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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 com.simiacryptus.util.FastRandom;
import com.simiacryptus.util.Util;
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.DoubleSupplier;
import java.util.function.IntToDoubleFunction;
/**
* Adds a bias tensor to the input. Expects a single input of the same dimension as the bias tensor.
*/
@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 key.
*/
protected BiasLayer() {
super();
bias = null;
}
/**
* Instantiates a new Bias key.
*
* @param dims the dims
*/
public BiasLayer(final int... dims) {
bias = new Tensor(dims);
}
/**
* Instantiates a new Bias key.
*
* @param json the json
* @param rs
*/
protected BiasLayer(@Nonnull final JsonObject json, Map rs) {
super(json);
bias = Tensor.fromJson(json.get("bias"),rs);
}
/**
* From json bias key.
*
* @param json the json
* @param rs the rs
* @return the bias key
*/
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);
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 bias key.
*
* @param f the f
* @return the bias key
*/
@Nonnull
public BiasLayer addWeights(@Nonnull final DoubleSupplier f) {
double[] bias = this.bias.getData();
Util.add(f, bias);
return this;
}
@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
TensorList input;
if (0 == inObj.length) {
input = TensorArray.create();
} else {
input = inObj[0].getData();
}
return new Result(TensorArray.wrap(input.stream().parallel()
.map(r -> {
@Nonnull Tensor tensor = new Tensor(add(r.getData()), r.getDimensions());
r.freeRef();
return tensor;
}).toArray(i -> new Tensor[i])),
(@Nonnull final DeltaSet buffer, @Nonnull final TensorList delta) -> {
if (!isFrozen()) {
final Delta deltaBuffer = buffer.get(BiasLayer.this.getId(), bias);
if (1 == bias.length()) {
delta.stream().parallel().forEach(d -> {
@Nullable final double[] array = d.getData();
deltaBuffer.addInPlace(1 == array.length ? array : new double[]{Arrays.stream(array).sum()});
d.freeRef();
});
} else {
delta.stream().parallel().forEach(d -> {
deltaBuffer.addInPlace(d.getData());
d.freeRef();
});
}
deltaBuffer.freeRef();
}
if (0 < inObj.length && inObj[0].isAlive()) {
delta.addRef();
inObj[0].accumulate(buffer, delta);
}
}) {
@Override
protected void _free() {
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
return 0 < inObj.length && inObj[0].isAlive() || !isFrozen();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
json.add("bias", bias.toJson(resources, dataSerializer));
return json;
}
/**
* Set nn key.
*
* @param ds the ds
* @return the nn key
*/
@Nonnull
public Layer set(@Nonnull final double[] ds) {
double[] bias = this.bias.getData();
for (int i = 0; i < ds.length; i++) {
bias[i] = ds[i];
}
return this;
}
/**
* Sets weights.
*
* @param f the f
* @return the weights
*/
@Nonnull
public BiasLayer setWeights(@Nonnull final IntToDoubleFunction f) {
double[] bias = this.bias.getData();
for (int i = 0; i < bias.length; i++) {
bias[i] = f.applyAsDouble(i);
}
return this;
}
/**
* Sets weights log.
*
* @param value the value
* @return the weights log
*/
@Nonnull
public BiasLayer setWeightsLog(final double value) {
double[] bias = this.bias.getData();
for (int i = 0; i < bias.length; i++) {
bias[i] = (FastRandom.INSTANCE.random() - 0.5) * Math.pow(10, value);
}
return this;
}
@Nonnull
@Override
public List state() {
return Arrays.asList(bias.getData());
}
/**
* Set bias key.
*
* @param tensor the tensor
* @return the bias key
*/
@Nonnull
public BiasLayer set(@Nonnull Tensor tensor) {
double[] bias = this.bias.getData();
assert bias.length == tensor.length();
for (int i = 0; i < bias.length; i++) {
bias[i] = tensor.get(i);
}
return this;
}
}