diff --git a/notebooks/convolutional/Cropping1D.ipynb b/notebooks/convolutional/Cropping1D.ipynb
new file mode 100644
index 0000000..bfec772
--- /dev/null
+++ b/notebooks/convolutional/Cropping1D.ipynb
@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:17:41.918185",
+     "start_time": "2016-11-15T10:17:41.915648"
+    },
+    "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 Cropping1D\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:17:42.093433",
+     "start_time": "2016-11-15T10:17:42.091041"
+    },
+    "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": [
+    "### Cropping1D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.Cropping1D.0] cropping (1,1) on 6x4 input**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:20:10.543929",
+     "start_time": "2016-11-15T10:20:10.526160"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 4)\n",
+      "in: [-0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108, -0.1961, -0.031679, -0.188902, 0.846634]\n",
+      "out shape: (4, 4)\n",
+      "out: [-0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 4)\n",
+    "L = Cropping1D(cropping=(1,1))\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(240)\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.Cropping1D.1] cropping (2,3) on 6x4 input**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:19:26.401100",
+     "start_time": "2016-11-15T10:19:26.382981"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (6, 4)\n",
+      "in: [0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066, 0.532874, 0.866766, -0.544852, 0.876693, -0.523052, -0.716556, 0.875262, 0.544879]\n",
+      "out shape: (1, 4)\n",
+      "out: [-0.484373, -0.506163, 0.353904, 0.513668]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (6, 4)\n",
+    "L = Cropping1D(cropping=(2,3))\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(241)\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 (with sys packages)",
+   "language": "python",
+   "name": "py3syspck"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/convolutional/Cropping2D.ipynb b/notebooks/convolutional/Cropping2D.ipynb
new file mode 100644
index 0000000..de98279
--- /dev/null
+++ b/notebooks/convolutional/Cropping2D.ipynb
@@ -0,0 +1,289 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:05:43.575560",
+     "start_time": "2016-11-15T10:05:42.736839"
+    },
+    "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 Cropping2D\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:05:43.681603",
+     "start_time": "2016-11-15T10:05:43.678284"
+    },
+    "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": [
+    "### Cropping2D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.Cropping2D.0] cropping (1,1),(1, 1) on 3x5x4 input, dim_ordering=tf**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:06:18.618446",
+     "start_time": "2016-11-15T10:06:18.599690"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (3, 5, 4)\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, 0.490496, -0.651771, 0.293113, -0.580922, -0.396436, 0.864522, -0.411659, 0.317379, 0.771844, 0.507235, 0.067782, 0.922641, -0.944205, 0.383713, -0.380187, -0.875176, 0.799314, -0.681818, -0.761323, 0.768315, -0.298568, -0.611688, -0.768656, 0.525693]\n",
+      "out shape: (1, 3, 4)\n",
+      "out: [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"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (3, 5, 4)\n",
+    "L = Cropping2D(cropping=((1,1),(1, 1)), 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.Cropping2D.1] cropping (1,1),(1, 1) on 3x5x4 input, dim_ordering=th**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:06:23.116129",
+     "start_time": "2016-11-15T10:06:23.092950"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (3, 5, 4)\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, 0.490496, -0.651771, 0.293113, -0.580922, -0.396436, 0.864522, -0.411659, 0.317379, 0.771844, 0.507235, 0.067782, 0.922641, -0.944205, 0.383713, -0.380187, -0.875176, 0.799314, -0.681818, -0.761323, 0.768315, -0.298568, -0.611688, -0.768656, 0.525693]\n",
+      "out shape: (3, 3, 2)\n",
+      "out: [0.428035, 0.160547, 0.272735, 0.010827, 0.381948, -0.676994, 0.565201, -0.492094, -0.710674, 0.406968, -0.793497, -0.040684, 0.507235, 0.067782, 0.383713, -0.380187, -0.681818, -0.761323]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (3, 5, 4)\n",
+    "L = Cropping2D(cropping=((1,1),(1, 1)), 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.Cropping2D.2] cropping (4,2),(3,1) on 8x7x6 input, dim_ordering=tf**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:06:23.525520",
+     "start_time": "2016-11-15T10:06:23.501122"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (8, 7, 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, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306, 0.898062, -0.203771, 0.645788, 0.596358, -0.571654, -0.636819, -0.367376, -0.892607, -0.123297, 0.748404, -0.783012, 0.061963, -0.908546, -0.988517, 0.97308, 0.483845, 0.967956, 0.95863, 0.633963, -0.109138, -0.615821, -0.479821, 0.42827, 0.761351, 0.108265, -0.835679, -0.200177, 0.556308, -0.346135, -0.591775, 0.888818, 0.088756, 0.887542, 0.231398, -0.062874, 0.379803, 0.248259, -0.004909, -0.694836, 0.246241, 0.576698, 0.877474, 0.353439, 0.528707, 0.585634, -0.985339, 0.438776, -0.314863, 0.784171, 0.12525, 0.889265, -0.644213, 0.052514, 0.733269, 0.099278, -0.141949, 0.498542, 0.735955, 0.760313, -0.660278, -0.604273, 0.199522, 0.346309, -0.488666, 0.706332, 0.995574, 0.234642, 0.042779, 0.583139, 0.159919, 0.104808, 0.232585, 0.625624, 0.748204, -0.878399, 0.514601, -0.392989, -0.835864, 0.247256, -0.889704, -0.776558, 0.069902, -0.982291, 0.139565, 0.559765, 0.364254, -0.167329, -0.027032, -0.396669, 0.887928, 0.24142, -0.950083, -0.354758, 0.815999, -0.533224, 0.001955, -0.997966, -0.716888, 0.861882, -0.883, 0.797625, 0.602008, -0.633245, -0.710132, 0.608369, -0.784398, 0.576564, 0.633279, 0.542795, 0.90345, 0.457414, 0.242734, 0.310622, -0.82517, 0.262998, -0.571046, 0.936527, 0.878469, -0.347791, -0.859749, -0.952931, -0.129702, 0.685477, -0.796418, 0.377681, 0.858414, -0.835134, 0.014816, 0.103085, 0.850919, -0.005764, 0.715943, 0.003942, 0.746505, -0.422674, 0.819656, 0.037147, 0.078259, 0.609219, 0.240121, -0.457013, -0.947625, -0.598543, 0.754378, 0.694688, 0.001741, -0.731877, -0.442437, 0.116966, 0.360755, 0.396709, 0.018889, -0.243222, 0.03233, 0.154516, 0.04915, 0.942633, -0.225662, 0.778155, 0.927135, -0.298936, -0.73013, -0.978144, -0.838932, 0.085808, -0.636771, -0.209125, 0.739682, 0.447976, -0.737185, 0.645007, -0.038338, -0.031764, -0.667117, -0.075763, 0.701876, 0.505738, 0.997181, -0.554428, -0.862689, 0.966555, 0.401094, -0.489316, 0.197598, 0.440944, 0.437652, -0.078096, 0.271921, 0.702964, -0.154695, 0.964746, 0.089738, -0.530063, -0.374368, -0.501949, -0.435823, -0.059041, 0.059762, 0.18368, -0.879219, -0.853283, 0.463376, 0.790678, -0.906068, -0.772338, 0.117419, 0.409168, 0.219139, -0.017059, 0.552391, 0.835856, -0.710045, 0.404615, -0.182959, -0.331881, -0.395508, -0.730692, 0.252354, 0.915251, -0.79357, 0.70079, -0.011832, -0.18324, 0.070365, 0.769189, 0.383026, -0.868293, 0.800455, 0.727587, -0.62417, -0.300598, -0.63724, -0.08886, 0.176103, 0.086305, -0.782202, -0.719459, -0.153585, -0.037527, 0.923902, 0.043134, -0.500307, -0.862448, -0.591515, 0.803712, 0.066251, -0.016947, 0.412293, -0.004009, 0.370996, -0.556311, -0.286518, -0.454324, 0.069928, -0.002182, -0.425322, 0.99516, 0.537532, 0.054711, 0.22533, -0.238724, 0.495853, 0.901436, 0.405811, -0.430268, -0.542533, -0.663813, -0.007121, 0.462074, -0.349408, 0.257701, 0.493632, -0.674434, -0.235252, 0.039342, 0.166896, 0.724501, 0.41962, -0.566634, 0.811497, 0.614648, -0.109631, -0.830493, -0.022923, -0.890479, 0.487839, 0.967617, 0.629215]\n",
+      "out shape: (2, 3, 6)\n",
+      "out: [0.609218, 0.240121, -0.457013, -0.947625, -0.598543, 0.754378, 0.694688, 0.001741, -0.731877, -0.442437, 0.116966, 0.360755, 0.396709, 0.018889, -0.243222, 0.03233, 0.154516, 0.04915, 0.966555, 0.401094, -0.489316, 0.197598, 0.440944, 0.437652, -0.078096, 0.271921, 0.702964, -0.154695, 0.964746, 0.089738, -0.530063, -0.374368, -0.501949, -0.435823, -0.059041, 0.059762]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (8, 7, 6)\n",
+    "L = Cropping2D(cropping=((4,2),(3,1)), 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.Cropping2D.3] cropping (4,2),(3,1) on 8x7x6 input, dim_ordering=th**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:06:23.828740",
+     "start_time": "2016-11-15T10:06:23.813107"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (8, 7, 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, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306, 0.898062, -0.203771, 0.645788, 0.596358, -0.571654, -0.636819, -0.367376, -0.892607, -0.123297, 0.748404, -0.783012, 0.061963, -0.908546, -0.988517, 0.97308, 0.483845, 0.967956, 0.95863, 0.633963, -0.109138, -0.615821, -0.479821, 0.42827, 0.761351, 0.108265, -0.835679, -0.200177, 0.556308, -0.346135, -0.591775, 0.888818, 0.088756, 0.887542, 0.231398, -0.062874, 0.379803, 0.248259, -0.004909, -0.694836, 0.246241, 0.576698, 0.877474, 0.353439, 0.528707, 0.585634, -0.985339, 0.438776, -0.314863, 0.784171, 0.12525, 0.889265, -0.644213, 0.052514, 0.733269, 0.099278, -0.141949, 0.498542, 0.735955, 0.760313, -0.660278, -0.604273, 0.199522, 0.346309, -0.488666, 0.706332, 0.995574, 0.234642, 0.042779, 0.583139, 0.159919, 0.104808, 0.232585, 0.625624, 0.748204, -0.878399, 0.514601, -0.392989, -0.835864, 0.247256, -0.889704, -0.776558, 0.069902, -0.982291, 0.139565, 0.559765, 0.364254, -0.167329, -0.027032, -0.396669, 0.887928, 0.24142, -0.950083, -0.354758, 0.815999, -0.533224, 0.001955, -0.997966, -0.716888, 0.861882, -0.883, 0.797625, 0.602008, -0.633245, -0.710132, 0.608369, -0.784398, 0.576564, 0.633279, 0.542795, 0.90345, 0.457414, 0.242734, 0.310622, -0.82517, 0.262998, -0.571046, 0.936527, 0.878469, -0.347791, -0.859749, -0.952931, -0.129702, 0.685477, -0.796418, 0.377681, 0.858414, -0.835134, 0.014816, 0.103085, 0.850919, -0.005764, 0.715943, 0.003942, 0.746505, -0.422674, 0.819656, 0.037147, 0.078259, 0.609219, 0.240121, -0.457013, -0.947625, -0.598543, 0.754378, 0.694688, 0.001741, -0.731877, -0.442437, 0.116966, 0.360755, 0.396709, 0.018889, -0.243222, 0.03233, 0.154516, 0.04915, 0.942633, -0.225662, 0.778155, 0.927135, -0.298936, -0.73013, -0.978144, -0.838932, 0.085808, -0.636771, -0.209125, 0.739682, 0.447976, -0.737185, 0.645007, -0.038338, -0.031764, -0.667117, -0.075763, 0.701876, 0.505738, 0.997181, -0.554428, -0.862689, 0.966555, 0.401094, -0.489316, 0.197598, 0.440944, 0.437652, -0.078096, 0.271921, 0.702964, -0.154695, 0.964746, 0.089738, -0.530063, -0.374368, -0.501949, -0.435823, -0.059041, 0.059762, 0.18368, -0.879219, -0.853283, 0.463376, 0.790678, -0.906068, -0.772338, 0.117419, 0.409168, 0.219139, -0.017059, 0.552391, 0.835856, -0.710045, 0.404615, -0.182959, -0.331881, -0.395508, -0.730692, 0.252354, 0.915251, -0.79357, 0.70079, -0.011832, -0.18324, 0.070365, 0.769189, 0.383026, -0.868293, 0.800455, 0.727587, -0.62417, -0.300598, -0.63724, -0.08886, 0.176103, 0.086305, -0.782202, -0.719459, -0.153585, -0.037527, 0.923902, 0.043134, -0.500307, -0.862448, -0.591515, 0.803712, 0.066251, -0.016947, 0.412293, -0.004009, 0.370996, -0.556311, -0.286518, -0.454324, 0.069928, -0.002182, -0.425322, 0.99516, 0.537532, 0.054711, 0.22533, -0.238724, 0.495853, 0.901436, 0.405811, -0.430268, -0.542533, -0.663813, -0.007121, 0.462074, -0.349408, 0.257701, 0.493632, -0.674434, -0.235252, 0.039342, 0.166896, 0.724501, 0.41962, -0.566634, 0.811497, 0.614648, -0.109631, -0.830493, -0.022923, -0.890479, 0.487839, 0.967617, 0.629215]\n",
+      "out shape: (8, 1, 2)\n",
+      "out: [0.290718, -0.038623, -0.479821, 0.42827, -0.488666, 0.706332, -0.784398, 0.576564, -0.442437, 0.116966, -0.154695, 0.964746, -0.63724, -0.08886, -0.235252, 0.039342]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (8, 7, 6)\n",
+    "L = Cropping2D(cropping=((4,2),(3,1)), 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": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (with sys packages)",
+   "language": "python",
+   "name": "py3syspck"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/convolutional/Cropping3D.ipynb b/notebooks/convolutional/Cropping3D.ipynb
new file mode 100644
index 0000000..5e7ba09
--- /dev/null
+++ b/notebooks/convolutional/Cropping3D.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:23:54.915740",
+     "start_time": "2016-11-15T10:23:54.178233"
+    },
+    "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 Cropping3D\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:23:54.919435",
+     "start_time": "2016-11-15T10:23:54.917091"
+    },
+    "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": [
+    "### Cropping3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[convolutional.Cropping3D.0] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=tf**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:25:16.914056",
+     "start_time": "2016-11-15T10:25:16.898008"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (3, 5, 3, 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, 0.617251, 0.334305, -0.351137, -0.642574, 0.108974, -0.993964, 0.051085, -0.372012, 0.843766, 0.088025, -0.598662, 0.789035, -0.971233, -0.917609, 0.10035, -0.993908, 0.001303, 0.151006, -0.222302, -0.462642, 0.318882, 0.910461, -0.951606, -0.628088, 0.858834, 0.18648, 0.005839, -0.976863, -0.602595, -0.907466, 0.537085, 0.254335, -0.624879, -0.26462, -0.597274, 0.717515, 0.933281, -0.928165, -0.664793, 0.350419, 0.604354, 0.134613, -0.170544, 0.287505, -0.342583, -0.908204, -0.055996, 0.686364, -0.877763, 0.666511, -0.333493, -0.283194, 0.764997, 0.702478, -0.495511, 0.451364, 0.945924, -0.238126, 0.146568, -0.281422, -0.379007, 0.914172, 0.337272, 0.175728, -0.945958, -0.727577, -0.133378, -0.069779, -0.952209, 0.237581, -0.244335, 0.838199, -0.187442, 0.255861, 0.28158, -0.60797, 0.992258, 0.16382, 0.240562, 0.353587, 0.300174, 0.216722, -0.422345, -0.239941, 0.354771, 0.567014, -0.085289, 0.751301, -0.255041, -0.656808, -0.220736, -0.275667, 0.568007, 0.251274, 0.888831, 0.976139, 0.040987, 0.474905, 0.181267, 0.707542, -0.570195, 0.897656, -0.91601, -0.419443, -0.346422, -0.770603, 0.883902, -0.490007, 0.470248, -0.217687, 0.365399]\n",
+      "out shape: (1, 3, 1, 3)\n",
+      "out: [-0.26462, -0.597274, 0.717515, -0.170544, 0.287505, -0.342583, -0.283194, 0.764997, 0.702478]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (3, 5, 3, 3)\n",
+    "L = Cropping3D(cropping=((1,1), (1,1), (1,1)), 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.Cropping3D.1] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=th**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:32:51.809434",
+     "start_time": "2016-11-15T10:32:51.786296"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (3, 5, 3, 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, 0.617251, 0.334305, -0.351137, -0.642574, 0.108974, -0.993964, 0.051085, -0.372012, 0.843766, 0.088025, -0.598662, 0.789035, -0.971233, -0.917609, 0.10035, -0.993908, 0.001303, 0.151006, -0.222302, -0.462642, 0.318882, 0.910461, -0.951606, -0.628088, 0.858834, 0.18648, 0.005839, -0.976863, -0.602595, -0.907466, 0.537085, 0.254335, -0.624879, -0.26462, -0.597274, 0.717515, 0.933281, -0.928165, -0.664793, 0.350419, 0.604354, 0.134613, -0.170544, 0.287505, -0.342583, -0.908204, -0.055996, 0.686364, -0.877763, 0.666511, -0.333493, -0.283194, 0.764997, 0.702478, -0.495511, 0.451364, 0.945924, -0.238126, 0.146568, -0.281422, -0.379007, 0.914172, 0.337272, 0.175728, -0.945958, -0.727577, -0.133378, -0.069779, -0.952209, 0.237581, -0.244335, 0.838199, -0.187442, 0.255861, 0.28158, -0.60797, 0.992258, 0.16382, 0.240562, 0.353587, 0.300174, 0.216722, -0.422345, -0.239941, 0.354771, 0.567014, -0.085289, 0.751301, -0.255041, -0.656808, -0.220736, -0.275667, 0.568007, 0.251274, 0.888831, 0.976139, 0.040987, 0.474905, 0.181267, 0.707542, -0.570195, 0.897656, -0.91601, -0.419443, -0.346422, -0.770603, 0.883902, -0.490007, 0.470248, -0.217687, 0.365399]\n",
+      "out shape: (3, 3, 1, 1)\n",
+      "out: [-0.700858, -0.90685, -0.372012, -0.597274, 0.287505, 0.764997, 0.353587, -0.255041, 0.474905]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (3, 5, 3, 3)\n",
+    "L = Cropping3D(cropping=((1,1), (1,1), (1,1)), 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.Cropping3D.2] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=tf**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:33:43.754762",
+     "start_time": "2016-11-15T10:33:43.728485"
+    },
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (7, 6, 6, 6)\n",
+      "in: [-0.215694, 0.441215, 0.116911, 0.53299, 0.883562, -0.535525, -0.869764, -0.596287, 0.576428, -0.689083, -0.132924, -0.129935, -0.17672, -0.29097, 0.590914, 0.992098, 0.908965, -0.170202, 0.640203, 0.178644, 0.866749, -0.545566, -0.827072, 0.420342, -0.076191, 0.207686, -0.908472, -0.795307, -0.948319, 0.683682, -0.563278, -0.82135, 0.391356, 0.873557, 0.101774, 0.552487, -0.805471, -0.158387, 0.737777, 0.407663, 0.024323, 0.931444, -0.813418, -0.054911, -0.8488, -0.528392, 0.31073, -0.518975, -0.334317, 0.984459, 0.876118, 0.939272, -0.286813, -0.499695, 0.741409, 0.979405, 0.384731, -0.861441, 0.956879, 0.371096, 0.395767, -0.182768, -0.04864, 0.943523, 0.252702, -0.922957, 0.084044, -0.151514, -0.104476, -0.519114, 0.170185, -0.304221, 0.648458, 0.975786, -0.026504, 0.806763, -0.551306, 0.363744, -0.253539, 0.520681, -0.555914, 0.164078, 0.069617, 0.61564, -0.814065, 0.834879, 0.448904, 0.572477, -0.634663, -0.962277, -0.511182, 0.28331, 0.456662, 0.097291, -0.612782, 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+      "out shape: (2, 3, 1, 6)\n",
+      "out: [0.795219, -0.16835, -0.51469, 0.478591, -0.749477, 0.155347, 0.195573, 0.58147, 0.06667, 0.854165, 0.416721, 0.034649, 0.480717, 0.050545, -0.517454, 0.646335, -0.520894, 0.298304, 0.691759, -0.628298, 0.697236, 0.670352, 0.110078, -0.137484, 0.042634, -0.939601, -0.949383, -0.383965, -0.42231, -0.374712, -0.267755, -0.299397, 0.475516, 0.224848, -0.613151, 0.853631]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (7, 6, 6, 6)\n",
+    "L = Cropping3D(cropping=((3,2), (2,1), (2,3)), 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(262)\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.Cropping3D.3] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=th**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2016-11-15T10:34:42.856366",
+     "start_time": "2016-11-15T10:34:42.837264"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "in shape: (7, 6, 6, 6)\n",
+      "in: [-0.215694, 0.441215, 0.116911, 0.53299, 0.883562, -0.535525, -0.869764, -0.596287, 0.576428, -0.689083, -0.132924, -0.129935, -0.17672, -0.29097, 0.590914, 0.992098, 0.908965, -0.170202, 0.640203, 0.178644, 0.866749, -0.545566, -0.827072, 0.420342, -0.076191, 0.207686, -0.908472, -0.795307, -0.948319, 0.683682, -0.563278, -0.82135, 0.391356, 0.873557, 0.101774, 0.552487, -0.805471, -0.158387, 0.737777, 0.407663, 0.024323, 0.931444, -0.813418, -0.054911, -0.8488, -0.528392, 0.31073, -0.518975, -0.334317, 0.984459, 0.876118, 0.939272, -0.286813, -0.499695, 0.741409, 0.979405, 0.384731, -0.861441, 0.956879, 0.371096, 0.395767, -0.182768, -0.04864, 0.943523, 0.252702, -0.922957, 0.084044, -0.151514, -0.104476, -0.519114, 0.170185, -0.304221, 0.648458, 0.975786, -0.026504, 0.806763, -0.551306, 0.363744, -0.253539, 0.520681, -0.555914, 0.164078, 0.069617, 0.61564, -0.814065, 0.834879, 0.448904, 0.572477, -0.634663, -0.962277, -0.511182, 0.28331, 0.456662, 0.097291, -0.612782, 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+      "out shape: (7, 1, 3, 1)\n",
+      "out: [0.665183, -0.117032, 0.377938, 0.750921, -0.78475, -0.864729, 0.767556, 0.980922, -0.868826, 0.06667, -0.738848, 0.422474, -0.949383, 0.872373, -0.45984, 0.275255, 0.676277, -0.078256, -0.943866, 0.112569, -0.76884]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_in_shape = (7, 6, 6, 6)\n",
+    "L = Cropping3D(cropping=((3,2), (2,1), (2,3)), dim_ordering='th')\n",
+    "\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(262)\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": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (with sys packages)",
+   "language": "python",
+   "name": "py3syspck"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/src/layers/convolutional/Cropping1D.js b/src/layers/convolutional/Cropping1D.js
new file mode 100644
index 0000000..0fe048f
--- /dev/null
+++ b/src/layers/convolutional/Cropping1D.js
@@ -0,0 +1,47 @@
+import Layer from '../../Layer'
+import Tensor from '../../Tensor'
+import ops from 'ndarray-ops'
+
+/**
+ * Cropping1D layer class
+ */
+export default class Cropping1D extends Layer {
+  /**
+   * Creates a Cropping1D activation layer
+   * @param {cropping} attrs.cropping - tuple of of int (length 2)
+   */
+  constructor (attrs = {}) {
+    super(attrs)
+    this.layerClass = 'Cropping1D'
+
+    const {
+      cropping = [0,0],
+      dimOrdering = 'tf'
+    } = attrs
+
+    this.cropping = cropping
+    this.dimOrdering = dimOrdering
+  }
+
+  /**
+   * Method for layer computational logic
+   * @param {Tensor} x
+   * @returns {Tensor} x
+   */
+  call (x) {
+    const inputShape = x.tensor.shape
+    const outputShape = [
+      inputShape[0] - this.cropping[0] - this.cropping[1],
+      inputShape[1]
+    ]
+    let y = new Tensor([], outputShape)
+    ops.assign(
+      y.tensor,
+      x.tensor
+        .hi(inputShape[0] - this.cropping[1], inputShape[2])
+        .lo(this.cropping[0], 0)
+    )
+    x.tensor = y.tensor
+    return x
+  }
+}
diff --git a/src/layers/convolutional/Cropping2D.js b/src/layers/convolutional/Cropping2D.js
new file mode 100644
index 0000000..1ac6d65
--- /dev/null
+++ b/src/layers/convolutional/Cropping2D.js
@@ -0,0 +1,59 @@
+import Layer from '../../Layer'
+import Tensor from '../../Tensor'
+import ops from 'ndarray-ops'
+
+/**
+ * Cropping2D layer class
+ */
+export default class Cropping2D extends Layer {
+  /**
+   * Creates a Cropping2D activation layer
+   * @param {cropping} attrs.cropping - tuple of tuple of int (length 2)
+   */
+  constructor (attrs = {}) {
+    super(attrs)
+    this.layerClass = 'Cropping2D'
+
+    const {
+      cropping = [[0,0], [0,0]],
+      dimOrdering = 'tf'
+    } = attrs
+
+    this.cropping = cropping
+    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.cropping[0][0] - this.cropping[0][1],
+      inputShape[1] - this.cropping[1][0] - this.cropping[1][1],
+      inputShape[2]
+    ]
+    let y = new Tensor([], outputShape)
+    ops.assign(
+      y.tensor,
+      x.tensor
+        .hi(inputShape[0] - this.cropping[0][1], inputShape[1] - this.cropping[1][1], inputShape[2])
+        .lo(this.cropping[0][0], this.cropping[1][0], 0)
+    )
+    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/Cropping3D.js b/src/layers/convolutional/Cropping3D.js
new file mode 100644
index 0000000..8a1ce85
--- /dev/null
+++ b/src/layers/convolutional/Cropping3D.js
@@ -0,0 +1,60 @@
+import Layer from '../../Layer'
+import Tensor from '../../Tensor'
+import ops from 'ndarray-ops'
+
+/**
+ * Cropping3D layer class
+ */
+export default class Cropping3D extends Layer {
+  /**
+   * Creates a Cropping3D activation layer
+   * @param {cropping} attrs.cropping - tuple of tuple of int (length 3)
+   */
+  constructor (attrs = {}) {
+    super(attrs)
+    this.layerClass = 'Cropping3D'
+
+    const {
+      cropping = [[0,0], [0,0], [0,0]],
+      dimOrdering = 'tf'
+    } = attrs
+
+    this.cropping = cropping
+    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.cropping[0][0] - this.cropping[0][1],
+      inputShape[1] - this.cropping[1][0] - this.cropping[1][1],
+      inputShape[2] - this.cropping[2][0] - this.cropping[2][1],
+      inputShape[3]
+    ]
+    let y = new Tensor([], outputShape)
+    ops.assign(
+      y.tensor,
+      x.tensor
+        .hi(inputShape[0] - this.cropping[0][1], inputShape[1] - this.cropping[1][1], inputShape[2] - this.cropping[2][1], inputShape[3])
+        .lo(this.cropping[0][0], this.cropping[1][0], this.cropping[2][0], 0)
+    )
+    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 187c2fa..a1bd2c1 100644
--- a/src/layers/convolutional/index.js
+++ b/src/layers/convolutional/index.js
@@ -10,6 +10,9 @@ import UpSampling3D from './UpSampling3D'
 import ZeroPadding1D from './ZeroPadding1D'
 import ZeroPadding2D from './ZeroPadding2D'
 import ZeroPadding3D from './ZeroPadding3D'
+import Cropping1D from './Cropping1D'
+import Cropping2D from './Cropping2D'
+import Cropping3D from './Cropping3D'
 
 export {
   Convolution1D,
@@ -23,5 +26,8 @@ export {
   UpSampling3D,
   ZeroPadding1D,
   ZeroPadding2D,
-  ZeroPadding3D
+  ZeroPadding3D,
+  Cropping1D,
+  Cropping2D,
+  Cropping3D
 }
diff --git a/test/convolutional/Cropping1D.js b/test/convolutional/Cropping1D.js
new file mode 100644
index 0000000..7614219
--- /dev/null
+++ b/test/convolutional/Cropping1D.js
@@ -0,0 +1,48 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: Cropping1D', 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: Cropping1D', styles.h1)
+  })
+
+  it(`[convolutional.Cropping1D.0] cropping (1,1) on 6x4 input`, function () {
+    const key = `convolutional.Cropping1D.0`
+    console.log(`\n%c[${key}] cropping (1,1) on 6x4 input`, styles.h3)
+    let testLayer = new layers.Cropping1D({ cropping: [1,1] })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.Cropping1D.1] cropping (2,3) on 6x4 input`, function () {
+    const key = `convolutional.Cropping1D.1`
+    console.log(`\n%c[${key}] cropping (2,3) on 6x4 input`, styles.h3)
+    let testLayer = new layers.Cropping1D({ cropping: [2,3] })
+    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/Cropping2D.js b/test/convolutional/Cropping2D.js
new file mode 100644
index 0000000..f3b1ec3
--- /dev/null
+++ b/test/convolutional/Cropping2D.js
@@ -0,0 +1,82 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: Cropping2D', 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: Cropping2D', styles.h1)
+  })
+
+  it(`[convolutional.Cropping2D.0] cropping (1,1),(1, 1) on 3x5x4 input, dim_ordering=tf`, function () {
+    const key = `convolutional.Cropping2D.0`
+    console.log(`\n%c[${key}] cropping (1,1),(1, 1) on 3x5x4 input, dimOrdering=tf`, styles.h3)
+    let testLayer = new layers.Cropping2D({ cropping: [[1, 1], [1, 1]], dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.Cropping2D.0] cropping (1,1),(1, 1) on 3x5x4 input, dim_ordering=th`, function () {
+    const key = `convolutional.Cropping2D.1`
+    console.log(`\n%c[${key}] cropping (1,1),(1, 1) on 3x5x4 input, dimOrdering=th`, styles.h3)
+    let testLayer = new layers.Cropping2D({ cropping: [[1, 1], [1, 1]], 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.Cropping2D.2] cropping (4,2),(3,1) on 8x7x6 input, dim_ordering=tf`, function () {
+    const key = `convolutional.Cropping2D.2`
+    console.log(`\n%c[${key}] cropping (4,2),(3,1) on 8x7x6 input, dimOrdering=tf`, styles.h3)
+    let testLayer = new layers.Cropping2D({ cropping: [[4,2],[3,1]], dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.Cropping2D.2] cropping (4,2),(3,1) on 8x7x6 input, dim_ordering=th`, function () {
+    const key = `convolutional.Cropping2D.3`
+    console.log(`\n%c[${key}] cropping (4,2),(3,1) on 8x7x6 input, dimOrdering=th`, styles.h3)
+    let testLayer = new layers.Cropping2D({ cropping: [[4,2],[3,1]], 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/Cropping3D.js b/test/convolutional/Cropping3D.js
new file mode 100644
index 0000000..5527de7
--- /dev/null
+++ b/test/convolutional/Cropping3D.js
@@ -0,0 +1,82 @@
+/* eslint-env browser, mocha */
+
+describe('convolutional layer: Cropping3D', 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: Cropping3D', styles.h1)
+  })
+
+  it(`[convolutional.Cropping3D.0] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=tf`, function () {
+    const key = `convolutional.Cropping3D.0`
+    console.log(`\n%c[${key}] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=tf`, styles.h3)
+    let testLayer = new layers.Cropping3D({ cropping: [[1, 1], [1, 1], [1,1]], dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.Cropping3D.0] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=th`, function () {
+    const key = `convolutional.Cropping3D.1`
+    console.log(`\n%c[${key}] cropping ((1,1), (1,1), (1,1)) on 3x5x3x3 input, dim_ordering=th`, styles.h3)
+    let testLayer = new layers.Cropping3D({ cropping: [[1, 1], [1, 1], [1,1]], 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.Cropping3D.2] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=tf`, function () {
+    const key = `convolutional.Cropping3D.2`
+    console.log(`\n%c[${key}] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=tf`, styles.h3)
+    let testLayer = new layers.Cropping3D({ cropping: [[3,2],[2,1],[2,3]], dimOrdering: 'tf' })
+    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
+    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
+    const startTime = performance.now()
+    t = testLayer.call(t)
+    const endTime = performance.now()
+    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
+    logTime(startTime, endTime)
+    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
+    const shapeExpected = TEST_DATA[key].expected.shape
+    assert.deepEqual(t.tensor.shape, shapeExpected)
+    assert.isTrue(approxEquals(t.tensor, dataExpected))
+  })
+
+  it(`[convolutional.Cropping3D.2] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=th`, function () {
+    const key = `convolutional.Cropping3D.3`
+    console.log(`\n%c[${key}] cropping (3,2), (2,1), (2,3)) on 7x6x6x6 input, dim_ordering=th`, styles.h3)
+    let testLayer = new layers.Cropping3D({ cropping: [[3,2],[2,1],[2,3]], 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/data_Cropping1D.js b/test/convolutional/data_Cropping1D.js
new file mode 100644
index 0000000..8c3a8b8
--- /dev/null
+++ b/test/convolutional/data_Cropping1D.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.Cropping1D.0': {
+      input: {
+        data: [-0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108, -0.1961, -0.031679, -0.188902, 0.846634],
+        shape: [6,4]
+      },
+      expected: {
+        data: [-0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108],
+        shape: [4, 4]
+      }
+    },
+    'convolutional.Cropping1D.1': {
+      input: {
+        data: [0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066, 0.532874, 0.866766, -0.544852, 0.876693, -0.523052, -0.716556, 0.875262, 0.544879],
+        shape: [6, 4]
+      },
+      expected: {
+        data: [-0.484373, -0.506163, 0.353904, 0.513668],
+        shape: [1, 4]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()
diff --git a/test/convolutional/data_Cropping2D.js b/test/convolutional/data_Cropping2D.js
new file mode 100644
index 0000000..f53fc03
--- /dev/null
+++ b/test/convolutional/data_Cropping2D.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.Cropping2D.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, 0.490496, -0.651771, 0.293113, -0.580922, -0.396436, 0.864522, -0.411659, 0.317379, 0.771844, 0.507235, 0.067782, 0.922641, -0.944205, 0.383713, -0.380187, -0.875176, 0.799314, -0.681818, -0.761323, 0.768315, -0.298568, -0.611688, -0.768656, 0.525693],
+        shape: [3, 5, 4]
+      },
+      expected: {
+        data: [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: [1, 3, 4]
+      }
+    },
+    'convolutional.Cropping2D.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, 0.170854, -0.139788, -0.710674, 0.406968, 0.705926, -0.094137, -0.793497, -0.040684, 0.522292, 0.490496, -0.651771, 0.293113, -0.580922, -0.396436, 0.864522, -0.411659, 0.317379, 0.771844, 0.507235, 0.067782, 0.922641, -0.944205, 0.383713, -0.380187, -0.875176, 0.799314, -0.681818, -0.761323, 0.768315, -0.298568, -0.611688, -0.768656, 0.525693],
+        shape: [3, 5, 4]
+      },
+      expected: {
+        data: [0.428035, 0.160547, 0.272735, 0.010827, 0.381948, -0.676994, 0.565201, -0.492094, -0.710674, 0.406968, -0.793497, -0.040684, 0.507235, 0.067782, 0.383713, -0.380187, -0.681818, -0.761323],
+        shape: [3, 3, 2]
+      }
+    },
+    'convolutional.Cropping2D.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, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306, 0.898062, -0.203771, 0.645788, 0.596358, -0.571654, -0.636819, -0.367376, -0.892607, -0.123297, 0.748404, -0.783012, 0.061963, -0.908546, -0.988517, 0.97308, 0.483845, 0.967956, 0.95863, 0.633963, -0.109138, -0.615821, -0.479821, 0.42827, 0.761351, 0.108265, -0.835679, -0.200177, 0.556308, -0.346135, -0.591775, 0.888818, 0.088756, 0.887542, 0.231398, -0.062874, 0.379803, 0.248259, -0.004909, -0.694836, 0.246241, 0.576698, 0.877474, 0.353439, 0.528707, 0.585634, -0.985339, 0.438776, -0.314863, 0.784171, 0.12525, 0.889265, -0.644213, 0.052514, 0.733269, 0.099278, -0.141949, 0.498542, 0.735955, 0.760313, -0.660278, -0.604273, 0.199522, 0.346309, -0.488666, 0.706332, 0.995574, 0.234642, 0.042779, 0.583139, 0.159919, 0.104808, 0.232585, 0.625624, 0.748204, -0.878399, 0.514601, -0.392989, -0.835864, 0.247256, -0.889704, -0.776558, 0.069902, -0.982291, 0.139565, 0.559765, 0.364254, -0.167329, -0.027032, -0.396669, 0.887928, 0.24142, -0.950083, -0.354758, 0.815999, -0.533224, 0.001955, -0.997966, -0.716888, 0.861882, -0.883, 0.797625, 0.602008, -0.633245, -0.710132, 0.608369, -0.784398, 0.576564, 0.633279, 0.542795, 0.90345, 0.457414, 0.242734, 0.310622, -0.82517, 0.262998, -0.571046, 0.936527, 0.878469, -0.347791, -0.859749, -0.952931, -0.129702, 0.685477, -0.796418, 0.377681, 0.858414, -0.835134, 0.014816, 0.103085, 0.850919, -0.005764, 0.715943, 0.003942, 0.746505, -0.422674, 0.819656, 0.037147, 0.078259, 0.609219, 0.240121, -0.457013, -0.947625, -0.598543, 0.754378, 0.694688, 0.001741, -0.731877, -0.442437, 0.116966, 0.360755, 0.396709, 0.018889, -0.243222, 0.03233, 0.154516, 0.04915, 0.942633, -0.225662, 0.778155, 0.927135, -0.298936, -0.73013, -0.978144, -0.838932, 0.085808, -0.636771, -0.209125, 0.739682, 0.447976, -0.737185, 0.645007, -0.038338, -0.031764, -0.667117, -0.075763, 0.701876, 0.505738, 0.997181, -0.554428, -0.862689, 0.966555, 0.401094, -0.489316, 0.197598, 0.440944, 0.437652, -0.078096, 0.271921, 0.702964, -0.154695, 0.964746, 0.089738, -0.530063, -0.374368, -0.501949, -0.435823, -0.059041, 0.059762, 0.18368, -0.879219, -0.853283, 0.463376, 0.790678, -0.906068, -0.772338, 0.117419, 0.409168, 0.219139, -0.017059, 0.552391, 0.835856, -0.710045, 0.404615, -0.182959, -0.331881, -0.395508, -0.730692, 0.252354, 0.915251, -0.79357, 0.70079, -0.011832, -0.18324, 0.070365, 0.769189, 0.383026, -0.868293, 0.800455, 0.727587, -0.62417, -0.300598, -0.63724, -0.08886, 0.176103, 0.086305, -0.782202, -0.719459, -0.153585, -0.037527, 0.923902, 0.043134, -0.500307, -0.862448, -0.591515, 0.803712, 0.066251, -0.016947, 0.412293, -0.004009, 0.370996, -0.556311, -0.286518, -0.454324, 0.069928, -0.002182, -0.425322, 0.99516, 0.537532, 0.054711, 0.22533, -0.238724, 0.495853, 0.901436, 0.405811, -0.430268, -0.542533, -0.663813, -0.007121, 0.462074, -0.349408, 0.257701, 0.493632, -0.674434, -0.235252, 0.039342, 0.166896, 0.724501, 0.41962, -0.566634, 0.811497, 0.614648, -0.109631, -0.830493, -0.022923, -0.890479, 0.487839, 0.967617, 0.629215],
+        shape: [8, 7, 6]
+      },
+      expected: {
+        data:  [0.609218, 0.240121, -0.457013, -0.947625, -0.598543, 0.754378, 0.694688, 0.001741, -0.731877, -0.442437, 0.116966, 0.360755, 0.396709, 0.018889, -0.243222, 0.03233, 0.154516, 0.04915, 0.966555, 0.401094, -0.489316, 0.197598, 0.440944, 0.437652, -0.078096, 0.271921, 0.702964, -0.154695, 0.964746, 0.089738, -0.530063, -0.374368, -0.501949, -0.435823, -0.059041, 0.059762],
+        shape: [2, 3, 6]
+      }
+    },
+    'convolutional.Cropping2D.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, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306, 0.898062, -0.203771, 0.645788, 0.596358, -0.571654, -0.636819, -0.367376, -0.892607, -0.123297, 0.748404, -0.783012, 0.061963, -0.908546, -0.988517, 0.97308, 0.483845, 0.967956, 0.95863, 0.633963, -0.109138, -0.615821, -0.479821, 0.42827, 0.761351, 0.108265, -0.835679, -0.200177, 0.556308, -0.346135, -0.591775, 0.888818, 0.088756, 0.887542, 0.231398, -0.062874, 0.379803, 0.248259, -0.004909, -0.694836, 0.246241, 0.576698, 0.877474, 0.353439, 0.528707, 0.585634, -0.985339, 0.438776, -0.314863, 0.784171, 0.12525, 0.889265, -0.644213, 0.052514, 0.733269, 0.099278, -0.141949, 0.498542, 0.735955, 0.760313, -0.660278, -0.604273, 0.199522, 0.346309, -0.488666, 0.706332, 0.995574, 0.234642, 0.042779, 0.583139, 0.159919, 0.104808, 0.232585, 0.625624, 0.748204, -0.878399, 0.514601, -0.392989, -0.835864, 0.247256, -0.889704, -0.776558, 0.069902, -0.982291, 0.139565, 0.559765, 0.364254, -0.167329, -0.027032, -0.396669, 0.887928, 0.24142, -0.950083, -0.354758, 0.815999, -0.533224, 0.001955, -0.997966, -0.716888, 0.861882, -0.883, 0.797625, 0.602008, -0.633245, -0.710132, 0.608369, -0.784398, 0.576564, 0.633279, 0.542795, 0.90345, 0.457414, 0.242734, 0.310622, -0.82517, 0.262998, -0.571046, 0.936527, 0.878469, -0.347791, -0.859749, -0.952931, -0.129702, 0.685477, -0.796418, 0.377681, 0.858414, -0.835134, 0.014816, 0.103085, 0.850919, -0.005764, 0.715943, 0.003942, 0.746505, -0.422674, 0.819656, 0.037147, 0.078259, 0.609219, 0.240121, -0.457013, -0.947625, -0.598543, 0.754378, 0.694688, 0.001741, -0.731877, -0.442437, 0.116966, 0.360755, 0.396709, 0.018889, -0.243222, 0.03233, 0.154516, 0.04915, 0.942633, -0.225662, 0.778155, 0.927135, -0.298936, -0.73013, -0.978144, -0.838932, 0.085808, -0.636771, -0.209125, 0.739682, 0.447976, -0.737185, 0.645007, -0.038338, -0.031764, -0.667117, -0.075763, 0.701876, 0.505738, 0.997181, -0.554428, -0.862689, 0.966555, 0.401094, -0.489316, 0.197598, 0.440944, 0.437652, -0.078096, 0.271921, 0.702964, -0.154695, 0.964746, 0.089738, -0.530063, -0.374368, -0.501949, -0.435823, -0.059041, 0.059762, 0.18368, -0.879219, -0.853283, 0.463376, 0.790678, -0.906068, -0.772338, 0.117419, 0.409168, 0.219139, -0.017059, 0.552391, 0.835856, -0.710045, 0.404615, -0.182959, -0.331881, -0.395508, -0.730692, 0.252354, 0.915251, -0.79357, 0.70079, -0.011832, -0.18324, 0.070365, 0.769189, 0.383026, -0.868293, 0.800455, 0.727587, -0.62417, -0.300598, -0.63724, -0.08886, 0.176103, 0.086305, -0.782202, -0.719459, -0.153585, -0.037527, 0.923902, 0.043134, -0.500307, -0.862448, -0.591515, 0.803712, 0.066251, -0.016947, 0.412293, -0.004009, 0.370996, -0.556311, -0.286518, -0.454324, 0.069928, -0.002182, -0.425322, 0.99516, 0.537532, 0.054711, 0.22533, -0.238724, 0.495853, 0.901436, 0.405811, -0.430268, -0.542533, -0.663813, -0.007121, 0.462074, -0.349408, 0.257701, 0.493632, -0.674434, -0.235252, 0.039342, 0.166896, 0.724501, 0.41962, -0.566634, 0.811497, 0.614648, -0.109631, -0.830493, -0.022923, -0.890479, 0.487839, 0.967617, 0.629215],
+        shape: [8, 7, 6]
+      },
+      expected: {
+        data: [0.290718, -0.038623, -0.479821, 0.42827, -0.488666, 0.706332, -0.784398, 0.576564, -0.442437, 0.116966, -0.154695, 0.964746, -0.63724, -0.08886, -0.235252, 0.039342],
+        shape: [8, 1, 2]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()
diff --git a/test/convolutional/data_Cropping3D.js b/test/convolutional/data_Cropping3D.js
new file mode 100644
index 0000000..04bf6b3
--- /dev/null
+++ b/test/convolutional/data_Cropping3D.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.Cropping3D.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, 0.617251, 0.334305, -0.351137, -0.642574, 0.108974, -0.993964, 0.051085, -0.372012, 0.843766, 0.088025, -0.598662, 0.789035, -0.971233, -0.917609, 0.10035, -0.993908, 0.001303, 0.151006, -0.222302, -0.462642, 0.318882, 0.910461, -0.951606, -0.628088, 0.858834, 0.18648, 0.005839, -0.976863, -0.602595, -0.907466, 0.537085, 0.254335, -0.624879, -0.26462, -0.597274, 0.717515, 0.933281, -0.928165, -0.664793, 0.350419, 0.604354, 0.134613, -0.170544, 0.287505, -0.342583, -0.908204, -0.055996, 0.686364, -0.877763, 0.666511, -0.333493, -0.283194, 0.764997, 0.702478, -0.495511, 0.451364, 0.945924, -0.238126, 0.146568, -0.281422, -0.379007, 0.914172, 0.337272, 0.175728, -0.945958, -0.727577, -0.133378, -0.069779, -0.952209, 0.237581, -0.244335, 0.838199, -0.187442, 0.255861, 0.28158, -0.60797, 0.992258, 0.16382, 0.240562, 0.353587, 0.300174, 0.216722, -0.422345, -0.239941, 0.354771, 0.567014, -0.085289, 0.751301, -0.255041, -0.656808, -0.220736, -0.275667, 0.568007, 0.251274, 0.888831, 0.976139, 0.040987, 0.474905, 0.181267, 0.707542, -0.570195, 0.897656, -0.91601, -0.419443, -0.346422, -0.770603, 0.883902, -0.490007, 0.470248, -0.217687, 0.365399],
+        shape: [3, 5, 3, 3]
+      },
+      expected: {
+        data: [-0.26462, -0.597274, 0.717515, -0.170544, 0.287505, -0.342583, -0.283194, 0.764997, 0.702478],
+        shape: [1, 3, 1, 3]
+      }
+    },
+    'convolutional.Cropping3D.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, 0.617251, 0.334305, -0.351137, -0.642574, 0.108974, -0.993964, 0.051085, -0.372012, 0.843766, 0.088025, -0.598662, 0.789035, -0.971233, -0.917609, 0.10035, -0.993908, 0.001303, 0.151006, -0.222302, -0.462642, 0.318882, 0.910461, -0.951606, -0.628088, 0.858834, 0.18648, 0.005839, -0.976863, -0.602595, -0.907466, 0.537085, 0.254335, -0.624879, -0.26462, -0.597274, 0.717515, 0.933281, -0.928165, -0.664793, 0.350419, 0.604354, 0.134613, -0.170544, 0.287505, -0.342583, -0.908204, -0.055996, 0.686364, -0.877763, 0.666511, -0.333493, -0.283194, 0.764997, 0.702478, -0.495511, 0.451364, 0.945924, -0.238126, 0.146568, -0.281422, -0.379007, 0.914172, 0.337272, 0.175728, -0.945958, -0.727577, -0.133378, -0.069779, -0.952209, 0.237581, -0.244335, 0.838199, -0.187442, 0.255861, 0.28158, -0.60797, 0.992258, 0.16382, 0.240562, 0.353587, 0.300174, 0.216722, -0.422345, -0.239941, 0.354771, 0.567014, -0.085289, 0.751301, -0.255041, -0.656808, -0.220736, -0.275667, 0.568007, 0.251274, 0.888831, 0.976139, 0.040987, 0.474905, 0.181267, 0.707542, -0.570195, 0.897656, -0.91601, -0.419443, -0.346422, -0.770603, 0.883902, -0.490007, 0.470248, -0.217687, 0.365399],
+        shape: [3, 5, 3, 3]
+      },
+      expected: {
+        data: [-0.700858, -0.90685, -0.372012, -0.597274, 0.287505, 0.764997, 0.353587, -0.255041, 0.474905],
+        shape: [3, 3, 1, 1]
+      }
+    },
+    'convolutional.Cropping3D.2': {
+      input: {
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0.292814, 0.275683, 0.124566, 0.551632, -0.010923, 0.678319, 0.628851, -0.458944, 0.800552, -0.397508, 0.98454, -0.965652, -0.20582, 0.820265, -0.132816, -0.278788, -0.856228, 0.139643, 0.703275, -0.322692, -0.338363, 0.378641, -0.28617, 0.101698, 0.433472, 0.068613, -0.148242, -0.466705, 0.738394, -0.410927, 0.984962, 0.405669, 0.125866, -0.982088, 0.955039, 0.845436, -0.745603, 0.446631, 0.78486, 0.369507, -0.380944, 0.883958, 0.268992, 0.106985, 0.97518, -0.222182, 0.831121, 0.946638, -0.357391, -0.347985, 0.787543, 0.209383, 0.476927, -0.374685, 0.693189, 0.60696, -0.978613, 0.289488, 0.943956, 0.035094, -0.89281, 0.105184, -0.666919, 0.304481, -0.955062, 0.925374, 0.157778, -0.468151, -0.31179, -0.012953, 0.164448, -0.656307, 0.343733],
+        shape: [7, 6, 6, 6]
+      },
+      expected: {
+        data: [0.665183, -0.117032, 0.377938, 0.750921, -0.78475, -0.864729, 0.767556, 0.980922, -0.868826, 0.06667, -0.738848, 0.422474, -0.949383, 0.872373, -0.45984, 0.275255, 0.676277, -0.078256, -0.943866, 0.112569, -0.76884],
+        shape: [7, 1, 3, 1]
+      }
+    }
+  }
+
+  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
+})()
diff --git a/test/index.html b/test/index.html
index 9381735..63e0a09 100644
--- a/test/index.html
+++ b/test/index.html
@@ -71,6 +71,12 @@
   <script src="convolutional/ZeroPadding2D.js"></script>
   <script src="convolutional/data_ZeroPadding3D.js"></script>
   <script src="convolutional/ZeroPadding3D.js"></script>
+  <script src="convolutional/data_Cropping1D.js"></script>
+  <script src="convolutional/Cropping1D.js"></script>
+  <script src="convolutional/data_Cropping2D.js"></script>
+  <script src="convolutional/Cropping2D.js"></script>
+  <script src="convolutional/data_Cropping3D.js"></script>
+  <script src="convolutional/Cropping3D.js"></script>
 
   <!-- pooling -->
   <script src="pooling/data_MaxPooling1D.js"></script>