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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, IdentityOutputShape, TensorModule}
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
/**
* Densely connected highway network.
* Highway layers are a natural extension of LSTMs to feedforward networks.
* The input of this layer should be 2D, i.e. (batch, input dim).
*
* When you use this layer as the first layer of a model, you need to provide the argument
* inputShape (a Single Shape, does not include the batch dimension).
*
* @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 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 Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class Highway[T: ClassTag](
val activation: KerasLayer[Tensor[T], Tensor[T], T] = null,
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 == 2,
s"Highway requires 2D input, but got input dim ${input.length}")
inputShape
}
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val input = inputShape.toSingle().toArray
val layer = com.intel.analytics.bigdl.nn.Highway[T](
size = input(1),
withBias = bias,
activation = if (activation != null) {
activation.build(inputShape)
activation.labor.asInstanceOf[TensorModule[T]]
} else {
null
},
wRegularizer = wRegularizer,
bRegularizer = bRegularizer
)
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object Highway {
def apply[@specialized(Float, Double) T: ClassTag](
activation: String = null,
wRegularizer: Regularizer[T] = null,
bRegularizer: Regularizer[T] = null,
bias: Boolean = true,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : Highway[T] = {
new Highway[T](KerasUtils.getKerasActivation(activation),
wRegularizer, bRegularizer, bias, inputShape)
}
}
