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CNN.sc
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## CNN\n", | |
| "\n", | |
| "This plugin provides a standalone implementation of a Convolutional Neural Network." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Dependencies" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import $ivy.`com.thoughtworks.each::each:3.3.1`\n", | |
| "import $ivy.`com.thoughtworks.deeplearning::plugins-builtins:2.0.0`\n", | |
| "\n", | |
| "import scala.concurrent.ExecutionContext.Implicits.global\n", | |
| "import org.nd4j.linalg.api.ndarray.INDArray\n", | |
| "import org.nd4j.linalg.convolution.Convolution\n", | |
| "import org.nd4j.linalg.util.ArrayUtil\n", | |
| "import org.nd4j.linalg.factory.Nd4j\n", | |
| "import org.nd4j.linalg.api.ops.impl.transforms.IsMax\n", | |
| "import scalaz.syntax.all._\n", | |
| "import com.thoughtworks.raii.shared._\n", | |
| "import com.thoughtworks.raii.asynchronous._\n", | |
| "import com.thoughtworks.feature.ImplicitApply\n", | |
| "import com.thoughtworks.each.Monadic._\n", | |
| "import com.thoughtworks.feature.Factory\n", | |
| "import com.thoughtworks.deeplearning.DeepLearning\n", | |
| "import com.thoughtworks.deeplearning.plugins.ImplicitsSingleton\n", | |
| "import com.thoughtworks.deeplearning.plugins.INDArrayLayers\n", | |
| "import com.thoughtworks.deeplearning.plugins.Operators\n", | |
| "import com.thoughtworks.deeplearning.plugins.Training\n", | |
| "import com.thoughtworks.deeplearning.plugins.Builtins" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "interp.load(scala.io.Source.fromURL(new java.net.URL(\"https://gist.githubusercontent.com/TerrorJack/cdd9cc5adc82fc86abf8b4c72cd26e76/raw/1f15523ee4b5a7fcc7e7317ae34ba09be207a62a/CNN.sc\")).mkString)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "interp.load(\"\"\"\n", | |
| " val hyperparameters = Factory[Builtins with CNNs].newInstance()\n", | |
| "\"\"\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### For sbt projects" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "// build.sbt\n", | |
| "libraryDependencies += \"com.thoughtworks.deeplearning\" %% \"plugins-builtins\" % \"latest.release\"\n", | |
| "\n", | |
| "addCompilerPlugin(\"com.thoughtworks.import\" %% \"import\" % \"latest.release\")\n", | |
| "\n", | |
| "// XXX.scala\n", | |
| "import $exec.`https://gist.githubusercontent.com/TerrorJack/cdd9cc5adc82fc86abf8b4c72cd26e76/raw/1f15523ee4b5a7fcc7e7317ae34ba09be207a62a/CNN.sc`\n", | |
| "\n", | |
| "import scala.concurrent.ExecutionContext.Implicits.global\n", | |
| "import org.nd4j.linalg.api.ndarray.INDArray\n", | |
| "import org.nd4j.linalg.convolution.Convolution\n", | |
| "import org.nd4j.linalg.util.ArrayUtil\n", | |
| "import org.nd4j.linalg.factory.Nd4j\n", | |
| "import org.nd4j.linalg.api.ops.impl.transforms.IsMax\n", | |
| "import scalaz.syntax.all._\n", | |
| "import com.thoughtworks.raii.shared._\n", | |
| "import com.thoughtworks.raii.asynchronous._\n", | |
| "import com.thoughtworks.feature.ImplicitApply\n", | |
| "import com.thoughtworks.each.Monadic._\n", | |
| "import com.thoughtworks.feature.Factory\n", | |
| "import com.thoughtworks.deeplearning.DeepLearning\n", | |
| "import com.thoughtworks.deeplearning.plugins.ImplicitsSingleton\n", | |
| "import com.thoughtworks.deeplearning.plugins.INDArrayLayers\n", | |
| "import com.thoughtworks.deeplearning.plugins.Operators\n", | |
| "import com.thoughtworks.deeplearning.plugins.Training\n", | |
| "import com.thoughtworks.deeplearning.plugins.Builtins\n", | |
| " \n", | |
| "val hyperparameters = Factory[Builtins with CNNs].newInstance()" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Scala", | |
| "language": "scala", | |
| "name": "scala" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": "text/x-scala", | |
| "file_extension": ".scala", | |
| "mimetype": "text/x-scala", | |
| "name": "scala211", | |
| "nbconvert_exporter": "script", | |
| "pygments_lexer": "scala", | |
| "version": "2.11.11" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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| trait CNNs | |
| extends com.thoughtworks.deeplearning.plugins.INDArrayLayers | |
| with com.thoughtworks.deeplearning.plugins.ImplicitsSingleton | |
| with com.thoughtworks.deeplearning.plugins.Training | |
| with com.thoughtworks.deeplearning.plugins.Operators { | |
| import org.nd4j.linalg.api.ndarray.INDArray | |
| import org.nd4j.linalg.convolution.Convolution | |
| import org.nd4j.linalg.util.ArrayUtil | |
| import org.nd4j.linalg.factory.Nd4j | |
| import org.nd4j.linalg.api.ops.impl.transforms.IsMax | |
| import scalaz.syntax.all._ | |
| import com.thoughtworks.raii.shared._ | |
| import com.thoughtworks.raii.asynchronous._ | |
| import com.thoughtworks.feature.ImplicitApply | |
| import com.thoughtworks.each.Monadic._ | |
| import com.thoughtworks.feature.Factory | |
| import com.thoughtworks.deeplearning.DeepLearning | |
| trait ImplicitsApi | |
| extends super[INDArrayLayers].ImplicitsApi | |
| with super[Training].ImplicitsApi | |
| with super[Operators].ImplicitsApi | |
| type Implicits <: ImplicitsApi | |
| private def toArray(tuple2: (Int, Int)): Array[Int] = { | |
| val (one, two) = tuple2 | |
| Array(one, two) | |
| } | |
| def im2col[Operand0, Out <: INDArrayLayer](operand0: Operand0, | |
| kernel: (Int, Int), | |
| stride: (Int, Int), | |
| padding: (Int, Int))( | |
| implicit deepLearning: DeepLearning.Aux[Operand0, INDArray, INDArray], | |
| layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = { | |
| INDArrayLayer.unary(operand0) { data0: INDArray => | |
| val shape0 = data0.shape | |
| val strideArray = toArray(stride) | |
| val paddingArray = toArray(padding) | |
| val outputData = Convolution.im2col(data0, toArray(kernel), strideArray, paddingArray) | |
| val delta0 = { outputDelta: INDArray => | |
| Convolution.col2im(outputDelta, strideArray, paddingArray, shape0(2), shape0(3)) | |
| } | |
| (outputData, delta0) | |
| } | |
| } | |
| @inline | |
| def conv2d[Input, Weight, Bias, Out <: INDArrayLayer](input: Input, | |
| weight: Weight, | |
| bias: Bias, | |
| kernel: (Int, Int), | |
| stride: (Int, Int), | |
| padding: (Int, Int))( | |
| implicit inputDeepLearning: DeepLearning.Aux[Input, INDArray, INDArray], | |
| weightDeepLearning: DeepLearning.Aux[Weight, INDArray, INDArray], | |
| biasDeepLearning: DeepLearning.Aux[Bias, INDArray, INDArray], | |
| layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = { | |
| import implicits._ | |
| INDArrayLayer(monadic[Do] { | |
| val inputShape = input.forward.each.data.shape | |
| val numberOfImages = inputShape(0) | |
| val depth = inputShape(1) | |
| val height = inputShape(2) | |
| val width = inputShape(3) | |
| val numberOfKernels = weight.forward.each.data.shape.head | |
| val col = im2col(input, kernel, stride, padding) | |
| val permutedCol = col.permute(0, 4, 5, 1, 2, 3) | |
| val depthKernelKernel = depth * kernel._1 * kernel._2 | |
| val operandCol2d = permutedCol.reshape(numberOfImages * height * width, depthKernelKernel) | |
| val reshapedWeight = weight.reshape(numberOfKernels, depthKernelKernel) | |
| val permutedWeight = reshapedWeight.permute(1, 0) | |
| val dotResult = operandCol2d dot permutedWeight | |
| val plusResult = dotResult + bias | |
| val reshapeResult = plusResult.reshape(numberOfImages, height, width, numberOfKernels) | |
| reshapeResult.permute(0, 3, 1, 2).forward.each | |
| }) | |
| } | |
| @inline | |
| def maxPool[Operand0, Out <: INDArrayLayer](operand0: Operand0, poolSize: (Int, Int))( | |
| implicit deepLearning: DeepLearning.Aux[Operand0, INDArray, INDArray], | |
| layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = { | |
| INDArrayLayer.unary(operand0) { data0: INDArray => | |
| val shape0 = data0.shape | |
| val kernelAndStrideSize: Array[Int] = toArray(poolSize) | |
| val preMaxPool: INDArray = | |
| Convolution | |
| .im2col(data0, kernelAndStrideSize, kernelAndStrideSize, Array(0, 0)) | |
| .permute(0, 1, 4, 5, 2, 3) | |
| val preShape: Seq[Int] = preMaxPool.shape().toSeq | |
| val lastDimensionSize: Int = preShape.takeRight(2).product | |
| val reshapedPreMaxPool: INDArray = preMaxPool | |
| .reshape(preShape.take(preShape.length - 2) :+ lastDimensionSize: _*) | |
| val outputData = reshapedPreMaxPool.max(4) | |
| val delta0 = { outputDelta: INDArray => | |
| val a = reshapedPreMaxPool | |
| val upStreamDup = a.dup() | |
| val rows = ArrayUtil.prod(a.length()) | |
| val isMax: INDArray = Nd4j.getExecutioner | |
| .execAndReturn(new IsMax(upStreamDup, 4)) | |
| .reshape(preShape.take(preShape.length - 2) :+ poolSize._2 :+ poolSize._1: _*) | |
| .permute(0, 1, 2, 4, 3, 5) | |
| .reshape('c', rows, 1) | |
| val outputDelta1d = { | |
| outputDelta | |
| .repeat(-1, poolSize._1) | |
| .permute(1, 0, 3, 2) | |
| .repeat(-1, poolSize._2) | |
| .permute(1, 0, 3, 2) | |
| .reshape('c', shape0.product, 1) | |
| } | |
| isMax | |
| .muliColumnVector(outputDelta1d) | |
| .reshape(shape0: _*) | |
| } | |
| (outputData, delta0) | |
| } | |
| } | |
| } |
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| MIT License | |
| Copyright (c) 2017 ThoughtWorks Inc. | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: | |
| The above copyright notice and this permission notice shall be included in all | |
| copies or substantial portions of the Software. | |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| SOFTWARE. |
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