org.deeplearning4j.arbiter.layers.SubsamplingLayerSpace Maven / Gradle / Ivy
/*-
*
* * Copyright 2016 Skymind,Inc.
* *
* * 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 org.deeplearning4j.arbiter.layers;
import lombok.AccessLevel;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.parameter.FixedValue;
import org.deeplearning4j.arbiter.util.LeafUtils;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
/**
* Layer hyperparameter configuration space for subsampling layers
*
* @author Alex Black
*/
@Data
@EqualsAndHashCode(callSuper = true)
@NoArgsConstructor(access = AccessLevel.PRIVATE) //For Jackson JSON/YAML deserialization
public class SubsamplingLayerSpace extends LayerSpace {
protected ParameterSpace convolutionMode;
protected ParameterSpace poolingType;
protected ParameterSpace kernelSize;
protected ParameterSpace stride;
protected ParameterSpace padding;
protected ParameterSpace pnorm;
protected ParameterSpace eps;
private SubsamplingLayerSpace(Builder builder) {
super(builder);
this.convolutionMode = builder.convolutionMode;
this.poolingType = builder.poolingType;
this.kernelSize = builder.kernelSize;
this.stride = builder.stride;
this.padding = builder.padding;
this.pnorm = builder.pnorm;
this.eps = builder.eps;
this.numParameters = LeafUtils.countUniqueParameters(collectLeaves());
}
@Override
public SubsamplingLayer getValue(double[] values) {
SubsamplingLayer.Builder b = new SubsamplingLayer.Builder();
setLayerOptionsBuilder(b, values);
return b.build();
}
protected void setLayerOptionsBuilder(SubsamplingLayer.Builder builder, double[] values) {
super.setLayerOptionsBuilder(builder, values);
if (convolutionMode != null)
builder.convolutionMode(convolutionMode.getValue(values));
if (poolingType != null)
builder.poolingType(poolingType.getValue(values));
if (kernelSize != null)
builder.kernelSize(kernelSize.getValue(values));
if (stride != null)
builder.stride(stride.getValue(values));
if (padding != null)
builder.padding(padding.getValue(values));
if(pnorm != null)
builder.pnorm(pnorm.getValue(values));
if(eps != null)
builder.eps(eps.getValue(values));
}
@Override
public String toString() {
return toString(", ");
}
@Override
public String toString(String delim) {
StringBuilder sb = new StringBuilder("SubsamplingLayerSpace(");
if (convolutionMode != null)
sb.append("convolutionMode: ").append(convolutionMode).append(delim);
if (poolingType != null)
sb.append("poolingType: ").append(poolingType).append(delim);
if (kernelSize != null)
sb.append("kernelSize: ").append(kernelSize).append(delim);
if (stride != null)
sb.append("stride: ").append(stride).append(delim);
if (padding != null)
sb.append("padding: ").append(padding).append(delim);
if (pnorm != null)
sb.append("pnorm: ").append(pnorm).append(delim);
if (eps != null)
sb.append("eps: ").append(eps).append(delim);
sb.append(super.toString(delim)).append(")");
return sb.toString();
}
public static class Builder extends FeedForwardLayerSpace.Builder {
protected ParameterSpace convolutionMode;
protected ParameterSpace poolingType;
protected ParameterSpace kernelSize;
protected ParameterSpace stride;
protected ParameterSpace padding;
protected ParameterSpace pnorm;
protected ParameterSpace eps;
public Builder convolutionMode(ConvolutionMode convolutionMode){
return convolutionMode(new FixedValue<>(convolutionMode));
}
public Builder convolutionMode(ParameterSpace convolutionMode){
this.convolutionMode = convolutionMode;
return this;
}
public Builder poolingType(SubsamplingLayer.PoolingType poolingType) {
return poolingType(new FixedValue<>(poolingType));
}
public Builder poolingType(ParameterSpace poolingType) {
this.poolingType = poolingType;
return this;
}
public Builder kernelSize(int... kernelSize) {
return kernelSize(new FixedValue<>(kernelSize));
}
public Builder kernelSize(ParameterSpace kernelSize) {
this.kernelSize = kernelSize;
return this;
}
public Builder stride(int... stride) {
return stride(new FixedValue(stride));
}
public Builder stride(ParameterSpace stride) {
this.stride = stride;
return this;
}
public Builder padding(int... padding) {
return padding(new FixedValue(padding));
}
public Builder padding(ParameterSpace padding) {
this.padding = padding;
return this;
}
public Builder pnorm(int pnorm){
return pnorm(new FixedValue<>(pnorm));
}
public Builder pnorm(ParameterSpace pnorm){
this.pnorm = pnorm;
return this;
}
public Builder eps(double eps){
return eps(new FixedValue<>(eps));
}
public Builder eps(ParameterSpace eps){
this.eps = eps;
return this;
}
@SuppressWarnings("unchecked")
public SubsamplingLayerSpace build() {
return new SubsamplingLayerSpace(this);
}
}
}