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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.Cell
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, 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
/**
* Long Short Term Memory unit architecture.
* The input of this layer should be 3D, i.e. (batch, time steps, 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 outputDim Hidden unit size. Dimension of internal projections and final output.
* @param activation Activation function to use.
* You can also pass in corresponding string representations such as 'relu'
* or 'sigmoid', etc. for simple activations in the factory method.
* Default is 'tanh'.
* @param innerActivation Activation function for inner cells.
* You can also pass in corresponding string representations such as 'relu'
* or 'sigmoid', etc. for simple activations in the factory method.
* Default is 'hard_sigmoid'.
* @param returnSequences Whether to return the full sequence or only return the last output
* in the output sequence. Default is false.
* @param goBackwards Whether the input sequence will be processed backwards. Default is false.
* @param wRegularizer An instance of [[Regularizer]], (eg. L1 or L2 regularization),
* applied to the input weights matrices. Default is null.
* @param uRegularizer An instance of [[Regularizer]], applied the recurrent weights matrices.
* Default is null.
* @param bRegularizer An instance of [[Regularizer]], applied to the bias. Default is null.
* @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class LSTM[T: ClassTag](
outputDim: Int,
val activation: KerasLayer[Tensor[T], Tensor[T], T] = null,
val innerActivation: KerasLayer[Tensor[T], Tensor[T], T] = null,
returnSequences: Boolean = false,
goBackwards: Boolean = false,
var wRegularizer: Regularizer[T] = null,
var uRegularizer: Regularizer[T] = null,
var bRegularizer: Regularizer[T] = null,
inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends Recurrent[T](outputDim, returnSequences, goBackwards, inputShape) {
override def buildCell(input: Array[Int]): Cell[T] = {
com.intel.analytics.bigdl.nn.LSTM[T](
inputSize = input(2),
hiddenSize = outputDim,
activation = activation.doBuild(inputShape).asInstanceOf[TensorModule[T]],
innerActivation = innerActivation.doBuild(inputShape).asInstanceOf[TensorModule[T]],
wRegularizer = wRegularizer,
uRegularizer = uRegularizer,
bRegularizer = bRegularizer)
}
}
object LSTM {
def apply[@specialized(Float, Double) T: ClassTag](
outputDim: Int,
activation: String = "tanh",
innerActivation: String = "hard_sigmoid",
returnSequences: Boolean = false,
goBackwards: Boolean = false,
wRegularizer: Regularizer[T] = null,
uRegularizer: Regularizer[T] = null,
bRegularizer: Regularizer[T] = null,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : LSTM[T] = {
new LSTM(outputDim, KerasUtils.getKerasActivation(activation),
KerasUtils.getKerasActivation(innerActivation), returnSequences,
goBackwards, wRegularizer, uRegularizer, bRegularizer, inputShape)
}
}
