Add Cropping1D,2D,3D (transcranial#28)

* Added failing test for convolutional1D with input (16,1) * bugfix * Add Cropping1D,2D,3D * Update readme * update build * Revert "bugfix" This reverts commit e769612. * Revert "Added failing test for convolutional1D with input (16,1)" This reverts commit 5c91ed8.
neurals-ro · Nov 16, 2016 · c6e12f9 · c6e12f9
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diff --git a/README.md b/README.md
@@ -150,7 +150,7 @@ See `demos/src/` for source code of real examples written in VueJS.
 
   - *advanced activations*: LeakyReLU, PReLU, ELU, ParametricSoftplus, ThresholdedReLU, SReLU
 
-  - *convolutional*: Convolution1D, Convolution2D, AtrousConvolution2D, SeparableConvolution2D, Deconvolution2D, Convolution3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D
+  - *convolutional*: Convolution1D, Convolution2D, AtrousConvolution2D, SeparableConvolution2D, Deconvolution2D, Convolution3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D, Cropping1D, Cropping2D, Cropping3D
 
   - *core*: Dense, Activation, Dropout, SpatialDropout2D, SpatialDropout3D, Flatten, Reshape, Permute, RepeatVector, Merge, Highway, MaxoutDense
 
@@ -170,7 +170,7 @@ See `demos/src/` for source code of real examples written in VueJS.
 
   - *core*: Lambda
 
-  - *convolutional*: AtrousConvolution1D, Cropping1D, Cropping2D, Cropping3D
+  - *convolutional*: AtrousConvolution1D
 
   - *locally-connected*: LocallyConnected1D, LocallyConnected2D
 

diff --git a/dist/keras.js b/dist/keras.js
diff --git a/notebooks/convolutional/Cropping1D.ipynb 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
+}