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.keras
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
import com.intel.analytics.bigdl.nn.{InitializationMethod, Xavier, Zeros}
import com.intel.analytics.bigdl.nn.{SpatialDilatedConvolution, Squeeze, Transpose, Sequential => TSequential}
import com.intel.analytics.bigdl.optim.Regularizer
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Shape
import scala.reflect.ClassTag
/**
* Applies an atrous convolution operator for filtering neighborhoods of 1-D inputs.
* A.k.a dilated convolution or convolution with holes.
* Bias will be included in this layer.
* Border mode currently supported for this layer is 'valid'.
* You can also use AtrousConv1D as an alias of this layer.
* The input of this layer should be 3D.
*
* When using this layer as the first layer in a model, you need to provide the argument
* inputShape (a Single Shape, does not include the batch dimension).
*
* @param nbFilter Number of convolution kernels to use.
* @param filterLength The extension (spatial or temporal) of each filter.
* @param init Initialization method for the weights of the layer. Default is Xavier.
* You can also pass in corresponding string representations such as 'glorot_uniform'
* or 'normal', etc. for simple init methods in the factory method.
* @param activation Activation function to use. Default is null.
* You can also pass in corresponding string representations such as 'relu'
* or 'sigmoid', etc. for simple activations in the factory method.
* @param subsampleLength Factor by which to subsample output. Integer. Default is 1.
* @param atrousRate Factor for kernel dilation. Also called filter_dilation elsewhere.
* Integer. Default is 1.
* @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization),
* applied to the input weights matrices. Default is null.
* @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null.
* @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class AtrousConvolution1D[T: ClassTag](
val nbFilter: Int,
val filterLength: Int,
val init: InitializationMethod = Xavier,
val activation: KerasLayer[Tensor[T], Tensor[T], T] = null,
val subsampleLength: Int = 1,
val atrousRate: Int = 1,
var wRegularizer: Regularizer[T] = null,
var bRegularizer: Regularizer[T] = null,
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {
override def computeOutputShape(inputShape: Shape): Shape = {
val input = inputShape.toSingle().toArray
require(input.length == 3,
s"AtrousConvolution1D requires 3D input, but got input dim ${input.length}")
val length = KerasUtils.computeConvOutputLength(input(1), filterLength,
"valid", subsampleLength, atrousRate)
Shape(input(0), length, nbFilter)
}
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val input = inputShape.toSingle().toArray
val model = TSequential[T]()
model.add(Transpose(Array((2, 3))))
model.add(com.intel.analytics.bigdl.nn.Reshape(Array(input(2), input(1), 1), Some(true)))
val layer = SpatialDilatedConvolution(
nInputPlane = input(2),
nOutputPlane = nbFilter,
kW = 1,
kH = filterLength,
dW = 1,
dH = subsampleLength,
dilationW = 1,
dilationH = atrousRate,
wRegularizer = wRegularizer,
bRegularizer = bRegularizer)
layer.setInitMethod(weightInitMethod = init, biasInitMethod = Zeros)
model.add(layer)
model.add(Transpose(Array((2, 3))))
model.add(Squeeze(4))
if (activation != null) {
model.add(activation.doBuild(inputShape))
}
model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object AtrousConvolution1D {
def apply[@specialized(Float, Double) T: ClassTag](
nbFilter: Int,
filterLength: Int,
init: String = "glorot_uniform",
activation: String = null,
subsampleLength: Int = 1,
atrousRate: Int = 1,
wRegularizer: Regularizer[T] = null,
bRegularizer: Regularizer[T] = null,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): AtrousConvolution1D[T] = {
new AtrousConvolution1D[T](nbFilter, filterLength, KerasUtils.getInitMethod(init),
KerasUtils.getKerasActivation(activation), subsampleLength, atrousRate,
wRegularizer, bRegularizer, inputShape)
}
}
