com.simiacryptus.mindseye.layers.java.ImgPixelSoftmaxLayer 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.mindseye.lang.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.annotation.Nonnull;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.UUID;
import java.util.stream.IntStream;
/**
* Scales the input using per-color-band coefficients
*/
@SuppressWarnings("serial")
public class ImgPixelSoftmaxLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(ImgPixelSoftmaxLayer.class);
/**
* Instantiates a new Img band scale key.
*/
public ImgPixelSoftmaxLayer() {
super();
}
/**
* Instantiates a new Img band scale key.
*
* @param json the json
*/
protected ImgPixelSoftmaxLayer(@Nonnull final JsonObject json) {
super(json);
}
/**
* From json img band scale key.
*
* @param json the json
* @param rs the rs
* @return the img band scale key
*/
public static ImgPixelSoftmaxLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new ImgPixelSoftmaxLayer(json);
}
@Nonnull
@Override
public Result eval(final Result... inObj) {
assert 1 == inObj.length;
return eval(inObj[0]);
}
/**
* Eval nn result.
*
* @param input the input
* @return the nn result
*/
@Nonnull
public Result eval(@Nonnull final Result input) {
final TensorList inputData = input.getData();
inputData.addRef();
input.addRef();
int[] inputDims = inputData.getDimensions();
assert 3 == inputDims.length;
final int inputBands = inputDims[2];
final int width = inputDims[0];
final int height = inputDims[1];
TensorArray maxima = TensorArray.wrap(inputData.stream().map(inputTensor -> {
try {
return new Tensor(width, height, 1).setByCoord(c -> {
return IntStream.range(0, inputBands).mapToDouble(band -> {
int[] coords = c.getCoords();
return inputTensor.get(coords[0], coords[1], band);
}).max().getAsDouble();
});
} finally {
inputTensor.freeRef();
}
}).toArray(i -> new Tensor[i]));
TensorArray exps = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
final Tensor inputTensor = inputData.get(index);
Tensor maxTensor = maxima.get(index);
try {
return new Tensor(inputDims).setByCoord(c -> {
int[] coords = c.getCoords();
return Math.exp(inputTensor.get(c) - maxTensor.get(coords[0], coords[1], 0));
});
} finally {
inputTensor.freeRef();
maxTensor.freeRef();
}
}).toArray(i -> new Tensor[i]));
maxima.freeRef();
TensorArray sums = TensorArray.wrap(exps.stream().map(expTensor -> {
try {
return new Tensor(width, height, 1).setByCoord(c -> {
return IntStream.range(0, inputBands).mapToDouble(band -> {
int[] coords = c.getCoords();
return expTensor.get(coords[0], coords[1], band);
}).sum();
});
} finally {
expTensor.freeRef();
}
}).toArray(i -> new Tensor[i]));
TensorArray output = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
Tensor sumTensor = sums.get(index);
Tensor expTensor = exps.get(index);
try {
return new Tensor(inputDims).setByCoord(c -> {
int[] coords = c.getCoords();
return (expTensor.get(c) / sumTensor.get(coords[0], coords[1], 0));
});
} finally {
sumTensor.freeRef();
expTensor.freeRef();
}
}).toArray(i -> new Tensor[i]));
return new Result(output, (@Nonnull final DeltaSet buffer, @Nonnull final TensorList delta) -> {
if (input.isAlive()) {
TensorArray dots = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
final Tensor deltaTensor = delta.get(index);
Tensor expTensor = exps.get(index);
try {
return new Tensor(width, height, 1).setByCoord(c -> {
return IntStream.range(0, inputBands).mapToDouble(band -> {
int[] coords = c.getCoords();
return expTensor.get(coords[0], coords[1], band) * deltaTensor.get(coords[0], coords[1], band);
}).sum();
});
} finally {
expTensor.freeRef();
deltaTensor.freeRef();
}
}).toArray(i -> new Tensor[i]));
TensorArray passback = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
final Tensor deltaTensor = delta.get(index);
final Tensor expTensor = exps.get(index);
Tensor sumTensor = sums.get(index);
Tensor dotTensor = dots.get(index);
try {
return new Tensor(inputDims).setByCoord(c -> {
int[] coords = c.getCoords();
double sum = sumTensor.get(coords[0], coords[1], 0);
double dot = dotTensor.get(coords[0], coords[1], 0);
double deltaValue = deltaTensor.get(c);
double expValue = expTensor.get(c);
return (sum * deltaValue - dot) * expValue / (sum * sum);
});
} finally {
deltaTensor.freeRef();
expTensor.freeRef();
sumTensor.freeRef();
dotTensor.freeRef();
}
}).toArray(i -> new Tensor[i]));
input.accumulate(buffer, passback);
dots.freeRef();
}
delta.freeRef();
}) {
@Override
protected void _free() {
inputData.freeRef();
input.freeRef();
sums.freeRef();
exps.freeRef();
}
@Override
public boolean isAlive() {
return input.isAlive() || !isFrozen();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
return json;
}
@Nonnull
@Override
public List state() {
return Arrays.asList();
}
}