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/*
* 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, DataFormat}
import com.intel.analytics.bigdl.nn.{Squeeze, 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
/**
* Locally-connected layer for 1D inputs which works similarly to the TemporalConvolution layer,
* except that weights are unshared, that is, a different set of filters
* is applied at each different patch of the input.
* Border mode currently supported for this layer is 'valid'.
* 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 Dimensionality of the output.
* @param filterLength The extension (spatial or temporal) of each filter.
* @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 Integer. Factor by which to subsample output.
* @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.
* @param bias Whether to include a bias (i.e. make the layer affine rather than linear).
* Default is true.
* @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class LocallyConnected1D[T: ClassTag](
val nbFilter: Int,
val filterLength: Int,
val activation: KerasLayer[Tensor[T], Tensor[T], T] = null,
val subsampleLength: Int = 1,
var wRegularizer: Regularizer[T] = null,
var bRegularizer: Regularizer[T] = null,
val bias: Boolean = true,
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"LocallyConnected1D requires 3D input, but got input dim ${input.length}")
val length = KerasUtils.computeConvOutputLength(input(1), filterLength,
"valid", subsampleLength)
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(com.intel.analytics.bigdl.nn.Reshape(Array(input(1), 1, input(2)), Some(true)))
val layer = com.intel.analytics.bigdl.nn.LocallyConnected2D(
nInputPlane = input(2),
inputWidth = 1,
inputHeight = input(1),
nOutputPlane = nbFilter,
kernelW = 1,
kernelH = filterLength,
strideW = 1,
strideH = subsampleLength,
wRegularizer = wRegularizer,
bRegularizer = bRegularizer,
withBias = bias,
format = DataFormat.NHWC)
model.add(layer)
model.add(Squeeze(3))
if (activation != null) {
model.add(activation.doBuild(inputShape))
}
model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object LocallyConnected1D {
def apply[@specialized(Float, Double) T: ClassTag](
nbFilter: Int,
filterLength: Int,
activation: String = null,
subsampleLength: Int = 1,
wRegularizer: Regularizer[T] = null,
bRegularizer: Regularizer[T] = null,
bias: Boolean = true,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): LocallyConnected1D[T] = {
new LocallyConnected1D[T](nbFilter, filterLength,
KerasUtils.getKerasActivation(activation), subsampleLength,
wRegularizer, bRegularizer, bias, inputShape)
}
}