org.deeplearning4j.nn.conf.distribution.serde.LegacyDistributionDeserializer Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * information regarding copyright ownership.
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* * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.conf.distribution.serde;
import org.deeplearning4j.nn.conf.distribution.*;
import org.nd4j.shade.jackson.core.JsonParseException;
import org.nd4j.shade.jackson.core.JsonParser;
import org.nd4j.shade.jackson.core.JsonProcessingException;
import org.nd4j.shade.jackson.databind.DeserializationContext;
import org.nd4j.shade.jackson.databind.JsonDeserializer;
import org.nd4j.shade.jackson.databind.JsonNode;
import java.io.IOException;
public class LegacyDistributionDeserializer extends JsonDeserializer {
@Override
public Distribution deserialize(JsonParser jp, DeserializationContext deserializationContext)
throws IOException, JsonProcessingException {
//Manually parse old format
JsonNode node = jp.getCodec().readTree(jp);
if (node.has("normal")) {
JsonNode n = node.get("normal");
if (!n.has("mean") || !n.has("std")) {
throw new JsonParseException("Cannot deserialize Distribution: legacy format 'normal' wrapper object "
+ " is missing 'mean' or 'std' field", jp.getCurrentLocation());
}
double m = n.get("mean").asDouble();
double s = n.get("std").asDouble();
return new NormalDistribution(m, s);
} else if (node.has("gaussian")) {
JsonNode n = node.get("gaussian");
if (!n.has("mean") || !n.has("std")) {
throw new JsonParseException("Cannot deserialize Distribution: legacy format 'gaussian' wrapper object "
+ " is missing 'mean' or 'std' field", jp.getCurrentLocation());
}
double m = n.get("mean").asDouble();
double s = n.get("std").asDouble();
return new GaussianDistribution(m, s);
} else if (node.has("uniform")) {
JsonNode n = node.get("uniform");
if (!n.has("lower") || !n.has("upper")) {
throw new JsonParseException("Cannot deserialize Distribution: legacy format 'uniform' wrapper object "
+ " is missing 'lower' or 'upper' field", jp.getCurrentLocation());
}
double l = n.get("lower").asDouble();
double u = n.get("upper").asDouble();
return new UniformDistribution(l, u);
} else if (node.has("binomial")) {
JsonNode n = node.get("binomial");
if (!n.has("numberOfTrials") || !n.has("probabilityOfSuccess")) {
throw new JsonParseException("Cannot deserialize Distribution: legacy format 'binomial' wrapper object "
+ " is missing 'lower' or 'upper' field", jp.getCurrentLocation());
}
int num = n.get("numberOfTrials").asInt();
double p = n.get("probabilityOfSuccess").asDouble();
return new BinomialDistribution(num, p);
} else {
throw new JsonParseException(
"Cannot deserialize Distribution: expected type field or legacy format wrapper"
+ " object with name being one of {normal, gaussian, uniform, binomial}",
jp.getCurrentLocation());
}
}
}