com.intel.analytics.bigdl.nn.keras.SoftMax.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
import com.intel.analytics.bigdl.nn.{Transpose, Sequential => TSequential}
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
/**
* Just a wrapper class. Please use Activation('softmax') instead.
*/
class SoftMax[T: ClassTag](
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 || input.length == 3,
s"SoftMax requires 2D or 3D 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.SoftMax()
if (input.length <= 2) {
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
else {
val model = TSequential[T]()
model.add(Transpose(Array((1, 3))))
model.add(layer)
model.add(Transpose(Array((1, 3))))
model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
}
object SoftMax {
def apply[@specialized(Float, Double) T: ClassTag](
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SoftMax[T] = {
new SoftMax[T](inputShape)
}
}