4 input`, styles.h3)
+    let testLayer = new layers.UpSampling1D(3)
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+})
diff --git a/test/convolutional/UpSampling2D.js b/test/convolutional/UpSampling2D.js
new file mode 100644
index 0000000..56ebf34
--- /dev/null
+++ b/test/convolutional/UpSampling2D.js
@@ -0,0 +1,82 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: UpSampling2D', function () {
+  const assert = chai.assert
+  const styles = testGlobals.styles
+  const logTime = testGlobals.logTime
+  const stringifyCondensed = testGlobals.stringifyCondensed
+  const approxEquals = KerasJS.testUtils.approxEquals
+  const layers = KerasJS.layers
+
+  before(function () {
+    console.log('\n%cconvolutional layer: UpSampling2D', styles.h1)
+  })
+
+  it(`[convolutional.UpSampling2D.0] size 2x2 upsampling on 3x3x3 input, dimOrdering=tf`, function () {
+    const key = `convolutional.UpSampling2D.0`
+    console.log(`\n%c[${key}] size 2x2 upsampling on 3x3x3 input, dimOrdering=tf`, styles.h3)
+    let testLayer = new layers.UpSampling2D([2, 2], { dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.UpSampling2D.1] size 2x2 upsampling on 3x3x3 input, dimOrdering=th`, function () {
+    const key = `convolutional.UpSampling2D.1`
+    console.log(`\n%c[${key}] size 2x2 upsampling on 3x3x3 input, dimOrdering=th`, styles.h3)
+    let testLayer = new layers.UpSampling2D([2, 2], { dimOrdering: 'th' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.UpSampling2D.2] size 3x2 upsampling on 4x2x2 input, dimOrdering=tf`, function () {
+    const key = `convolutional.UpSampling2D.2`
+    console.log(`\n%c[${key}] size 3x2 upsampling on 4x2x2 input, dimOrdering=tf`, styles.h3)
+    let testLayer = new layers.UpSampling2D([3, 2], { dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.UpSampling2D.3] size 1x3 upsampling on 4x3x2 input, dimOrdering=th`, function () {
+    const key = `convolutional.UpSampling2D.3`
+    console.log(`\n%c[${key}] size 1x3 upsampling on 4x3x2 input, dimOrdering=th`, styles.h3)
+    let testLayer = new layers.UpSampling2D([1, 3], { dimOrdering: 'th' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+})
diff --git a/test/convolutional/UpSampling3D.js b/test/convolutional/UpSampling3D.js
new file mode 100644
index 0000000..be4c93e
--- /dev/null
+++ b/test/convolutional/UpSampling3D.js
@@ -0,0 +1,82 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: UpSampling3D', function () {
+  const assert = chai.assert
+  const styles = testGlobals.styles
+  const logTime = testGlobals.logTime
+  const stringifyCondensed = testGlobals.stringifyCondensed
+  const approxEquals = KerasJS.testUtils.approxEquals
+  const layers = KerasJS.layers
+
+  before(function () {
+    console.log('\n%cconvolutional layer: UpSampling3D', styles.h1)
+  })
+
+  it(`[convolutional.UpSampling3D.0] size 2x2x2 upsampling on 2x2x2x3 input, dimOrdering=tf`, function () {
+    const key = `convolutional.UpSampling3D.0`
+    console.log(`\n%c[${key}] size 2x2x2 upsampling on 2x2x2x3 input, dimOrdering=tf`, styles.h3)
+    let testLayer = new layers.UpSampling3D([2, 2, 2], { dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.UpSampling3D.1] size 2x2x2 upsampling on 2x2x2x3 input, dimOrdering=th`, function () {
+    const key = `convolutional.UpSampling3D.1`
+    console.log(`\n%c[${key}] size 2x2x2 upsampling on 2x2x2x3 input, dimOrdering=th`, styles.h3)
+    let testLayer = new layers.UpSampling3D([2, 2, 2], { dimOrdering: 'th' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.UpSampling3D.2] size 1x3x2 upsampling on 2x1x3x2 input, dimOrdering=tf`, function () {
+    const key = `convolutional.UpSampling3D.2`
+    console.log(`\n%c[${key}] size 1x3x2 upsampling on 2x1x3x2 input, dimOrdering=tf`, styles.h3)
+    let testLayer = new layers.UpSampling3D([1, 3, 2], { dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.UpSampling3D.3] 2x1x2 upsampling on 2x1x3x3 input, dimOrdering=th`, function () {
+    const key = `convolutional.UpSampling3D.3`
+    console.log(`\n%c[${key}] 2x1x2 upsampling on 2x1x3x3 input, dimOrdering=th`, styles.h3)
+    let testLayer = new layers.UpSampling3D([2, 1, 2], { dimOrdering: 'th' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+})
diff --git a/test/convolutional/data_UpSampling1D.js b/test/convolutional/data_UpSampling1D.js
new file mode 100644
index 0000000..dd1c762
--- /dev/null
+++ b/test/convolutional/data_UpSampling1D.js
@@ -0,0 +1,30 @@
+// TEST DATA
+// Keyed by mocha test ID
+// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
+
+(function () {
+  var DATA = {
+    'convolutional.UpSampling1D.0': {
+      input: {
+        data: [0.262, 0.764609, -0.482897, -0.371755, 0.871769, 0.490033, -0.986894, -0.960468, 0.373039, 0.911356, 0.00298, 0.270652, 0.749006, 0.692235, 0.471778],
+        shape: [3, 5]
+      },
+      expected: {
+        data: [0.262, 0.764609, -0.482897, -0.371755, 0.871769, 0.262, 0.764609, -0.482897, -0.371755, 0.871769, 0.490033, -0.986894, -0.960468, 0.373039, 0.911356, 0.490033, -0.986894, -0.960468, 0.373039, 0.911356, 0.00298, 0.270652, 0.749006, 0.692235, 0.471778, 0.00298, 0.270652, 0.749006, 0.692235, 0.471778],
+        shape: [6, 5]
+      }
+    },
+    'convolutional.UpSampling1D.1': {
+      input: {
+        data: [0.562988, 0.168418, -0.14658, -0.369311, 0.653777, 0.806859, -0.922124, 0.830445, -0.878989, -0.638546, -0.855401, -0.082476, 0.416718, -0.03353, -0.949107, -0.866195],
+        shape: [4, 4]
+      },
+      expected: {
+        data: [0.562988, 0.168418, -0.14658, -0.369311, 0.562988, 0.168418, -0.14658, -0.369311, 0.562988, 0.168418, -0.14658, -0.369311, 0.653777, 0.806859, -0.922124, 0.830445, 0.653777, 0.806859, -0.922124, 0.830445, 0.653777, 0.806859, -0.922124, 0.830445, -0.878989, -0.638546, -0.855401, -0.082476, -0.878989, -0.638546, -0.855401, -0.082476, -0.878989, -0.638546, -0.855401, -0.082476, 0.416718, -0.03353, -0.949107, -0.866195, 0.416718, -0.03353, -0.949107, -0.866195, 0.416718, -0.03353, -0.949107, -0.866195],
+        shape: [12, 4]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()
diff --git a/test/convolutional/data_UpSampling2D.js b/test/convolutional/data_UpSampling2D.js
new file mode 100644
index 0000000..76ba040
--- /dev/null
+++ b/test/convolutional/data_UpSampling2D.js
@@ -0,0 +1,50 @@
+// TEST DATA
+// Keyed by mocha test ID
+// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
+
+(function () {
+  var DATA = {
+    'convolutional.UpSampling2D.0': {
+      input: {
+        data: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094],
+        shape: [3, 3, 3]
+      },
+      expected: {
+        data: [-0.570441, -0.454673, -0.285321, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.160547, -0.332203, 0.546391, -0.570441, -0.454673, -0.285321, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, 0.753553, -0.031788, 0.915329, 0.272735, 0.010827, -0.763164, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.162138, 0.565201, -0.492094, -0.738844, 0.269075, 0.434091, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.162138, 0.565201, -0.492094],
+        shape: [6, 6, 3]
+      }
+    },
+    'convolutional.UpSampling2D.1': {
+      input: {
+        data: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094],
+        shape: [3, 3, 3]
+      },
+      expected: {
+        data: [-0.570441, -0.570441, -0.454673, -0.454673, -0.285321, -0.285321, -0.570441, -0.570441, -0.454673, -0.454673, -0.285321, -0.285321, 0.237249, 0.237249, 0.282682, 0.282682, 0.428035, 0.428035, 0.237249, 0.237249, 0.282682, 0.282682, 0.428035, 0.428035, 0.160547, 0.160547, -0.332203, -0.332203, 0.546391, 0.546391, 0.160547, 0.160547, -0.332203, -0.332203, 0.546391, 0.546391, 0.272735, 0.272735, 0.010827, 0.010827, -0.763164, -0.763164, 0.272735, 0.272735, 0.010827, 0.010827, -0.763164, -0.763164, -0.442696, -0.442696, 0.381948, 0.381948, -0.676994, -0.676994, -0.442696, -0.442696, 0.381948, 0.381948, -0.676994, -0.676994, 0.753553, 0.753553, -0.031788, -0.031788, 0.915329, 0.915329, 0.753553, 0.753553, -0.031788, -0.031788, 0.915329, 0.915329, -0.738844, -0.738844, 0.269075, 0.269075, 0.434091, 0.434091, -0.738844, -0.738844, 0.269075, 0.269075, 0.434091, 0.434091, 0.991585, 0.991585, -0.944288, -0.944288, 0.258834, 0.258834, 0.991585, 0.991585, -0.944288, -0.944288, 0.258834, 0.258834, 0.162138, 0.162138, 0.565201, 0.565201, -0.492094, -0.492094, 0.162138, 0.162138, 0.565201, 0.565201, -0.492094, -0.492094],
+        shape: [3, 6, 6]
+      }
+    },
+    'convolutional.UpSampling2D.2': {
+      input: {
+        data: [0.275222, -0.793967, -0.468107, -0.841484, -0.295362, 0.78175, 0.068787, -0.261747, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.99869, -0.963008],
+        shape: [4, 2, 2]
+      },
+      expected: {
+        data: [0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, 0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, 0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008],
+        shape: [12, 4, 2]
+      }
+    },
+    'convolutional.UpSampling2D.3': {
+      input: {
+        data: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111],
+        shape: [4, 3, 2]
+      },
+      expected: {
+        data: [-0.989173, -0.989173, -0.989173, -0.133618, -0.133618, -0.133618, -0.505338, -0.505338, -0.505338, 0.023259, 0.023259, 0.023259, 0.503982, 0.503982, 0.503982, -0.303769, -0.303769, -0.303769, -0.436321, -0.436321, -0.436321, 0.793911, 0.793911, 0.793911, 0.416102, 0.416102, 0.416102, 0.806405, 0.806405, 0.806405, -0.098342, -0.098342, -0.098342, -0.738022, -0.738022, -0.738022, -0.982676, -0.982676, -0.982676, 0.805073, 0.805073, 0.805073, 0.741244, 0.741244, 0.741244, -0.941634, -0.941634, -0.941634, -0.253526, -0.253526, -0.253526, -0.136544, -0.136544, -0.136544, -0.295772, -0.295772, -0.295772, 0.207565, 0.207565, 0.207565, -0.517246, -0.517246, -0.517246, -0.686963, -0.686963, -0.686963, -0.176235, -0.176235, -0.176235, -0.354111, -0.354111, -0.354111],
+        shape: [4, 3, 6]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()
diff --git a/test/convolutional/data_UpSampling3D.js b/test/convolutional/data_UpSampling3D.js
new file mode 100644
index 0000000..fbf50c3
--- /dev/null
+++ b/test/convolutional/data_UpSampling3D.js
@@ -0,0 +1,50 @@
+// TEST DATA
+// Keyed by mocha test ID
+// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
+
+(function () {
+  var DATA = {
+    'convolutional.UpSampling3D.0': {
+      input: {
+        data: [-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],
+        shape: [2, 2, 2, 3]
+      },
+      expected: {
+        data: [-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],
+        shape: [4, 4, 4, 3]
+      }
+    },
+    'convolutional.UpSampling3D.1': {
+      input: {
+        data: [-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],
+        shape: [2, 2, 2, 3]
+      },
+      expected: {
+        data: [-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],
+        shape: [2, 4, 4, 6]
+      }
+    },
+    'convolutional.UpSampling3D.2': {
+      input: {
+        data: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022],
+        shape: [2, 1, 3, 2]
+      },
+      expected: {
+        data: [-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],
+        shape: [2, 3, 6, 2]
+      }
+    },
+    'convolutional.UpSampling3D.3': {
+      input: {
+        data: [-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],
+        shape: [2, 1, 3, 3]
+      },
+      expected: {
+        data: [-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],
+        shape: [2, 2, 3, 6]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()