com.intel.analytics.bigdl.nn.keras.Recurrent.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.{Cell, Reverse, Select, Sequential => TSequential}
import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule
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
/**
* This is the abstract base class for recurrent layers.
* Do not create a new instance of it or use it in a model.
* Please use its child classes, 'SimpleRNN', 'LSTM' and 'GRU' instead.
*/
abstract class Recurrent[T: ClassTag](
val outputDim: Int,
val returnSequences: Boolean = false,
val goBackwards: Boolean = false,
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"Recurrent layers require 3D input, but got input dim ${input.length}")
if (returnSequences) Shape(input(0), input(1), outputDim)
else Shape(input(0), outputDim)
}
def buildCell(input: Array[Int]): Cell[T] = {
throw new RuntimeException("Recurrent cell haven't been implemented yet.")
}
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val input = inputShape.toSingle().toArray
val model = TSequential[T]()
if (goBackwards) model.add(Reverse(2))
val rec = com.intel.analytics.bigdl.nn.Recurrent[T]()
rec.add(buildCell(input))
model.add(rec)
if (!returnSequences) model.add(Select(2, -1))
model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}