Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.nn
import com.intel.analytics.bigdl.nn.VariableFormat.Default
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.RandomGenerator
/**
* VariableFormat describe the meaning of each dimension of the variable
* (the trainable parameters of a model like weight and bias) and can be used to
* return the fan in and fan out size of the variable when provided the variable shape.
*/
trait VariableFormat {
def getFanIn(shape: Array[Int]): Int = {
throw new Exception("FanIn is not defined in this format")
}
def getFanOut(shape: Array[Int]): Int = {
throw new Exception("FanOut is not defined in this format")
}
}
object VariableFormat {
/**
* The default VariableFormat used when we do not care about
* the specified format of this variable.
*/
case object Default extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
shape.product
}
override def getFanOut(shape: Array[Int]): Int = {
shape.product
}
}
case object ONE_D extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
shape(0)
}
override def getFanOut(shape: Array[Int]): Int = {
1
}
}
case object IN_OUT extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
shape(0)
}
override def getFanOut(shape: Array[Int]): Int = {
shape(1)
}
}
case object OUT_IN extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
shape(1)
}
override def getFanOut(shape: Array[Int]): Int = {
shape(0)
}
}
case object IN_OUT_KW_KH extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(2) * shape(3)
shape(0) * receptiveFieldSize
}
override def getFanOut(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(2) * shape(3)
shape(1) * receptiveFieldSize
}
}
case object OUT_IN_KW_KH extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(2) * shape(3)
shape(1) * receptiveFieldSize
}
override def getFanOut(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(2) * shape(3)
shape(0) * receptiveFieldSize
}
}
case object GP_OUT_IN_KW_KH extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(0) * shape(3) * shape(4)
shape(2) * receptiveFieldSize
}
override def getFanOut(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(0) * shape(3) * shape(4)
shape(1) * receptiveFieldSize
}
}
case object GP_IN_OUT_KW_KH extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(0) * shape(3) * shape(4)
shape(1) * receptiveFieldSize
}
override def getFanOut(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(0) * shape(3) * shape(4)
shape(2) * receptiveFieldSize
}
}
case object OUT_IN_KT_KH_KW extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(2) * shape(3) * shape(4)
shape(1) * receptiveFieldSize
}
override def getFanOut(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(2) * shape(3) * shape(4)
shape(0) * receptiveFieldSize
}
}
case object GP_KH_KW_IN_OUT extends VariableFormat {
override def getFanIn(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(0) * shape(1) * shape(2)
shape(2) * receptiveFieldSize
}
override def getFanOut(shape: Array[Int]): Int = {
val receptiveFieldSize = shape(0) * shape(1) * shape(2)
shape(3) * receptiveFieldSize
}
}
}
/**
* Initialization method to initialize bias and weight.
* The init method will be called in Module.reset()
*/
trait InitializationMethod {
type Shape = Array[Int]
/**
* Initialize the given weight and bias.
*
* @param variable the weight to initialize
* @param dataFormat the data format of weight indicating the dimension order of
* the weight. "output_first" means output is in the lower dimension
* "input_first" means input is in the lower dimension.
*/
def init[T](variable: Tensor[T], dataFormat: VariableFormat = Default)
(implicit ev: TensorNumeric[T]): Unit
}
/**
* Initializer that generates tensors with a uniform distribution.
*
* It draws samples from a uniform distribution within [-limit, limit]
* where "limit" is "1/sqrt(fan_in)"
*
*/
case object RandomUniform extends InitializationMethod {
override def init[T](variable: Tensor[T], dataFormat: VariableFormat)
(implicit ev: TensorNumeric[T]): Unit = {
val shape = variable.size()
val fanIn = dataFormat.getFanIn(shape)
val stdv = 1.0 / math.sqrt(fanIn)
variable.rand(-stdv, stdv)
}
}
/**
* Initializer that generates tensors with a uniform distribution.
*
* It draws samples from a uniform distribution within [lower, upper]
*
*/
case class RandomUniform(lower: Double, upper: Double) extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat = Default)
(implicit ev: TensorNumeric[T]): Unit = {
variable.rand(lower, upper)
}
}
/**
* Initializer that generates tensors with a normal distribution.
*
*/
case class RandomNormal(mean: Double, stdv: Double) extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat = Default)
(implicit ev: TensorNumeric[T]): Unit = {
variable.randn(mean, stdv)
}
}
/**
* Initializer that generates tensors with zeros.
*/
case object Zeros extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat = Default)
(implicit ev: TensorNumeric[T]): Unit = {
variable.zero()
}
}
/**
* Initializer that generates tensors with zeros.
*/
case object Ones extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat = Default)
(implicit ev: TensorNumeric[T]): Unit = {
variable.fill(ev.one)
}
}
/**
* Initializer that generates tensors with certain constant double.
*/
case class ConstInitMethod(value: Double) extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat = Default)
(implicit ev: TensorNumeric[T]): Unit = {
variable.fill(ev.fromType(value))
}
}
/**
* In short, it helps signals reach deep into the network.
*
* During the training process of deep nn:
* 1. If the weights in a network start are too small,
* then the signal shrinks as it passes through
* each layer until it’s too tiny to be useful.
*
* 2. If the weights in a network start too large,
* then the signal grows as it passes through each
* layer until it’s too massive to be useful.
*
* Xavier initialization makes sure the weights are ‘just right’,
* keeping the signal in a reasonable range of values through many layers.
*
* More details on the paper
* [Understanding the difficulty of training deep feedforward neural networks]
* (http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)
*/
case object Xavier extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat)
(implicit ev: TensorNumeric[T]): Unit = {
val shape = variable.size()
val fanIn = dataFormat.getFanIn(shape)
val fanOut = dataFormat.getFanOut(shape)
val stdv = math.sqrt(6.0 / (fanIn + fanOut))
variable.rand(-stdv, stdv)
}
}
/**
* A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically
* accounts for ReLU nonlinearities.
*
* Aside: for another perspective on the scaling factor, see the derivation of
* [Saxe, McClelland, and Ganguli 2013 (v3)].
*
* It fills the incoming matrix by randomly sampling Gaussian data with std =
* sqrt(2 / n) where n is the fanIn, fanOut, or their average, depending on
* the varianceNormAverage parameter.
*
* @param varianceNormAverage VarianceNorm use average of (fanIn + fanOut) or just fanOut
*/
case class MsraFiller(varianceNormAverage: Boolean = true) extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat)
(implicit ev: TensorNumeric[T]): Unit = {
val shape = variable.size()
val fanIn = dataFormat.getFanIn(shape)
val fanOut = dataFormat.getFanOut(shape)
val n = if (varianceNormAverage) {
(fanIn + fanOut) / 2
} else {
fanOut
}
val std = math.sqrt(2.0 / n)
variable.apply1(_ => ev.fromType(RandomGenerator.RNG.normal(0, std)))
}
}
/**
* Initialize the weight with coefficients for bilinear interpolation.
*
* A common use case is with the DeconvolutionLayer acting as upsampling.
* The variable tensor passed in the init function should have 5 dimensions
* of format [nGroup, nInput, nOutput, kH, kW], and kH should be equal to kW
*
*/
case object BilinearFiller extends InitializationMethod {
def init[T](variable: Tensor[T], dataFormat: VariableFormat = Default)
(implicit ev: TensorNumeric[T]): Unit = {
val shape = variable.size()
require(shape.length == 5, s"weight must be 5 dim, " +
s"but got ${shape.length}")
val kH = shape(3)
val kW = shape(4)
require(kH == kW, s"Kernel $kH * $kW must be square")
val f = Math.ceil(kW / 2.0).toInt
val c = (2 * f - 1 - f % 2) / (2.0f * f)
val weightArray = variable.storage().array()
val weightOffset = variable.storageOffset() - 1
var i = 0
while(i < variable.nElement()) {
val x : Float = i % kW
val y : Float = (i / kW) % kH
weightArray(i + weightOffset) = ev.fromType[Float](
(1f - math.abs(x / f - c)) * (1f - math.abs(y / f - c)))
i += 1
}
}
}
