com.simiacryptus.mindseye.layers.java.ProductLayer 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.lang.ref.ReferenceCounting;
import com.simiacryptus.lang.ref.ReferenceCountingBase;
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;
/**
* The type Product key.
*/
@SuppressWarnings("serial")
public class ProductLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(ProductLayer.class);
/**
* Instantiates a new Product key.
*/
public ProductLayer() {
}
/**
* Instantiates a new Product key.
*
* @param id the id
*/
protected ProductLayer(@Nonnull final JsonObject id) {
super(id);
}
/**
* From json product key.
*
* @param json the json
* @param rs the rs
* @return the product key
*/
public static ProductLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new ProductLayer(json);
}
@Nonnull
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
final Result in0 = inObj[0];
assert Arrays.stream(inObj).mapToInt(x -> x.getData().length()).distinct().count() == 1 : Arrays.toString(Arrays.stream(inObj).mapToInt(x -> x.getData().length()).toArray());
@Nonnull final double[] sum_A = new double[in0.getData().length()];
final Tensor[] outputA = IntStream.range(0, in0.getData().length()).mapToObj(dataIndex -> {
double sum = 1;
for (@Nonnull final Result input : inObj) {
Tensor tensor = input.getData().get(dataIndex);
@Nullable final double[] tensorData = tensor.getData();
for (final double element2 : tensorData) {
sum *= element2;
}
tensor.freeRef();
}
sum_A[dataIndex] = sum;
return new Tensor(new double[]{sum}, 1);
}).toArray(i -> new Tensor[i]);
return new Result(TensorArray.wrap(outputA), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList delta) -> {
for (@Nonnull final Result input : inObj) {
if (input.isAlive()) {
TensorList data = input.getData();
input.accumulate(buffer, TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
Tensor dataTensor = delta.get(dataIndex);
Tensor lTensor = data.get(dataIndex);
@Nonnull final Tensor passback = new Tensor(lTensor.getDimensions());
for (int i = 0; i < lTensor.length(); i++) {
double d = lTensor.getData()[i];
double deltaV = dataTensor.get(0);
passback.set(i, d == 0 ? 0 : (deltaV * sum_A[dataIndex] / d));
}
dataTensor.freeRef();
lTensor.freeRef();
return passback;
}).toArray(i -> new Tensor[i])));
}
}
delta.freeRef();
}) {
@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();
}
}