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Testing Tools for Neural Network 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.test.unit;
import com.simiacryptus.lang.ref.ReferenceCounting;
import com.simiacryptus.mindseye.lang.Layer;
import com.simiacryptus.mindseye.lang.Tensor;
import com.simiacryptus.mindseye.test.SimpleEval;
import com.simiacryptus.mindseye.test.ToleranceStatistics;
import com.simiacryptus.notebook.NotebookOutput;
import com.simiacryptus.util.data.DoubleStatistics;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Arrays;
import java.util.HashMap;
/**
* The type Reference io.
*/
public class ReferenceIO extends ComponentTestBase {
/**
* The Reference io.
*/
HashMap referenceIO;
/**
* Instantiates a new Reference io.
*
* @param referenceIO the reference io
*/
public ReferenceIO(final HashMap referenceIO) {
this.referenceIO = referenceIO;
}
@Override
protected void _free() {
referenceIO.keySet().stream().flatMap(x -> Arrays.stream(x)).forEach(ReferenceCounting::freeRef);
referenceIO.values().forEach(ReferenceCounting::freeRef);
super._free();
}
@Nullable
@Override
public ToleranceStatistics test(@Nonnull final NotebookOutput log, @Nonnull final Layer layer, @Nonnull final Tensor... inputPrototype) {
if (!referenceIO.isEmpty()) {
log.h1("Reference Input/Output Pairs");
log.p("Display pre-setBytes input/output example pairs:");
referenceIO.forEach((input, output) -> {
log.eval(() -> {
@Nonnull final SimpleEval eval = SimpleEval.run(layer, input);
Tensor evalOutput = eval.getOutput();
Tensor difference = output.scale(-1).addAndFree(evalOutput);
@Nonnull final DoubleStatistics error = new DoubleStatistics().accept(difference.getData());
String format = String.format("--------------------\nInput: \n[%s]\n--------------------\nOutput: \n%s\n%s\nError: %s\n--------------------\nDerivative: \n%s",
Arrays.stream(input).map(t -> Arrays.toString(t.getDimensions()) + "\n" + t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).get(),
Arrays.toString(evalOutput.getDimensions()),
evalOutput.prettyPrint(),
error,
Arrays.stream(eval.getDerivative()).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).get());
difference.freeRef();
eval.freeRef();
return format;
});
});
} else {
log.h1("Example Input/Output Pair");
log.p("Display input/output pairs from random executions:");
log.eval(() -> {
@Nonnull final SimpleEval eval = SimpleEval.run(layer, inputPrototype);
Tensor evalOutput = eval.getOutput();
String format = String.format("--------------------\nInput: \n[%s]\n--------------------\nOutput: \n%s\n%s\n--------------------\nDerivative: \n%s",
Arrays.stream(inputPrototype).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).orElse(""),
Arrays.toString(evalOutput.getDimensions()),
evalOutput.prettyPrint(),
Arrays.stream(eval.getDerivative()).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).orElse(""));
eval.freeRef();
return format;
});
}
return null;
}
@Nonnull
@Override
public String toString() {
return "ReferenceIO{" +
"referenceIO=" + referenceIO +
'}';
}
}
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