com.simiacryptus.mindseye.layers.java.CrossDotMetaLayer 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;
/**
* The type Cross dot meta key.
*/
@SuppressWarnings("serial")
public class CrossDotMetaLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(CrossDotMetaLayer.class);
/**
* Instantiates a new Cross dot meta key.
*/
public CrossDotMetaLayer() {
}
/**
* Instantiates a new Cross dot meta key.
*
* @param id the id
*/
protected CrossDotMetaLayer(@Nonnull final JsonObject id) {
super(id);
}
/**
* From json cross dot meta key.
*
* @param json the json
* @param rs the rs
* @return the cross dot meta key
*/
public static CrossDotMetaLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new CrossDotMetaLayer(json);
}
@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
final Result input = inObj[0];
final TensorList indata = input.getData();
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
indata.addRef();
final int itemCnt = indata.length();
final int dim = Tensor.length(indata.getDimensions());
@Nonnull final Tensor results = new Tensor(dim, dim);
for (int i = 0; i < dim; i++) {
for (int j = 0; j < dim; j++) {
if (i == j) {
continue;
}
double v = 0;
for (int k = 0; k < itemCnt; k++) {
Tensor tensor = indata.get(k);
@Nullable final double[] kk = tensor.getData();
v += kk[i] * kk[j];
tensor.freeRef();
}
results.set(new int[]{i, j}, v);
}
}
return new Result(TensorArray.wrap(results), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList delta) -> {
if (input.isAlive()) {
@Nullable final Tensor deltaTensor = delta.get(0);
@Nonnull final Tensor feedback[] = new Tensor[itemCnt];
Arrays.parallelSetAll(feedback, i -> new Tensor(dim));
for (int i = 0; i < dim; i++) {
for (int j = 0; j < dim; j++) {
if (i == j) {
continue;
}
final double v = deltaTensor.get(i, j);
for (int k = 0; k < itemCnt; k++) {
Tensor tensor = indata.get(k);
@Nullable final double[] kk = tensor.getData();
feedback[k].add(i, v * kk[j]);
feedback[k].add(j, v * kk[i]);
tensor.freeRef();
}
}
}
deltaTensor.freeRef();
@Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
input.accumulate(buffer, tensorArray);
}
}) {
@Override
protected void _free() {
indata.freeRef();
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
return input.isAlive();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
return super.getJsonStub();
}
@Nonnull
@Override
public List state() {
return Arrays.asList();
}
}