com.simiacryptus.mindseye.layers.java.SumMetaLayer Maven / Gradle / Ivy
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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.function.ToDoubleFunction;
import java.util.stream.IntStream;
/**
* The type Sum meta key.
*/
@SuppressWarnings("serial")
public class SumMetaLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(SumMetaLayer.class);
@Nullable
private Tensor lastResult;
private int minBatches = 1;
/**
* Instantiates a new Sum meta key.
*/
public SumMetaLayer() {
}
/**
* Instantiates a new Sum meta key.
*
* @param json the id
* @param resources the resources
*/
protected SumMetaLayer(@Nonnull final JsonObject json, Map resources) {
super(json);
lastResult = Tensor.fromJson(json.get("lastResult"), resources);
minBatches = json.get("minBatches").getAsInt();
}
/**
* From json sum meta key.
*
* @param json the json
* @param rs the rs
* @return the sum meta key
*/
public static SumMetaLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new SumMetaLayer(json, rs);
}
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
if (1 != inObj.length) throw new IllegalArgumentException();
final Result input = inObj[0];
TensorList inputData = input.getData();
final int itemCnt = inputData.length();
if (null == lastResult || minBatches < itemCnt) {
if(null != lastResult) lastResult.freeRef();
@Nonnull final ToDoubleFunction f = (c) ->
IntStream.range(0, itemCnt)
.mapToDouble(dataIndex -> {
Tensor tensor = inputData.get(dataIndex);
double v = tensor.get(c);
tensor.freeRef();
return v;
})
.sum();
lastResult = inputData.get(0).mapCoordsAndFree(f);
}
return new Result(TensorArray.create(lastResult), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList data) -> {
if (input.isAlive()) {
@Nullable final Tensor delta = data.get(0);
@Nonnull final Tensor feedback[] = new Tensor[itemCnt];
Arrays.parallelSetAll(feedback, i -> new Tensor(delta.getDimensions()));
delta.coordStream(false).forEach((inputCoord) -> {
for (int inputItem = 0; inputItem < itemCnt; inputItem++) {
feedback[inputItem].add(inputCoord, delta.get(inputCoord));
}
});
@Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
input.accumulate(buffer, tensorArray);
}
}) {
@Override
protected void _free() {
inputData.freeRef();
input.freeRef();
}
@Override
public boolean isAlive() {
return input.isAlive();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, @Nonnull DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
if (null != lastResult) {
json.add("lastResult", lastResult.toJson(resources, dataSerializer));
}
json.addProperty("minBatches", minBatches);
return json;
}
/**
* Gets min batches.
*
* @return the min batches
*/
public int getMinBatches() {
return minBatches;
}
/**
* Sets min batches.
*
* @param minBatches the min batches
* @return the min batches
*/
@Nonnull
public SumMetaLayer setMinBatches(final int minBatches) {
this.minBatches = minBatches;
return this;
}
@Nonnull
@Override
public List state() {
return Arrays.asList();
}
}