com.simiacryptus.mindseye.layers.java.ImgBandBiasLayer 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.lang.ref.RecycleBin;
import com.simiacryptus.mindseye.lang.*;
import com.simiacryptus.util.FastRandom;
import com.simiacryptus.util.JsonUtil;
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 per-color-band value offset to the single tensor input.
*/
@SuppressWarnings("serial")
public class ImgBandBiasLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(ImgBandBiasLayer.class);
@Nullable
private final double[] bias;
/**
* Instantiates a new Img band bias key.
*/
protected ImgBandBiasLayer() {
super();
bias = null;
}
/**
* Instantiates a new Img band bias key.
*
* @param bands the bands
*/
public ImgBandBiasLayer(final int bands) {
super();
bias = new double[bands];
}
/**
* Instantiates a new Img band bias key.
*
* @param json the json
*/
protected ImgBandBiasLayer(@Nonnull final JsonObject json) {
super(json);
bias = JsonUtil.getDoubleArray(json.getAsJsonArray("bias"));
}
/**
* From json img band bias key.
*
* @param json the json
* @param rs the rs
* @return the img band bias key
*/
public static ImgBandBiasLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new ImgBandBiasLayer(json);
}
/**
* Add double [ ].
*
* @param input the input
* @return the double [ ]
*/
@Nonnull
public double[] add(@Nonnull final double[] input) {
assert Arrays.stream(input).allMatch(v -> Double.isFinite(v));
assert null != input;
@Nullable final double[] bias = getBias();
assert null != bias;
if (input.length % bias.length != 0) throw new IllegalArgumentException();
@Nonnull final double[] array = new double[input.length];
final int size = input.length / bias.length;
for (int i = 0; i < array.length; i++) {
array[i] = input[i] + bias[i / size];
}
assert Arrays.stream(array).allMatch(v -> Double.isFinite(v));
return array;
}
/**
* Add weights img band bias key.
*
* @param f the f
* @return the img band bias key
*/
@Nonnull
public ImgBandBiasLayer addWeights(@Nonnull final DoubleSupplier f) {
Util.add(f, getBias());
return this;
}
@Nonnull
@Override
public Result eval(final Result... inObj) {
return eval(inObj[0]);
}
/**
* Eval nn result.
*
* @param input the input
* @return the nn result
*/
@Nonnull
public Result eval(@Nonnull final Result input) {
@Nullable final double[] bias = getBias();
input.addRef();
return new Result(TensorArray.wrap(input.getData().stream().parallel()
.map(r -> {
if (r.getDimensions().length != 3) {
throw new IllegalArgumentException(Arrays.toString(r.getDimensions()));
}
if (r.getDimensions()[2] != bias.length) {
throw new IllegalArgumentException(String.format("%s: %s does not have %s bands",
getName(), Arrays.toString(r.getDimensions()), bias.length));
}
@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 data) -> {
if (!isFrozen()) {
final Delta deltaBuffer = buffer.get(ImgBandBiasLayer.this.getId(), bias);
data.stream().parallel().forEach(d -> {
final double[] array = RecycleBin.DOUBLES.obtain(bias.length);
@Nullable final double[] signal = d.getData();
final int size = signal.length / bias.length;
for (int i = 0; i < signal.length; i++) {
array[i / size] += signal[i];
if (!Double.isFinite(array[i / size])) {
array[i / size] = 0.0;
}
}
d.freeRef();
assert Arrays.stream(array).allMatch(v -> Double.isFinite(v));
deltaBuffer.addInPlace(array);
RecycleBin.DOUBLES.recycle(array, array.length);
});
deltaBuffer.freeRef();
}
if (input.isAlive()) {
data.addRef();
input.accumulate(buffer, data);
}
data.freeRef();
}) {
@Override
protected void _free() {
input.freeRef();
}
@Override
public boolean isAlive() {
return input.isAlive() || !isFrozen();
}
};
}
/**
* Get bias double [ ].
*
* @return the double [ ]
*/
@Nullable
public double[] getBias() {
if (!Arrays.stream(bias).allMatch(v -> Double.isFinite(v))) {
throw new IllegalStateException(Arrays.toString(bias));
}
return bias;
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
json.add("bias", JsonUtil.getJson(getBias()));
return json;
}
/**
* Set nn key.
*
* @param ds the ds
* @return the nn key
*/
@Nonnull
public Layer set(@Nonnull final double[] ds) {
@Nullable final double[] bias = getBias();
for (int i = 0; i < ds.length; i++) {
bias[i] = ds[i];
}
assert Arrays.stream(bias).allMatch(v -> Double.isFinite(v));
return this;
}
/**
* Sets weights.
*
* @param f the f
* @return the weights
*/
@Nonnull
public ImgBandBiasLayer setWeights(@Nonnull final IntToDoubleFunction f) {
@Nullable final double[] bias = getBias();
for (int i = 0; i < bias.length; i++) {
bias[i] = f.applyAsDouble(i);
}
assert Arrays.stream(bias).allMatch(v -> Double.isFinite(v));
return this;
}
@Nonnull
@Override
public List state() {
return Arrays.asList(getBias());
}
/**
* Sets weights log.
*
* @param value the value
* @return the weights log
*/
@Nonnull
public ImgBandBiasLayer setWeightsLog(final double value) {
for (int i = 0; i < bias.length; i++) {
bias[i] = (FastRandom.INSTANCE.random() - 0.5) * Math.pow(10, value);
}
return this;
}
/**
* Sets and free.
*
* @param tensor the tensor
* @return the and free
*/
public ImgBandBiasLayer setAndFree(final Tensor tensor) {
set(tensor.getData());
tensor.freeRef();
return this;
}
/**
* Set img band bias key.
*
* @param tensor the tensor
* @return the img band bias key
*/
public ImgBandBiasLayer set(final Tensor tensor) {
return (ImgBandBiasLayer) set(tensor.getData());
}
}