ai.djl.nn.core.Prelu Maven / Gradle / Ivy
/*
* Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file 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 ai.djl.nn.core;
import ai.djl.MalformedModelException;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.internal.NDArrayEx;
import ai.djl.ndarray.types.Shape;
import ai.djl.nn.AbstractBlock;
import ai.djl.nn.Parameter;
import ai.djl.training.ParameterStore;
import ai.djl.util.PairList;
import java.io.DataInputStream;
import java.io.IOException;
/**
* Applies Leaky Parametric ReLU activation element-wise to the input.
*
* Leaky ReLUs attempt to fix the 'dying ReLU' problem by allowing a small slope when the input
* is negative and has a slope of one when input is positive. This is defined by \(y= x \gt 0 ? x :
* slope * x\).
*
*
Parametric ReLU is a Leaky ReLU in which the slope is learnt during training.
*/
public class Prelu extends AbstractBlock {
private static final byte VERSION = 2;
private Parameter alpha;
/** Creates a Parametric ReLU Block. */
@SuppressWarnings("this-escape")
public Prelu() {
super(VERSION);
alpha =
addParameter(
Parameter.builder()
.setName("alpha")
.setType(Parameter.Type.WEIGHT)
.optShape(new Shape())
.build());
}
/** {@inheritDoc} */
@Override
protected NDList forwardInternal(
ParameterStore parameterStore,
NDList inputs,
boolean training,
PairList params) {
NDArray input = inputs.singletonOrThrow();
NDArray alphaArr = parameterStore.getValue(alpha, input.getDevice(), training);
return prelu(input, alphaArr);
}
/** {@inheritDoc} */
@Override
public Shape[] getOutputShapes(Shape[] inputs) {
return new Shape[] {inputs[0]};
}
/** {@inheritDoc} */
@Override
public void loadMetadata(byte loadVersion, DataInputStream is)
throws IOException, MalformedModelException {
if (loadVersion == version) {
readInputShapes(is);
} else if (loadVersion != 1) {
throw new MalformedModelException("Unsupported encoding version: " + loadVersion);
}
}
/**
* Applies a Prelu activation on the input {@link NDArray}.
*
* Prelu is defined as \(y = max(0,x) + alpha * min(0, x) \) where alpha is learnable
* parameter
*
* @param input the input {@link NDArray}
* @param alpha learnable parameter
* @return the {@link NDArray} after applying Prelu activation
*/
public static NDList prelu(NDArray input, NDArray alpha) {
NDArrayEx ex = input.getNDArrayInternal();
return ex.prelu(input, alpha);
}
}