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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.ReferenceCounting;
import com.simiacryptus.mindseye.lang.*;
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.stream.IntStream;
/**
* A multi-purpose Nth-power exponential function. Has reasonably efficient specialized (pure java) implementations of
* many common signed rational values, such as +/-0.5, +/-1.0, 2.0, etc
*/
@SuppressWarnings("serial")
public final class NthPowerActivationLayer extends LayerBase {
private double power = 1.0;
/**
* Instantiates a new Nth power activation key.
*/
public NthPowerActivationLayer() {
}
/**
* Instantiates a new Nth power activation key.
*
* @param id the id
*/
protected NthPowerActivationLayer(@Nonnull final JsonObject id) {
super(id);
power = id.get("power").getAsDouble();
}
/**
* From json nth power activation key.
*
* @param json the json
* @param rs the rs
* @return the nth power activation key
*/
public static NthPowerActivationLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new NthPowerActivationLayer(json);
}
private static void nthPower(final double power, @Nonnull final Tensor input, final double[] inputData, final double[] gradientData, final double[] outputData) {
for (int i = 0; i < input.length(); i++) {
final double x = inputData[i];
final boolean isZero = Math.abs(x) < 1e-20;
double d = isZero ? 0.0 : power * Math.pow(x, power - 1);
double f = isZero ? 0.0 : Math.pow(x, power);
if (!Double.isFinite(d)) {
d = 0.0;
}
if (!Double.isFinite(f)) {
f = 0.0;
}
gradientData[i] = d;
outputData[i] = f;
}
}
private static void square(@Nonnull final Tensor input, final double[] inputData, final double[] gradientData, final double[] outputData) {
for (int i = 0; i < input.length(); i++) {
final double x = inputData[i];
gradientData[i] = 2 * x;
outputData[i] = x * x;
}
}
private static void squareRoot(@Nonnull final Tensor input, final double[] inputData, final double[] gradientData, final double[] outputData) {
for (int i = 0; i < input.length(); i++) {
final double x = inputData[i];
final boolean isZero = Math.abs(x) < 1e-20;
final double power = 0.5;
final double v = Math.pow(x, power);
double d = isZero ? 0.0 : power / v;
double f = isZero ? 0.0 : v;
if (!Double.isFinite(d)) {
d = 0.0;
}
if (!Double.isFinite(f)) {
f = 0.0;
}
gradientData[i] = d;
outputData[i] = f;
}
}
private static void unity(@Nonnull final Tensor input, final double[] inputData, final double[] gradientData, final double[] outputData) {
for (int i = 0; i < input.length(); i++) {
gradientData[i] = 0;
outputData[i] = 1;
}
}
@Override
public Result eval(@Nonnull final Result... inObj) {
final int itemCnt = inObj[0].getData().length();
assert 0 < itemCnt;
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
@Nonnull final Tensor inputGradientA[] = new Tensor[itemCnt];
return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).parallel().mapToObj(dataIndex -> {
@Nullable final Tensor input = inObj[0].getData().get(dataIndex);
@Nonnull final Tensor output = new Tensor(inObj[0].getData().getDimensions());
@Nonnull final Tensor gradient = new Tensor(input.length());
@Nullable final double[] inputData = input.getData();
@Nullable final double[] gradientData = gradient.getData();
@Nullable final double[] outputData = output.getData();
inputGradientA[dataIndex] = gradient;
if (power == 2) {
NthPowerActivationLayer.square(input, inputData, gradientData, outputData);
} else if (power == 0.5) {
NthPowerActivationLayer.squareRoot(input, inputData, gradientData, outputData);
} else if (power == 0.0) {
NthPowerActivationLayer.unity(input, inputData, gradientData, outputData);
} else {
NthPowerActivationLayer.nthPower(power, input, inputData, gradientData, outputData);
}
input.freeRef();
return output;
}).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList data) -> {
if (inObj[0].isAlive()) {
@Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, itemCnt).parallel().mapToObj(dataIndex -> {
@Nonnull final Tensor passback = new Tensor(data.getDimensions());
@Nullable final Tensor tensor = data.get(dataIndex);
@Nullable double[] tensorData = tensor.getData();
@Nullable final double[] gradientData = inputGradientA[dataIndex].getData();
IntStream.range(0, passback.length()).forEach(i -> {
final double v = gradientData[i];
if (Double.isFinite(v)) {
passback.set(i, tensorData[i] * v);
}
});
tensor.freeRef();
return passback;
}).toArray(i -> new Tensor[i]));
inObj[0].accumulate(buffer, tensorArray);
}
data.freeRef();
}) {
@Override
protected void _free() {
Arrays.stream(inObj).forEach(ReferenceCounting::freeRef);
Arrays.stream(inputGradientA).forEach(ReferenceCounting::freeRef);
}
@Override
public boolean isAlive() {
return 0.0 != power && inObj[0].isAlive();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
json.addProperty("power", power);
return json;
}
/**
* Gets power.
*
* @return the power
*/
public double getPower() {
return power;
}
/**
* Sets power.
*
* @param power the power
* @return the power
*/
@Nonnull
public NthPowerActivationLayer setPower(final double power) {
this.power = power;
return this;
}
@Override
public List state() {
return Arrays.asList();
}
}