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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF 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 opennlp.tools.dl;
import java.io.InputStream;
import java.nio.charset.StandardCharsets;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
import java.util.stream.Stream;
import org.apache.commons.io.IOUtils;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.params.ParameterizedTest;
import org.junit.jupiter.params.provider.Arguments;
import org.junit.jupiter.params.provider.MethodSource;
/**
* Tests for {@link StackedRNN}
*/
public class StackedRNNTest {
private final Random r = new Random(42);
private String text;
private List<String> words;
@BeforeEach
public void setUp() throws Exception {
try (InputStream stream = getClass().getResourceAsStream("/text/sentences.txt")) {
text = IOUtils.toString(stream, StandardCharsets.UTF_8);
words = Arrays.asList(text.split("\\s"));
}
}
private static Stream<Arguments> provideRNNParams() {
return Stream.of(
Arguments.of(1e-3f, 25, 50, 5)
);
}
@Disabled
// TODO check why this fails with:
// java.lang.IllegalStateException: Can't transpose array with rank < 2: array shape [62]
// ...
// on MacOS (only?)
@ParameterizedTest
@MethodSource("provideRNNParams")
public void testVanillaCharRNNLearn(float learningRate, int seqLength, int hiddenLayerSize, int epochs) throws Exception {
RNN rnn = new StackedRNN(learningRate, seqLength, hiddenLayerSize, epochs, text, 10, true, true);
evaluate(rnn, true);
rnn.serialize("target/scrnn-weights-");
}
private void evaluate(RNN rnn, boolean checkRatio) {
System.out.println(rnn);
rnn.learn();
double c = 0;
for (int i = 0; i < 2; i++) {
int seed = r.nextInt(rnn.getVocabSize());
String sample = rnn.sample(seed);
if (checkRatio && rnn.useChars) {
String[] sampleWords = sample.split(" ");
for (String sw : sampleWords) {
if (words.contains(sw)) {
c++;
}
}
if (c > 0) {
c /= sampleWords.length;
}
}
}
if (checkRatio && rnn.useChars) {
System.out.println("average correct word ratio: " + (c / 10d));
}
}
}