com.simiacryptus.mindseye.layers.java.BiasMetaLayer 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 Bias meta key.
*/
@SuppressWarnings("serial")
public class BiasMetaLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(BiasMetaLayer.class);
/**
* Instantiates a new Bias meta key.
*/
public BiasMetaLayer() {
}
/**
* Instantiates a new Bias meta key.
*
* @param id the id
*/
protected BiasMetaLayer(@Nonnull final JsonObject id) {
super(id);
}
/**
* From json bias meta key.
*
* @param json the json
* @param rs the rs
* @return the bias meta key
*/
public static BiasMetaLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new BiasMetaLayer(json);
}
@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
final int itemCnt = inObj[0].getData().length();
Tensor tensor1 = inObj[1].getData().get(0);
final Tensor[] tensors = IntStream.range(0, itemCnt)
.parallel()
.mapToObj(dataIndex -> {
Tensor tensor = inObj[0].getData().get(dataIndex);
Tensor mapIndex = tensor.mapIndex((v, c) -> {
return v + tensor1.get(c);
});
tensor.freeRef();
return mapIndex;
})
.toArray(i -> new Tensor[i]);
tensor1.freeRef();
Tensor tensor0 = tensors[0];
tensor0.addRef();
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
return new Result(TensorArray.wrap(tensors), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList data) -> {
if (inObj[0].isAlive()) {
data.addRef();
inObj[0].accumulate(buffer, data);
}
if (inObj[1].isAlive()) {
@Nonnull final ToDoubleFunction f = (c) -> {
return IntStream.range(0, itemCnt).mapToDouble(i -> {
Tensor tensor = data.get(i);
double v = tensor.get(c);
tensor.freeRef();
return v;
}).sum();
};
@Nullable final Tensor passback = tensor0.mapCoords(f);
@Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, inObj[1].getData().length())
.mapToObj(i -> {
if (i == 0) return passback;
else {
@Nullable Tensor map = passback.map(v -> 0);
passback.freeRef();
return map;
}
}).toArray(i -> new Tensor[i]));
inObj[1].accumulate(buffer, tensorArray);
}
}) {
@Override
protected void _free() {
tensor0.freeRef();
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
return inObj[0].isAlive() || inObj[1].isAlive();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
return super.getJsonStub();
}
@Nonnull
@Override
public List state() {
return Arrays.asList();
}
}