com.simiacryptus.mindseye.layers.java.BiasMetaLayer Maven / Gradle / Ivy
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/*
* 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 com.simiacryptus.ref.lang.RefUtil;
import com.simiacryptus.ref.wrappers.RefArrays;
import com.simiacryptus.ref.wrappers.RefIntStream;
import com.simiacryptus.ref.wrappers.RefList;
import org.jetbrains.annotations.NotNull;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Map;
import java.util.UUID;
import java.util.function.IntFunction;
/**
* The type Bias meta layer.
*/
@SuppressWarnings("serial")
public class BiasMetaLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(BiasMetaLayer.class);
/**
* Instantiates a new Bias meta layer.
*/
public BiasMetaLayer() {
}
/**
* Instantiates a new Bias meta layer.
*
* @param id the id
*/
protected BiasMetaLayer(@Nonnull final JsonObject id) {
super(id);
}
/**
* From json bias meta layer.
*
* @param json the json
* @param rs the rs
* @return the bias meta layer
*/
@Nonnull
@SuppressWarnings("unused")
public static BiasMetaLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new BiasMetaLayer(json);
}
@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
final Result in0 = inObj[0].addRef();
final Result in1 = inObj[1].addRef();
RefUtil.freeRef(inObj);
TensorList data0 = in0.getData();
final int itemCnt = data0.length();
TensorArray data = fwd(in1.addRef(), data0, itemCnt);
boolean alive = in0.isAlive() || in1.isAlive();
Result.Accumulator accumulator = new Accumulator(data.get(0), itemCnt, in0.getAccumulator(), in0.isAlive(), in1.getAccumulator(), in1.isAlive());
in0.freeRef();
in1.freeRef();
return new Result(data, accumulator, alive);
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
return super.getJsonStub();
}
@Nonnull
@Override
public RefList state() {
return RefArrays.asList();
}
public @SuppressWarnings("unused")
void _free() {
super._free();
}
@Nonnull
public @Override
@SuppressWarnings("unused")
BiasMetaLayer addRef() {
return (BiasMetaLayer) super.addRef();
}
@NotNull
private TensorArray fwd(Result in1, TensorList data0, int itemCnt) {
final TensorList data1 = in1.getData();
in1.freeRef();
Tensor tensor1 = data1.get(0);
data1.freeRef();
return new TensorArray(RefIntStream.range(0, itemCnt).parallel()
.mapToObj(RefUtil.wrapInterface((IntFunction extends Tensor>) dataIndex -> {
Tensor tensor = data0.get(dataIndex);
Tensor temp_48_0003 = tensor.mapIndex(RefUtil.wrapInterface((v, c) -> {
return v + tensor1.get(c);
}, tensor1.addRef()));
tensor.freeRef();
return temp_48_0003;
}, tensor1, data0))
.toArray(Tensor[]::new));
}
private static class Accumulator extends Result.Accumulator {
private final Tensor tensor0;
private final int itemCnt;
private Result.Accumulator accumulator1;
private boolean alive1;
private Result.Accumulator accumulator0;
private boolean alive0;
/**
* Instantiates a new Accumulator.
*
* @param tensor0 the tensor 0
* @param itemCnt the item cnt
* @param accumulator0 the accumulator 0
* @param alive0 the alive 0
* @param accumulator1 the accumulator 1
* @param alive1 the alive 1
*/
public Accumulator(Tensor tensor0, int itemCnt, Result.Accumulator accumulator0, boolean alive0, Result.Accumulator accumulator1, boolean alive1) {
this.tensor0 = tensor0;
this.itemCnt = itemCnt;
this.accumulator1 = accumulator1;
this.alive1 = alive1;
this.accumulator0 = accumulator0;
this.alive0 = alive0;
}
@Override
public void accept(@Nullable DeltaSet buffer, @Nonnull TensorList data) {
if (alive1) {
@Nonnull
TensorArray tensorArray = new TensorArray(RefIntStream.range(0, data.length())
.mapToObj(RefUtil.wrapInterface((IntFunction extends Tensor>) i -> {
if (i == 0)
return tensor0.mapCoords(RefUtil.wrapInterface(c -> {
return RefIntStream.range(0, itemCnt).mapToDouble(RefUtil.wrapInterface(j -> {
Tensor tensor = data.get(j);
double temp_48_0006 = tensor.get(c);
tensor.freeRef();
return temp_48_0006;
}, data.addRef())).sum();
}, data.addRef()));
else {
return tensor0.mapCoords(v -> 0);
}
}, data.addRef(), tensor0.addRef()))
.toArray(Tensor[]::new));
DeltaSet buffer1 = buffer == null ? null : buffer.addRef();
accumulator1.accept(buffer1, tensorArray);
}
if (alive0) {
DeltaSet buffer1 = buffer == null ? null : buffer.addRef();
accumulator0.accept(buffer1, data.addRef());
}
data.freeRef();
if (null != buffer)
buffer.freeRef();
}
public @SuppressWarnings("unused")
void _free() {
super._free();
accumulator0.freeRef();
accumulator1.freeRef();
tensor0.freeRef();
}
}
}