com.simiacryptus.mindseye.layers.java.SumReducerLayer Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of mindseye-java Show documentation
Show all versions of mindseye-java Show documentation
Pure Java Neural Networks Components
/*
* Copyright (c) 2019 by Andrew Charneski.
*
* The author licenses this file to you 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.simiacryptus.mindseye.layers.java;
import com.google.gson.JsonObject;
import com.simiacryptus.mindseye.lang.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.UUID;
import java.util.stream.IntStream;
/**
* Sums all input values to produce a single-element output.
*/
@SuppressWarnings("serial")
public class SumReducerLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(SumReducerLayer.class);
/**
* Instantiates a new Sum reducer key.
*/
public SumReducerLayer() {
}
/**
* Instantiates a new Sum reducer key.
*
* @param id the id
*/
protected SumReducerLayer(@Nonnull final JsonObject id) {
super(id);
}
/**
* From json sum reducer key.
*
* @param json the json
* @param rs the rs
* @return the sum reducer key
*/
public static SumReducerLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new SumReducerLayer(json);
}
@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
Arrays.stream(inObj).forEach(x -> x.getData().addRef());
return new Result(TensorArray.wrap(IntStream.range(0, inObj[0].getData().length()).parallel().mapToDouble(dataIndex -> {
double sum = 0;
for (@Nonnull final Result element : inObj) {
@Nullable Tensor tensor = element.getData().get(dataIndex);
@Nullable final double[] input = tensor.getData();
for (final double element2 : input) {
sum += element2;
}
tensor.freeRef();
}
return sum;
}).mapToObj(x -> new Tensor(new double[]{x}, new int[]{1})).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList data) -> {
for (@Nonnull final Result in_l : inObj) {
if (in_l.isAlive()) {
@Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, in_l.getData().length()).parallel().mapToObj(dataIndex -> {
Tensor tensor = data.get(dataIndex);
assert 1 == tensor.length() : Arrays.toString(tensor.getDimensions());
@Nonnull final Tensor passback = new Tensor(in_l.getData().getDimensions());
for (int i = 0; i < Tensor.length(in_l.getData().getDimensions()); i++) {
passback.set(i, tensor.get(0));
}
tensor.freeRef();
return passback;
}).toArray(i -> new Tensor[i]));
in_l.accumulate(buffer, tensorArray);
}
}
}) {
@Override
protected void _free() {
Arrays.stream(inObj).map(Result::getData).forEach(ReferenceCounting::freeRef);
Arrays.stream(inObj).forEach(ReferenceCountingBase::freeRef);
}
@Override
public boolean isAlive() {
for (@Nonnull final Result element : inObj)
if (element.isAlive()) {
return true;
}
return false;
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
return super.getJsonStub();
}
@Nonnull
@Override
public List state() {
return Arrays.asList();
}
}