diff --git a/index.html b/index.html
index 7edcd30..695180f 100644
--- a/index.html
+++ b/index.html
@@ -20,9 +20,11 @@
   <script src="/test/activations/data_activations.js"></script>
   <script src="/test/activations/activations.js"></script>
 
+  <!-- advanced_activations -->
   <script src="/test/advanced_activations/data_advanced_activations.js"></script>
   <script src="/test/advanced_activations/advanced_activations.js"></script>
 
+  <!-- core -->
   <script src="/test/core/data_Dense.js"></script>
   <script src="/test/core/Dense.js"></script>
   <script src="/test/core/data_Activation.js"></script>
@@ -44,6 +46,7 @@
   <script src="/test/core/data_MaxoutDense.js"></script>
   <script src="/test/core/MaxoutDense.js"></script>
 
+  <!-- convolutional -->
   <script src="/test/convolutional/data_Convolution1D.js"></script>
   <script src="/test/convolutional/Convolution1D.js"></script>
   <script src="/test/convolutional/data_Convolution2D.js"></script>
@@ -69,6 +72,12 @@
   <script src="/test/convolutional/data_ZeroPadding3D.js"></script>
   <script src="/test/convolutional/ZeroPadding3D.js"></script>
 
+  <!-- pooling -->
+  <script src="/test/pooling/data_MaxPooling1D.js"></script>
+  <script src="/test/pooling/MaxPooling1D.js"></script>
+  <script src="/test/pooling/data_AveragePooling1D.js"></script>
+  <script src="/test/pooling/AveragePooling1D.js"></script>
+
   <script>
     // mocha.checkLeaks();
     mocha.globals(['jQuery']);
diff --git a/notebooks/pooling/AveragePooling1D.ipynb b/notebooks/pooling/AveragePooling1D.ipynb
new file mode 100644
index 0000000..6b6ec51
--- /dev/null
+++ b/notebooks/pooling/AveragePooling1D.ipynb
@@ -0,0 +1,482 @@
+{
+ "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 AveragePooling1D\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": [
+    "### AveragePooling1D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.AveragePooling1D.0] input 6x6, pool_length=2, stride=None, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, 0.170854, -0.139788, -0.710674, 0.406968, 0.705926, -0.094137, -0.793497, -0.040684, 0.522292]\n",
+      "out shape: (3, 6)\n",
+      "out: [-0.204947, -0.393438, 0.130535, 0.254992, 0.146755, -0.167564, -0.59077, 0.325511, -0.121451, 0.872569, -0.488038, 0.587081, 0.284553, 0.635564, -0.293115, -0.311321, -0.090236, -0.094191]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = AveragePooling1D(pool_length=2, stride=None, border_mode='valid')\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.AveragePooling1D.1] input 6x6, pool_length=2, stride=1, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883, -0.394643, 0.498903, 0.029584, -0.17332, 0.628159, 0.445074]\n",
+      "out shape: (5, 6)\n",
+      "out: [0.172005, -0.527857, -0.54692, -0.442195, 0.282889, 0.81721, 0.512613, 0.228046, -0.812212, -0.502958, 0.437209, 0.336182, 0.894288, 0.166293, -0.761499, -0.151651, -0.445925, 0.172993, 0.832353, -0.150399, -0.142964, 0.306764, -0.820499, 0.651587, 0.218963, 0.291679, 0.133982, -0.109749, -0.053856, 0.610979]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = AveragePooling1D(pool_length=2, stride=1, border_mode='valid')\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.AveragePooling1D.2] input 6x6, pool_length=2, stride=3, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772]\n",
+      "out shape: (2, 6)\n",
+      "out: [-0.712747, 0.330146, -0.044618, 0.414832, 0.20282, -0.520896, -0.579092, -0.381128, -0.158586, -0.198122, -0.107429, -0.029636]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = AveragePooling1D(pool_length=2, stride=3, border_mode='valid')\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.AveragePooling1D.3] input 6x6, pool_length=2, stride=None, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, 0.392553, -0.191718, 0.055121, 0.289836, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, -0.538209, -0.580887, -0.370128, -0.924933, -0.161736, -0.205619, 0.793729, -0.354472]\n",
+      "out shape: (3, 6)\n",
+      "out: [-0.62359, -0.038285, -0.261934, -0.080966, -0.182441, 0.004328, 0.208461, 0.333278, 0.275656, 0.635205, -0.546779, 0.640653, -0.118725, -0.820791, 0.339178, -0.147228, 0.12776, -0.46768]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = AveragePooling1D(pool_length=2, stride=None, border_mode='same')\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": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.AveragePooling1D.4] input 6x6, pool_length=2, stride=1, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\n",
+      "in: [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549]\n",
+      "out shape: (6, 6)\n",
+      "out: [0.267569, -0.152518, -0.580796, 0.325122, 0.238808, -0.020921, -0.205617, -0.660188, -0.507748, 0.382052, 0.476182, -0.015192, -0.077158, -0.788586, -0.312553, 0.453351, -0.085005, 0.660344, 0.3829, -0.377788, -0.286068, 0.620436, -0.282143, 0.014686, -0.146838, 0.380289, -0.520696, 0.643502, -0.00363, -0.142979, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = AveragePooling1D(pool_length=2, stride=1, border_mode='same')\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(254)\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.AveragePooling1D.5] input 6x6, pool_length=2, stride=3, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\n",
+      "in: [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309]\n",
+      "out shape: (2, 6)\n",
+      "out: [-0.148732, -0.597122, -0.166153, -0.787408, -0.120339, -0.098928, -0.032609, -0.010347, -0.060213, 0.549367, -0.022065, -0.519997]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = AveragePooling1D(pool_length=2, stride=3, border_mode='same')\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(255)\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.AveragePooling1D.6] input 6x6, pool_length=3, stride=None, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\n",
+      "in: [-0.908432, 0.172241, -0.59352, -0.831514, -0.948016, -0.194126, -0.242576, -0.89432, 0.610714, -0.24071, -0.245859, 0.500851, 0.088791, 0.04635, 0.908568, -0.232197, -0.175815, -0.177919, -0.535898, 0.04802, 0.512585, 0.854168, 0.283045, 0.282488, -0.126263, 0.772568, 0.403228, 0.721107, -0.043311, -0.799013, -0.683105, -0.52703, 0.838417, 0.915738, 0.180207, -0.181716]\n",
+      "out shape: (2, 6)\n",
+      "out: [-0.354073, -0.225243, 0.308587, -0.434807, -0.456563, 0.042935, -0.448422, 0.097853, 0.584743, 0.830338, 0.139981, -0.232747]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = AveragePooling1D(pool_length=3, stride=None, border_mode='valid')\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(256)\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.AveragePooling1D.7] input 7x7, pool_length=3, stride=1, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (7, 7)\n",
+      "in: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, -0.351388, -0.501557, 0.017192, -0.026869, -0.768317, -0.476893, -0.895809, 0.764782, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.425255, -0.000952, -0.060338, -0.095258, 0.173776, -0.645557, 0.196502, 0.27885, -0.6868, -0.551196, 0.726361, -0.382779, 0.61877, 0.023847, -0.451251, 0.065412, -0.708225, -0.815011, -0.926643, 0.323493, -0.063352, 0.16365, -0.030438, -0.054635, 0.193949, -0.574495, 0.022988, 0.36335]\n",
+      "out shape: (7, 7)\n",
+      "out: [0.179048, 0.315252, 0.118001, -0.141866, 0.020584, -0.807329, 0.206697, 0.406718, 0.217249, 0.080014, 0.1589, 0.254063, -0.406451, 0.447915, 0.261919, 0.012494, -0.027723, -0.034381, 0.139301, -0.38202, 0.630545, 0.522054, -0.22217, -0.202498, 0.463845, 0.170672, 0.122839, 0.383566, 0.084285, -0.207447, -0.43992, -0.061303, -0.378549, 0.098902, 0.052332, -0.002917, -0.217275, -0.438018, 0.0351, -0.627973, 0.32175, 0.107948, -0.143801, 0.017487, -0.38143, -0.310531, -0.750569, 0.173241, 0.149999]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (7, 7)\n",
+    "L = AveragePooling1D(pool_length=3, stride=1, border_mode='same')\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(257)\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.AveragePooling1D.8] input 7x7, pool_length=3, stride=3, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (7, 7)\n",
+      "in: [-0.830746, 0.315868, -0.173506, 0.415541, -0.957882, 0.658995, 0.795264, -0.147083, -0.042061, 0.230065, 0.388847, -0.277524, -0.268423, 0.35691, -0.515291, -0.37555, 0.367489, 0.753251, -0.60764, -0.16741, -0.893275, -0.814508, -0.437352, 0.062193, -0.003077, 0.560767, -0.646034, -0.283879, 0.097661, 0.401756, -0.236235, -0.199824, -0.252007, -0.335503, 0.414988, 0.301686, 0.309765, -0.349835, -0.274081, 0.383308, -0.782973, -0.667924, 0.282556, -0.932491, 0.954125, 0.837689, 0.219229, -0.583405, -0.018424]\n",
+      "out shape: (3, 7)\n",
+      "out: [-0.488914, 0.136903, 0.02828, 0.402194, -0.617703, 0.195286, 0.576087, -0.410713, -0.137048, 0.064482, 0.18345, -0.099627, -0.382982, -0.254055, 0.292121, -0.311363, 0.302145, 0.281804, 0.301269, -0.683189, -0.343174]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (7, 7)\n",
+    "L = AveragePooling1D(pool_length=3, stride=3, border_mode='same')\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(258)\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": {
+  "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/pooling/MaxPooling1D.ipynb b/notebooks/pooling/MaxPooling1D.ipynb
new file mode 100644
index 0000000..dcbdd10
--- /dev/null
+++ b/notebooks/pooling/MaxPooling1D.ipynb
@@ -0,0 +1,481 @@
+{
+ "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 MaxPooling1D\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": [
+    "### MaxPooling1D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.MaxPooling1D.0] input 6x6, pool_length=2, stride=None, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, 0.170854, -0.139788, -0.710674, 0.406968, 0.705926, -0.094137, -0.793497, -0.040684, 0.522292]\n",
+      "out shape: (3, 6)\n",
+      "out: [0.160547, -0.332203, 0.546391, 0.272735, 0.282682, 0.428035, -0.442696, 0.381948, 0.434091, 0.991585, -0.031788, 0.915329, 0.406968, 0.705926, -0.094137, 0.170854, -0.040684, 0.522292]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = MaxPooling1D(pool_length=2, stride=None, border_mode='valid')\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.MaxPooling1D.1] input 6x6, pool_length=2, stride=1, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883, -0.394643, 0.498903, 0.029584, -0.17332, 0.628159, 0.445074]\n",
+      "out shape: (5, 6)\n",
+      "out: [0.275222, -0.261747, -0.468107, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.524308, 0.659706, 0.013277, 0.526292, 0.832569, 0.084455, 0.23838, 0.659706, -0.735871, 0.776883, 0.832569, 0.498903, 0.23838, -0.046178, 0.628159, 0.776883]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = MaxPooling1D(pool_length=2, stride=1, border_mode='valid')\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.MaxPooling1D.2] input 6x6, pool_length=2, stride=3, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772]\n",
+      "out shape: (2, 6)\n",
+      "out: [-0.436321, 0.793911, 0.416102, 0.806405, 0.503982, -0.303769, -0.295772, 0.207565, 0.200074, 0.290718, -0.038623, 0.294839]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = MaxPooling1D(pool_length=2, stride=3, border_mode='valid')\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.MaxPooling1D.3] input 6x6, pool_length=2, stride=None, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\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, 0.392553, -0.191718, 0.055121, 0.289836, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, -0.538209, -0.580887, -0.370128, -0.924933, -0.161736, -0.205619, 0.793729, -0.354472]\n",
+      "out shape: (3, 6)\n",
+      "out: [-0.47588, 0.366985, 0.040173, 0.015578, 0.541277, 0.241982, 0.392553, 0.858275, 0.496191, 0.980574, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, 0.793729, -0.354472]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = MaxPooling1D(pool_length=2, stride=None, border_mode='same')\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": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.MaxPooling1D.4] input 6x6, pool_length=2, stride=1, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\n",
+      "in: [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549]\n",
+      "out shape: (6, 6)\n",
+      "out: [0.511014, 0.280236, -0.481578, 0.692702, 0.64189, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, 0.369868, 0.767931, -0.735105, -0.091189, 0.835301, 0.310474, 0.950819, 0.767931, 0.086491, -0.091189, 0.835301, -0.083803, 0.950819, -0.002131, 0.674087, -0.480947, 0.881432, 0.076544, 0.63549, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = MaxPooling1D(pool_length=2, stride=1, border_mode='same')\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(254)\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.MaxPooling1D.5] input 6x6, pool_length=2, stride=3, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\n",
+      "in: [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309]\n",
+      "out shape: (2, 6)\n",
+      "out: [-0.072127, -0.553929, 0.023246, -0.638412, 0.556627, 0.284959, 0.461494, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = MaxPooling1D(pool_length=2, stride=3, border_mode='same')\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(255)\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.MaxPooling1D.6] input 6x6, pool_length=3, stride=None, border_mode='valid'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 6)\n",
+      "in: [-0.908432, 0.172241, -0.59352, -0.831514, -0.948016, -0.194126, -0.242576, -0.89432, 0.610714, -0.24071, -0.245859, 0.500851, 0.088791, 0.04635, 0.908568, -0.232197, -0.175815, -0.177919, -0.535898, 0.04802, 0.512585, 0.854168, 0.283045, 0.282488, -0.126263, 0.772568, 0.403228, 0.721107, -0.043311, -0.799013, -0.683105, -0.52703, 0.838417, 0.915738, 0.180207, -0.181716]\n",
+      "out shape: (2, 6)\n",
+      "out: [0.088791, 0.172241, 0.908568, -0.232197, -0.175815, 0.500851, -0.126263, 0.772568, 0.838417, 0.915738, 0.283045, 0.282488]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 6)\n",
+    "L = MaxPooling1D(pool_length=3, stride=None, border_mode='valid')\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(256)\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.MaxPooling1D.7] input 7x7, pool_length=3, stride=1, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (7, 7)\n",
+      "in: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, -0.351388, -0.501557, 0.017192, -0.026869, -0.768317, -0.476893, -0.895809, 0.764782, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.425255, -0.000952, -0.060338, -0.095258, 0.173776, -0.645557, 0.196502, 0.27885, -0.6868, -0.551196, 0.726361, -0.382779, 0.61877, 0.023847, -0.451251, 0.065412, -0.708225, -0.815011, -0.926643, 0.323493, -0.063352, 0.16365, -0.030438, -0.054635, 0.193949, -0.574495, 0.022988, 0.36335]\n",
+      "out shape: (7, 7)\n",
+      "out: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, 0.764782, 0.862057, 0.613312, 0.262871, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.61877, 0.930351, 0.425255, 0.065412, -0.060338, 0.726361, 0.173776, 0.61877, 0.196502, 0.27885, 0.065412, -0.054635, 0.726361, -0.382779, 0.61877, 0.36335, 0.16365, 0.065412, -0.054635, 0.193949, -0.574495, 0.323493, 0.36335]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (7, 7)\n",
+    "L = MaxPooling1D(pool_length=3, stride=1, border_mode='same')\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(257)\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.MaxPooling1D.8] input 7x7, pool_length=3, stride=3, border_mode='same'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (7, 7)\n",
+      "in: [-0.830746, 0.315868, -0.173506, 0.415541, -0.957882, 0.658995, 0.795264, -0.147083, -0.042061, 0.230065, 0.388847, -0.277524, -0.268423, 0.35691, -0.515291, -0.37555, 0.367489, 0.753251, -0.60764, -0.16741, -0.893275, -0.814508, -0.437352, 0.062193, -0.003077, 0.560767, -0.646034, -0.283879, 0.097661, 0.401756, -0.236235, -0.199824, -0.252007, -0.335503, 0.414988, 0.301686, 0.309765, -0.349835, -0.274081, 0.383308, -0.782973, -0.667924, 0.282556, -0.932491, 0.954125, 0.837689, 0.219229, -0.583405, -0.018424]\n",
+      "out shape: (3, 7)\n",
+      "out: [-0.147083, 0.315868, 0.230065, 0.415541, -0.277524, 0.658995, 0.795264, 0.097661, 0.401756, 0.367489, 0.753251, 0.560767, -0.16741, 0.414988, 0.301686, 0.309765, 0.954125, 0.837689, 0.383308, -0.583405, -0.018424]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (7, 7)\n",
+    "L = MaxPooling1D(pool_length=3, stride=3, border_mode='same')\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(258)\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/AtrousConvolution2D.js b/src/layers/convolutional/AtrousConvolution2D.js
index 8554399..4d7d9e8 100644
--- a/src/layers/convolutional/AtrousConvolution2D.js
+++ b/src/layers/convolutional/AtrousConvolution2D.js
@@ -87,8 +87,8 @@ export default class AtrousConvolution2D extends Convolution2D {
 
     let patch = new Tensor([], [patchLen])
     let n = 0
-    for (let i = 0; i <= inputRows - nbRowDilated; i += this.subsample[0]) {
-      for (let j = 0; j <= inputCols - nbColDilated; j += this.subsample[1]) {
+    for (let i = 0, limit = inputRows - nbRowDilated; i <= limit; i += this.subsample[0]) {
+      for (let j = 0, limit = inputCols - nbColDilated; j <= limit; j += this.subsample[1]) {
         const patchData = flattenDeep(unpack(
           x.tensor
             .hi(i + nbRowDilated, j + nbColDilated, inputChannels)
diff --git a/src/layers/convolutional/Convolution2D.js b/src/layers/convolutional/Convolution2D.js
index 317efe0..ff8c773 100644
--- a/src/layers/convolutional/Convolution2D.js
+++ b/src/layers/convolutional/Convolution2D.js
@@ -143,8 +143,8 @@ export default class Convolution2D extends Layer {
 
     let patch = new Tensor([], [patchLen])
     let n = 0
-    for (let i = 0; i <= inputRows - nbRow; i += this.subsample[0]) {
-      for (let j = 0; j <= inputCols - nbCol; j += this.subsample[1]) {
+    for (let i = 0, limit = inputRows - nbRow; i <= limit; i += this.subsample[0]) {
+      for (let j = 0, limit = inputCols - nbCol; j <= limit; j += this.subsample[1]) {
         const patchData = flattenDeep(unpack(
           x.tensor.hi(i + nbRow, j + nbCol, inputChannels).lo(i, j, 0)
         ))
diff --git a/src/layers/convolutional/Convolution3D.js b/src/layers/convolutional/Convolution3D.js
index 464ff90..cc63a60 100644
--- a/src/layers/convolutional/Convolution3D.js
+++ b/src/layers/convolutional/Convolution3D.js
@@ -159,9 +159,9 @@ export default class Convolution3D extends Layer {
 
     let patch = new Tensor([], [patchLen])
     let n = 0
-    for (let i = 0; i <= inputDim1 - kernelDim1; i += this.subsample[0]) {
-      for (let j = 0; j <= inputDim2 - kernelDim2; j += this.subsample[1]) {
-        for (let k = 0; k <= inputDim3 - kernelDim3; k += this.subsample[2]) {
+    for (let i = 0, limit = inputDim1 - kernelDim1; i <= limit; i += this.subsample[0]) {
+      for (let j = 0, limit = inputDim2 - kernelDim2; j <= limit; j += this.subsample[1]) {
+        for (let k = 0, limit = inputDim3 - kernelDim3; k <= limit; k += this.subsample[2]) {
           const patchData = flattenDeep(unpack(
             x.tensor.hi(i + kernelDim1, j + kernelDim2, k + kernelDim3, inputChannels).lo(i, j, k, 0)
           ))
diff --git a/src/layers/index.js b/src/layers/index.js
index 1f08fb7..39c4838 100644
--- a/src/layers/index.js
+++ b/src/layers/index.js
@@ -1,3 +1,4 @@
 export * from './advanced_activations'
 export * from './core'
 export * from './convolutional'
+export * from './pooling'
diff --git a/src/layers/pooling/AveragePooling1D.js b/src/layers/pooling/AveragePooling1D.js
new file mode 100644
index 0000000..e26fc11
--- /dev/null
+++ b/src/layers/pooling/AveragePooling1D.js
@@ -0,0 +1,14 @@
+import _Pooling1D from './_Pooling1D'
+
+/**
+ * AveragePooling1D layer class, extends abstract _Pooling1D class
+ */
+export default class AveragePooling1D extends _Pooling1D {
+  /**
+   * Creates a AveragePooling1D activation layer
+   */
+  constructor (attrs = {}) {
+    super(attrs)
+    this.poolingFunc = 'average'
+  }
+}
diff --git a/src/layers/pooling/MaxPooling1D.js b/src/layers/pooling/MaxPooling1D.js
new file mode 100644
index 0000000..0a9f3cd
--- /dev/null
+++ b/src/layers/pooling/MaxPooling1D.js
@@ -0,0 +1,14 @@
+import _Pooling1D from './_Pooling1D'
+
+/**
+ * MaxPooling1D layer class, extends abstract _Pooling1D class
+ */
+export default class MaxPooling1D extends _Pooling1D {
+  /**
+   * Creates a MaxPooling1D activation layer
+   */
+  constructor (attrs = {}) {
+    super(attrs)
+    this.poolingFunc = 'max'
+  }
+}
diff --git a/src/layers/pooling/_Pooling1D.js b/src/layers/pooling/_Pooling1D.js
new file mode 100644
index 0000000..62a3e0b
--- /dev/null
+++ b/src/layers/pooling/_Pooling1D.js
@@ -0,0 +1,80 @@
+import Layer from '../../engine/Layer'
+import Tensor from '../../Tensor'
+import ops from 'ndarray-ops'
+
+/**
+ * _Pooling1D layer class
+ */
+export default class _Pooling1D extends Layer {
+  /**
+   * Creates a _Pooling1D activation layer
+   */
+  constructor (attrs = {}) {
+    super(attrs)
+    const {
+      poolLength = 2,
+      stride = null,
+      borderMode = 'valid'
+    } = attrs
+
+    this.poolLength = poolLength
+    this.stride = stride === null ? poolLength : stride
+    this.borderMode = borderMode
+
+    // default pooling function
+    // can be `max` or `average`
+    this.poolingFunc = 'max'
+  }
+
+  /**
+   * Method for layer computational logic
+   * @param {Tensor} x
+   * @returns {Tensor} x
+   */
+  call (x) {
+    if (this.poolingFunc !== 'max' && this.poolingFunc !== 'average') {
+      throw new Error(`[pooling._Pooling1D] pooling function must be max or average.`)
+    }
+
+    const stepsNew = this.borderMode === 'valid'
+      ? Math.floor((x.tensor.shape[0] - this.poolLength + this.stride) / this.stride)
+      : Math.floor((x.tensor.shape[0] + this.stride - 1) / this.stride)
+
+    let y = new Tensor([], [stepsNew, x.tensor.shape[1]])
+    let yStep = new Tensor([], [x.tensor.shape[1]])
+
+    // in borderMode same, start negative from beyond step 0
+    let step = this.borderMode === 'valid'
+      ? 0
+      : Math.min(0, Math.ceil((x.tensor.shape[0] - (stepsNew - 1) * this.stride - this.poolLength) / 2))
+
+    for (let i = 0; i < stepsNew; i++) {
+      let _step = Math.max(0, step)
+      let limit = this.poolLength + Math.min(0, step)
+      ops.assign(yStep.tensor, x.tensor.pick(_step, null))
+
+      let count = 1
+      for (let j = 1; j < limit; j++) {
+        if ((_step + j) > (x.tensor.shape[0] - 1)) {
+          break
+        }
+        if (this.poolingFunc === 'max') {
+          ops.maxeq(yStep.tensor, x.tensor.pick(_step + j, null))
+        } else if (this.poolingFunc === 'average') {
+          ops.addeq(yStep.tensor, x.tensor.pick(_step + j, null))
+        }
+        count += 1
+      }
+
+      if (this.poolingFunc === 'average') {
+        ops.divseq(yStep.tensor, count)
+      }
+
+      ops.assign(y.tensor.pick(i, null), yStep.tensor)
+      step += this.stride
+    }
+
+    x.tensor = y.tensor
+    return x
+  }
+}
diff --git a/src/layers/pooling/index.js b/src/layers/pooling/index.js
new file mode 100644
index 0000000..0c0afc7
--- /dev/null
+++ b/src/layers/pooling/index.js
@@ -0,0 +1,7 @@
+import MaxPooling1D from './MaxPooling1D'
+import AveragePooling1D from './AveragePooling1D'
+
+export {
+  MaxPooling1D,
+  AveragePooling1D
+}
diff --git a/test/pooling/AveragePooling1D.js b/test/pooling/AveragePooling1D.js
new file mode 100644
index 0000000..7575a41
--- /dev/null
+++ b/test/pooling/AveragePooling1D.js
@@ -0,0 +1,75 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: AveragePooling1D', 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
+
+  const testParams = [
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: null, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 1, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 3, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: null, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 1, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 3, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 3, stride: null, borderMode: 'valid' }
+    },
+    {
+      inputShape: [7, 7],
+      attrs: { poolLength: 3, stride: 1, borderMode: 'same' }
+    },
+    {
+      inputShape: [7, 7],
+      attrs: { poolLength: 3, stride: 3, borderMode: 'same' }
+    }
+  ]
+
+  before(function () {
+    console.log('\n%cconvolutional layer: AveragePooling1D', styles.h1)
+  })
+
+  testParams.forEach(({ inputShape, attrs }, i) => {
+    const key = `convolutional.AveragePooling1D.${i}`
+    const [inputLength, inputFeatures] = inputShape
+    const title = `[${key}] test: ${inputLength}x${inputFeatures} input, poolLength='${attrs.poolLength}', stride=${attrs.stride}, borderMode=${attrs.borderMode}`
+
+    it(title, function () {
+      console.log(`\n%c${title}`, styles.h3)
+      let testLayer = new layers.AveragePooling1D(attrs)
+      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/pooling/MaxPooling1D.js b/test/pooling/MaxPooling1D.js
new file mode 100644
index 0000000..42ef300
--- /dev/null
+++ b/test/pooling/MaxPooling1D.js
@@ -0,0 +1,75 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: MaxPooling1D', 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
+
+  const testParams = [
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: null, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 1, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 3, borderMode: 'valid' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: null, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 1, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 2, stride: 3, borderMode: 'same' }
+    },
+    {
+      inputShape: [6, 6],
+      attrs: { poolLength: 3, stride: null, borderMode: 'valid' }
+    },
+    {
+      inputShape: [7, 7],
+      attrs: { poolLength: 3, stride: 1, borderMode: 'same' }
+    },
+    {
+      inputShape: [7, 7],
+      attrs: { poolLength: 3, stride: 3, borderMode: 'same' }
+    }
+  ]
+
+  before(function () {
+    console.log('\n%cconvolutional layer: MaxPooling1D', styles.h1)
+  })
+
+  testParams.forEach(({ inputShape, attrs }, i) => {
+    const key = `convolutional.MaxPooling1D.${i}`
+    const [inputLength, inputFeatures] = inputShape
+    const title = `[${key}] test: ${inputLength}x${inputFeatures} input, poolLength='${attrs.poolLength}', stride=${attrs.stride}, borderMode=${attrs.borderMode}`
+
+    it(title, function () {
+      console.log(`\n%c${title}`, styles.h3)
+      let testLayer = new layers.MaxPooling1D(attrs)
+      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/pooling/data_AveragePooling1D.js b/test/pooling/data_AveragePooling1D.js
new file mode 100644
index 0000000..2a977a2
--- /dev/null
+++ b/test/pooling/data_AveragePooling1D.js
@@ -0,0 +1,100 @@
+// 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.AveragePooling1D.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, 0.170854, -0.139788, -0.710674, 0.406968, 0.705926, -0.094137, -0.793497, -0.040684, 0.522292],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.204947, -0.393438, 0.130535, 0.254992, 0.146755, -0.167564, -0.59077, 0.325511, -0.121451, 0.872569, -0.488038, 0.587081, 0.284553, 0.635564, -0.293115, -0.311321, -0.090236, -0.094191],
+        shape: [3, 6]
+      }
+    },
+    'convolutional.AveragePooling1D.1': {
+      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, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883, -0.394643, 0.498903, 0.029584, -0.17332, 0.628159, 0.445074],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [0.172005, -0.527857, -0.54692, -0.442195, 0.282889, 0.81721, 0.512613, 0.228046, -0.812212, -0.502958, 0.437209, 0.336182, 0.894288, 0.166293, -0.761499, -0.151651, -0.445925, 0.172993, 0.832353, -0.150399, -0.142964, 0.306764, -0.820499, 0.651587, 0.218963, 0.291679, 0.133982, -0.109749, -0.053856, 0.610979],
+        shape: [5, 6]
+      }
+    },
+    'convolutional.AveragePooling1D.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, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.712747, 0.330146, -0.044618, 0.414832, 0.20282, -0.520896, -0.579092, -0.381128, -0.158586, -0.198122, -0.107429, -0.029636],
+        shape: [2, 6]
+      }
+    },
+    'convolutional.AveragePooling1D.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, 0.392553, -0.191718, 0.055121, 0.289836, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, -0.538209, -0.580887, -0.370128, -0.924933, -0.161736, -0.205619, 0.793729, -0.354472],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.62359, -0.038285, -0.261934, -0.080966, -0.182441, 0.004328, 0.208461, 0.333278, 0.275656, 0.635205, -0.546779, 0.640653, -0.118725, -0.820791, 0.339178, -0.147228, 0.12776, -0.46768],
+        shape: [3, 6]
+      }
+    },
+    'convolutional.AveragePooling1D.4': {
+      input: {
+        data: [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [0.267569, -0.152518, -0.580796, 0.325122, 0.238808, -0.020921, -0.205617, -0.660188, -0.507748, 0.382052, 0.476182, -0.015192, -0.077158, -0.788586, -0.312553, 0.453351, -0.085005, 0.660344, 0.3829, -0.377788, -0.286068, 0.620436, -0.282143, 0.014686, -0.146838, 0.380289, -0.520696, 0.643502, -0.00363, -0.142979, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549],
+        shape: [6, 6]
+      }
+    },
+    'convolutional.AveragePooling1D.5': {
+      input: {
+        data: [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.148732, -0.597122, -0.166153, -0.787408, -0.120339, -0.098928, -0.032609, -0.010347, -0.060213, 0.549367, -0.022065, -0.519997],
+        shape: [2, 6]
+      }
+    },
+    'convolutional.AveragePooling1D.6': {
+      input: {
+        data: [-0.908432, 0.172241, -0.59352, -0.831514, -0.948016, -0.194126, -0.242576, -0.89432, 0.610714, -0.24071, -0.245859, 0.500851, 0.088791, 0.04635, 0.908568, -0.232197, -0.175815, -0.177919, -0.535898, 0.04802, 0.512585, 0.854168, 0.283045, 0.282488, -0.126263, 0.772568, 0.403228, 0.721107, -0.043311, -0.799013, -0.683105, -0.52703, 0.838417, 0.915738, 0.180207, -0.181716],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.354073, -0.225243, 0.308587, -0.434807, -0.456563, 0.042935, -0.448422, 0.097853, 0.584743, 0.830338, 0.139981, -0.232747],
+        shape: [2, 6]
+      }
+    },
+    'convolutional.AveragePooling1D.7': {
+      input: {
+        data: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, -0.351388, -0.501557, 0.017192, -0.026869, -0.768317, -0.476893, -0.895809, 0.764782, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.425255, -0.000952, -0.060338, -0.095258, 0.173776, -0.645557, 0.196502, 0.27885, -0.6868, -0.551196, 0.726361, -0.382779, 0.61877, 0.023847, -0.451251, 0.065412, -0.708225, -0.815011, -0.926643, 0.323493, -0.063352, 0.16365, -0.030438, -0.054635, 0.193949, -0.574495, 0.022988, 0.36335],
+        shape: [7, 7]
+      },
+      expected: {
+        data: [0.179048, 0.315252, 0.118001, -0.141866, 0.020584, -0.807329, 0.206697, 0.406718, 0.217249, 0.080014, 0.1589, 0.254063, -0.406451, 0.447915, 0.261919, 0.012494, -0.027723, -0.034381, 0.139301, -0.38202, 0.630545, 0.522054, -0.22217, -0.202498, 0.463845, 0.170672, 0.122839, 0.383566, 0.084285, -0.207447, -0.43992, -0.061303, -0.378549, 0.098902, 0.052332, -0.002917, -0.217275, -0.438018, 0.0351, -0.627973, 0.32175, 0.107948, -0.143801, 0.017487, -0.38143, -0.310531, -0.750569, 0.173241, 0.149999],
+        shape: [7, 7]
+      }
+    },
+    'convolutional.AveragePooling1D.8': {
+      input: {
+        data: [-0.830746, 0.315868, -0.173506, 0.415541, -0.957882, 0.658995, 0.795264, -0.147083, -0.042061, 0.230065, 0.388847, -0.277524, -0.268423, 0.35691, -0.515291, -0.37555, 0.367489, 0.753251, -0.60764, -0.16741, -0.893275, -0.814508, -0.437352, 0.062193, -0.003077, 0.560767, -0.646034, -0.283879, 0.097661, 0.401756, -0.236235, -0.199824, -0.252007, -0.335503, 0.414988, 0.301686, 0.309765, -0.349835, -0.274081, 0.383308, -0.782973, -0.667924, 0.282556, -0.932491, 0.954125, 0.837689, 0.219229, -0.583405, -0.018424],
+        shape: [7, 7]
+      },
+      expected: {
+        data: [-0.488914, 0.136903, 0.02828, 0.402194, -0.617703, 0.195286, 0.576087, -0.410713, -0.137048, 0.064482, 0.18345, -0.099627, -0.382982, -0.254055, 0.292121, -0.311363, 0.302145, 0.281804, 0.301269, -0.683189, -0.343174],
+        shape: [3, 7]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()
diff --git a/test/pooling/data_MaxPooling1D.js b/test/pooling/data_MaxPooling1D.js
new file mode 100644
index 0000000..bab7516
--- /dev/null
+++ b/test/pooling/data_MaxPooling1D.js
@@ -0,0 +1,100 @@
+// 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.MaxPooling1D.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, 0.170854, -0.139788, -0.710674, 0.406968, 0.705926, -0.094137, -0.793497, -0.040684, 0.522292],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [0.160547, -0.332203, 0.546391, 0.272735, 0.282682, 0.428035, -0.442696, 0.381948, 0.434091, 0.991585, -0.031788, 0.915329, 0.406968, 0.705926, -0.094137, 0.170854, -0.040684, 0.522292],
+        shape: [3, 6]
+      }
+    },
+    'convolutional.MaxPooling1D.1': {
+      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, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883, -0.394643, 0.498903, 0.029584, -0.17332, 0.628159, 0.445074],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [0.275222, -0.261747, -0.468107, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.524308, 0.659706, 0.013277, 0.526292, 0.832569, 0.084455, 0.23838, 0.659706, -0.735871, 0.776883, 0.832569, 0.498903, 0.23838, -0.046178, 0.628159, 0.776883],
+        shape: [5, 6]
+      }
+    },
+    'convolutional.MaxPooling1D.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, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.436321, 0.793911, 0.416102, 0.806405, 0.503982, -0.303769, -0.295772, 0.207565, 0.200074, 0.290718, -0.038623, 0.294839],
+        shape: [2, 6]
+      }
+    },
+    'convolutional.MaxPooling1D.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, 0.392553, -0.191718, 0.055121, 0.289836, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, -0.538209, -0.580887, -0.370128, -0.924933, -0.161736, -0.205619, 0.793729, -0.354472],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.47588, 0.366985, 0.040173, 0.015578, 0.541277, 0.241982, 0.392553, 0.858275, 0.496191, 0.980574, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, 0.793729, -0.354472],
+        shape: [3, 6]
+      }
+    },
+    'convolutional.MaxPooling1D.4': {
+      input: {
+        data: [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [0.511014, 0.280236, -0.481578, 0.692702, 0.64189, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, 0.369868, 0.767931, -0.735105, -0.091189, 0.835301, 0.310474, 0.950819, 0.767931, 0.086491, -0.091189, 0.835301, -0.083803, 0.950819, -0.002131, 0.674087, -0.480947, 0.881432, 0.076544, 0.63549, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549],
+        shape: [6, 6]
+      }
+    },
+    'convolutional.MaxPooling1D.5': {
+      input: {
+        data: [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [-0.072127, -0.553929, 0.023246, -0.638412, 0.556627, 0.284959, 0.461494, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385],
+        shape: [2, 6]
+      }
+    },
+    'convolutional.MaxPooling1D.6': {
+      input: {
+        data: [-0.908432, 0.172241, -0.59352, -0.831514, -0.948016, -0.194126, -0.242576, -0.89432, 0.610714, -0.24071, -0.245859, 0.500851, 0.088791, 0.04635, 0.908568, -0.232197, -0.175815, -0.177919, -0.535898, 0.04802, 0.512585, 0.854168, 0.283045, 0.282488, -0.126263, 0.772568, 0.403228, 0.721107, -0.043311, -0.799013, -0.683105, -0.52703, 0.838417, 0.915738, 0.180207, -0.181716],
+        shape: [6, 6]
+      },
+      expected: {
+        data: [0.088791, 0.172241, 0.908568, -0.232197, -0.175815, 0.500851, -0.126263, 0.772568, 0.838417, 0.915738, 0.283045, 0.282488],
+        shape: [2, 6]
+      }
+    },
+    'convolutional.MaxPooling1D.7': {
+      input: {
+        data: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, -0.351388, -0.501557, 0.017192, -0.026869, -0.768317, -0.476893, -0.895809, 0.764782, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.425255, -0.000952, -0.060338, -0.095258, 0.173776, -0.645557, 0.196502, 0.27885, -0.6868, -0.551196, 0.726361, -0.382779, 0.61877, 0.023847, -0.451251, 0.065412, -0.708225, -0.815011, -0.926643, 0.323493, -0.063352, 0.16365, -0.030438, -0.054635, 0.193949, -0.574495, 0.022988, 0.36335],
+        shape: [7, 7]
+      },
+      expected: {
+        data: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, 0.764782, 0.862057, 0.613312, 0.262871, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.61877, 0.930351, 0.425255, 0.065412, -0.060338, 0.726361, 0.173776, 0.61877, 0.196502, 0.27885, 0.065412, -0.054635, 0.726361, -0.382779, 0.61877, 0.36335, 0.16365, 0.065412, -0.054635, 0.193949, -0.574495, 0.323493, 0.36335],
+        shape: [7, 7]
+      }
+    },
+    'convolutional.MaxPooling1D.8': {
+      input: {
+        data: [-0.830746, 0.315868, -0.173506, 0.415541, -0.957882, 0.658995, 0.795264, -0.147083, -0.042061, 0.230065, 0.388847, -0.277524, -0.268423, 0.35691, -0.515291, -0.37555, 0.367489, 0.753251, -0.60764, -0.16741, -0.893275, -0.814508, -0.437352, 0.062193, -0.003077, 0.560767, -0.646034, -0.283879, 0.097661, 0.401756, -0.236235, -0.199824, -0.252007, -0.335503, 0.414988, 0.301686, 0.309765, -0.349835, -0.274081, 0.383308, -0.782973, -0.667924, 0.282556, -0.932491, 0.954125, 0.837689, 0.219229, -0.583405, -0.018424],
+        shape: [7, 7]
+      },
+      expected: {
+        data: [-0.147083, 0.315868, 0.230065, 0.415541, -0.277524, 0.658995, 0.795264, 0.097661, 0.401756, 0.367489, 0.753251, 0.560767, -0.16741, 0.414988, 0.301686, 0.309765, 0.954125, 0.837689, 0.383308, -0.583405, -0.018424],
+        shape: [3, 7]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()