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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 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.stream.IntStream;
/**
* This activation key uses a parameterized hyperbolic function. This function, ion various parameterizations, can
* resemble: x^2, abs(x), x^3, x However, at high +/- x, the behavior is nearly linear.
*/
@SuppressWarnings("serial")
public class HyperbolicActivationLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(HyperbolicActivationLayer.class);
@Nullable
private final Tensor weights;
private int negativeMode = 1;
/**
* Instantiates a new Hyperbolic activation key.
*/
public HyperbolicActivationLayer() {
super();
weights = new Tensor(2);
weights.set(0, 1.);
weights.set(1, 1.);
}
/**
* Instantiates a new Hyperbolic activation key.
*
* @param json the json
* @param resources the resources
*/
protected HyperbolicActivationLayer(@Nonnull final JsonObject json, Map resources) {
super(json);
weights = Tensor.fromJson(json.get("weights"), resources);
negativeMode = json.getAsJsonPrimitive("negativeMode").getAsInt();
}
/**
* From json hyperbolic activation key.
*
* @param json the json
* @param rs the rs
* @return the hyperbolic activation key
*/
public static HyperbolicActivationLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new HyperbolicActivationLayer(json, rs);
}
@Override
protected void _free() {
weights.freeRef();
super._free();
}
@Nonnull
@Override
public Result eval(final Result... inObj) {
final TensorList indata = inObj[0].getData();
indata.addRef();
inObj[0].addRef();
weights.addRef();
HyperbolicActivationLayer.this.addRef();
final int itemCnt = indata.length();
return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
@Nullable final Tensor input = indata.get(dataIndex);
@Nullable Tensor map = input.map(v -> {
final int sign = v < 0 ? negativeMode : 1;
final double a = Math.max(0, weights.get(v < 0 ? 1 : 0));
return sign * (Math.sqrt(Math.pow(a * v, 2) + 1) - a) / a;
});
input.freeRef();
return map;
}).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList delta) -> {
if (!isFrozen()) {
IntStream.range(0, delta.length()).forEach(dataIndex -> {
@Nullable Tensor deltaI = delta.get(dataIndex);
@Nullable Tensor inputI = indata.get(dataIndex);
@Nullable final double[] deltaData = deltaI.getData();
@Nullable final double[] inputData = inputI.getData();
@Nonnull final Tensor weightDelta = new Tensor(weights.getDimensions());
for (int i = 0; i < deltaData.length; i++) {
final double d = deltaData[i];
final double x = inputData[i];
final int sign = x < 0 ? negativeMode : 1;
final double a = Math.max(0, weights.getData()[x < 0 ? 1 : 0]);
weightDelta.add(x < 0 ? 1 : 0, -sign * d / (a * a * Math.sqrt(1 + Math.pow(a * x, 2))));
}
deltaI.freeRef();
inputI.freeRef();
buffer.get(HyperbolicActivationLayer.this.getId(), weights.getData()).addInPlace(weightDelta.getData()).freeRef();
weightDelta.freeRef();
});
}
if (inObj[0].isAlive()) {
@Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
@Nullable Tensor inputTensor = indata.get(dataIndex);
Tensor deltaTensor = delta.get(dataIndex);
@Nullable final double[] deltaData = deltaTensor.getData();
@Nonnull final int[] dims = indata.getDimensions();
@Nonnull final Tensor passback = new Tensor(dims);
for (int i = 0; i < passback.length(); i++) {
final double x = inputTensor.getData()[i];
final double d = deltaData[i];
final int sign = x < 0 ? negativeMode : 1;
final double a = Math.max(0, weights.getData()[x < 0 ? 1 : 0]);
passback.set(i, sign * d * a * x / Math.sqrt(1 + a * x * a * x));
}
deltaTensor.freeRef();
inputTensor.freeRef();
return passback;
}).toArray(i -> new Tensor[i]));
inObj[0].accumulate(buffer, tensorArray);
}
delta.freeRef();
}) {
@Override
protected void _free() {
indata.freeRef();
inObj[0].freeRef();
weights.freeRef();
HyperbolicActivationLayer.this.freeRef();
}
@Override
public boolean isAlive() {
return inObj[0].isAlive() || !isFrozen();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, @Nonnull DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
json.add("weights", weights.getJson(resources, dataSerializer));
json.addProperty("negativeMode", negativeMode);
return json;
}
/**
* Gets scale l.
*
* @return the scale l
*/
public double getScaleL() {
return 1 / weights.get(1);
}
/**
* Gets scale r.
*
* @return the scale r
*/
public double getScaleR() {
return 1 / weights.get(0);
}
/**
* Sets mode asymetric.
*
* @return the mode asymetric
*/
@Nonnull
public HyperbolicActivationLayer setModeAsymetric() {
negativeMode = 0;
return this;
}
/**
* Sets mode even.
*
* @return the mode even
*/
@Nonnull
public HyperbolicActivationLayer setModeEven() {
negativeMode = 1;
return this;
}
/**
* Sets mode odd.
*
* @return the mode odd
*/
@Nonnull
public HyperbolicActivationLayer setModeOdd() {
negativeMode = -1;
return this;
}
/**
* Sets scale.
*
* @param scale the scale
* @return the scale
*/
@Nonnull
public HyperbolicActivationLayer setScale(final double scale) {
weights.set(0, 1 / scale);
weights.set(1, 1 / scale);
return this;
}
@Nonnull
@Override
public List state() {
return Arrays.asList(weights.getData());
}
}