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A GATE plugin that provides many different machine learning
algorithms for a wide range of NLP-related machine learning tasks like
text classification, tagging, or chunking.
/*
* Copyright (c) 2015-2016 The University Of Sheffield.
*
* This file is part of gateplugin-LearningFramework
* (see https://github.com/GateNLP/gateplugin-LearningFramework).
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 2.1 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this software. If not, see .
*/
package gate.plugin.learningframework.engines;
/**
* An engine that represents Python Keras through en external process.
*
* This requires that the user configures the location of where keras-wrapper is installed.
* This can be done by setting the environment variable KERAS_WRAPPPER_HOME, the Java property
* gate.plugin.learningframework.keraswrapper.home or by adding another yaml file "keras.yaml"
* to the data directory which contains the setting keraswrapper.home.
* If the path starts with a slash
* it is an absolute path, otherwise the path is resolved relative to the
* directory.
*
*
* @author Johann Petrak
*/
public class EngineKerasWrapper extends EngineMBPythonNetworksBase {
static class KerasModel { }
public EngineKerasWrapper() {
WRAPPER_NAME = "KerasWrapper";
ENV_WRAPPER_HOME = "KERAS_WRAPPER_HOME";
PROP_WRAPPER_HOME = "gate.plugin.learningframework.keraswrapper.home";
YAML_FILE = "keras.yaml";
YAML_SETTING_WRAPPER_HOME = "keraswrapper.home";
SCRIPT_APPLY_BASENAME = "kerasWrapperApply";
SCRIPT_TRAIN_BASENAME = "kerasWrapperTrain";
SCRIPT_EVAL_BASENAME = "kerasWrapperEval";
MODEL_BASENAME = "kerasmodel";
MODEL_INSTANCE = new KerasModel();
}
}