com.intel.analytics.bigdl.nn.keras.TimeDistributed.scala Maven / Gradle / Ivy
/*
* 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, TensorModule}
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
/**
* TimeDistributed wrapper.
* Apply a layer to every temporal slice of an input.
* The input should be at least 3D, and the dimension of index one
* will be considered to be the temporal dimension.
* 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).
*
* If you apply TimeDistributed to a Dense layer, you can use:
* TimeDistributed(Dense(8), inputShape = Shape(10, 12))
*
* @param layer A layer instance.
* @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class TimeDistributed[T: ClassTag](
val layer: KerasLayer[Tensor[T], Tensor[T], T],
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {
private def getInnerInput(input: Array[Int]): Array[Int] = {
Array(input(0)) ++ input.slice(2, input.length)
}
override def computeOutputShape(inputShape: Shape): Shape = {
val input = inputShape.toSingle().toArray
require(input.length >=3,
s"TimeDistributed requires at least 3D input, but got input dim ${input.length}")
val innerInput = getInnerInput(input)
val innerOutput = layer.computeOutputShape(Shape(innerInput)).toSingle()
val output = innerOutput.take(1) ++ List(input(1)) ++ innerOutput.drop(1)
Shape(output.toArray)
}
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val input = inputShape.toSingle().toArray
val innerInput = getInnerInput(input)
layer.build(Shape(innerInput))
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
val timedistributed = com.intel.analytics.bigdl.nn.TimeDistributed(layer)
timedistributed.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object TimeDistributed {
def apply[@specialized(Float, Double) T: ClassTag](
layer: KerasLayer[Tensor[T], Tensor[T], T],
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): TimeDistributed[T] = {
new TimeDistributed[T](layer, inputShape)
}
}