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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.RecycleBin;
import com.simiacryptus.ref.lang.RefUtil;
import com.simiacryptus.ref.wrappers.RefArrays;
import com.simiacryptus.ref.wrappers.RefList;
import com.simiacryptus.util.FastRandom;
import com.simiacryptus.util.Util;
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.Consumer;
import java.util.function.DoubleSupplier;
import java.util.function.IntToDoubleFunction;
/**
* The type Bias layer.
*/
@SuppressWarnings("serial")
public class BiasLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(BiasLayer.class);
/**
* The Bias.
*/
@Nullable
public final Tensor bias;
/**
* Instantiates a new Bias layer.
*/
protected BiasLayer() {
super();
bias = null;
}
/**
* Instantiates a new Bias layer.
*
* @param dims the dims
*/
public BiasLayer(final int... dims) {
bias = new Tensor(dims);
}
/**
* Instantiates a new Bias layer.
*
* @param json the json
* @param rs the rs
*/
protected BiasLayer(@Nonnull final JsonObject json, Map rs) {
super(json);
bias = Tensor.fromJson(json.get("bias"), rs);
}
/**
* Sets weights.
*
* @param f the f
*/
public void setWeights(@Nonnull IntToDoubleFunction f) {
assert this.bias != null;
double[] bias = this.bias.getData();
for (int i = 0; i < bias.length; i++) {
bias[i] = f.applyAsDouble(i);
}
}
/**
* Sets weights log.
*
* @param value the value
*/
public void setWeightsLog(double value) {
assert this.bias != null;
double[] bias = this.bias.getData();
for (int i = 0; i < bias.length; i++) {
bias[i] = (FastRandom.INSTANCE.random() - 0.5) * Math.pow(10, value);
}
}
/**
* From json bias layer.
*
* @param json the json
* @param rs the rs
* @return the bias layer
*/
@Nonnull
@SuppressWarnings("unused")
public static BiasLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new BiasLayer(json, rs);
}
/**
* Add double [ ].
*
* @param input the input
* @return the double [ ]
*/
public double[] add(@Nonnull final double[] input) {
final double[] array = RecycleBin.DOUBLES.obtain(input.length);
assert this.bias != null;
double[] bias = this.bias.getData();
if (1 == bias.length) {
for (int i = 0; i < array.length; i++) {
array[i] = input[i] + bias[0];
}
} else {
for (int i = 0; i < array.length; i++) {
array[i] = input[i] + bias[i];
}
}
return array;
}
/**
* Add weights.
*
* @param f the f
*/
public void addWeights(@Nonnull DoubleSupplier f) {
assert this.bias != null;
Util.add(f, this.bias.getData());
}
@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
TensorList input = first(RefUtil.addRef(inObj));
TensorArray data = fwd(input);
boolean alive = 0 < inObj.length && inObj[0].isAlive();
final Result.Accumulator accumulator1 = inObj[0].getAccumulator();
final boolean alive1 = inObj[0].isAlive();
Accumulator accumulator = new Accumulator(bias.addRef(), isFrozen(), getId(), accumulator1, alive1);
RefUtil.freeRef(inObj);
return new Result(data, accumulator, alive || !isFrozen());
}
/**
* First tensor list.
*
* @param inObj the in obj
* @return the tensor list
*/
@NotNull
public TensorList first(@Nonnull Result[] inObj) {
try {
if (0 == inObj.length) {
return new TensorArray();
} else {
return inObj[0].getData();
}
} finally {
RefUtil.freeRef(inObj);
}
}
@Nonnull
@Override
public JsonObject getJson(Map resources, @Nonnull DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
assert bias != null;
json.add("bias", bias.getJson(resources, dataSerializer));
return json;
}
/**
* Set.
*
* @param ds the ds
*/
public void set(@Nonnull double[] ds) {
assert this.bias != null;
double[] bias = this.bias.getData();
for (int i = 0; i < ds.length; i++) {
bias[i] = ds[i];
}
}
@Nonnull
@Override
public RefList state() {
assert bias != null;
return RefArrays.asList(bias.getData());
}
/**
* Set.
*
* @param tensor the tensor
*/
public void set(@Nonnull Tensor tensor) {
assert this.bias != null;
double[] bias = this.bias.getData();
assert bias.length == tensor.length();
for (int i = 0; i < bias.length; i++) {
bias[i] = tensor.get(i);
}
tensor.freeRef();
}
public void _free() {
if (null != bias)
bias.freeRef();
super._free();
}
@Nonnull
public @Override
@SuppressWarnings("unused")
BiasLayer addRef() {
return (BiasLayer) super.addRef();
}
@NotNull
private TensorArray fwd(TensorList input) {
try {
return new TensorArray(input.stream().parallel().map(r -> {
Tensor tensor = new Tensor(add(r.getData()), r.getDimensions());
r.freeRef();
return tensor;
}).toArray(Tensor[]::new));
} finally {
input.freeRef();
}
}
private static class Accumulator extends Result.Accumulator {
private boolean frozen;
private Tensor bias;
private UUID id;
private Result.Accumulator accumulator;
private boolean alive;
/**
* Instantiates a new Accumulator.
*
* @param bias the bias
* @param frozen the frozen
* @param id the id
* @param accumulator the accumulator
* @param alive the alive
*/
public Accumulator(Tensor bias, boolean frozen, UUID id, Result.Accumulator accumulator, boolean alive) {
this.frozen = frozen;
this.bias = bias;
this.id = id;
this.accumulator = accumulator;
this.alive = alive;
}
@Override
public void accept(@Nonnull DeltaSet buffer, @Nonnull TensorList delta) {
if (!frozen) {
final Delta deltaBuffer = buffer.get(id, bias == null ? null : bias.addRef());
assert bias != null;
if (1 == bias.length()) {
delta.stream().parallel().forEach(RefUtil.wrapInterface((Consumer) d -> {
@Nullable final double[] array = d.getData();
d.freeRef();
assert deltaBuffer != null;
final double[] data = 1 == array.length ? array : new double[]{RefArrays.stream(array).sum()};
deltaBuffer.addInPlace(data);
}, deltaBuffer));
} else {
delta.stream().parallel().forEach(RefUtil.wrapInterface((Consumer) d -> {
assert deltaBuffer != null;
deltaBuffer.addInPlace(d.getData());
d.freeRef();
}, deltaBuffer));
}
}
if (alive) {
this.accumulator.accept(buffer.addRef(), delta.addRef());
}
delta.freeRef();
buffer.freeRef();
}
public @SuppressWarnings("unused")
void _free() {
super._free();
accumulator.freeRef();
;
assert bias != null;
bias.freeRef();
}
}
}