org.nd4j.autodiff.samediff.config.FitConfig Maven / Gradle / Ivy
/*
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
*/
package org.nd4j.autodiff.samediff.config;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import lombok.AccessLevel;
import lombok.Getter;
import lombok.NonNull;
import lombok.Setter;
import org.nd4j.autodiff.listeners.records.History;
import org.nd4j.autodiff.listeners.Listener;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.samediff.TrainingConfig;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.dataset.adapter.MultiDataSetIteratorAdapter;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
/**
* Configuration for a {@link SameDiff} training operation.
*
* Used in {@link SameDiff#fit()}.
*/
@Getter
@Setter
public class FitConfig {
@Setter(AccessLevel.NONE)
private SameDiff sd;
private MultiDataSetIterator trainingData;
private MultiDataSetIterator validationData = null;
private int epochs = -1;
private int validationFrequency = 1;
@NonNull
private List listeners = new ArrayList<>();
public FitConfig(@NonNull SameDiff sd) {
this.sd = sd;
}
/**
* Set the number of epochs to train for
*/
public FitConfig epochs(int epochs) {
this.epochs = epochs;
return this;
}
/**
* Set the training data
*/
public FitConfig train(@NonNull MultiDataSetIterator trainingData) {
this.trainingData = trainingData;
return this;
}
/**
* Set the training data
*/
public FitConfig train(@NonNull DataSetIterator trainingData) {
return train(new MultiDataSetIteratorAdapter(trainingData));
}
/**
* Set the training data and number of epochs
*/
public FitConfig train(@NonNull MultiDataSetIterator trainingData, int epochs) {
return train(trainingData).epochs(epochs);
}
/**
* Set the training data and number of epochs
*/
public FitConfig train(@NonNull DataSetIterator trainingData, int epochs) {
return train(trainingData).epochs(epochs);
}
/**
* Set the validation data
*/
public FitConfig validate(MultiDataSetIterator validationData) {
this.validationData = validationData;
return this;
}
/**
* Set the validation data
*/
public FitConfig validate(DataSetIterator validationData) {
if (validationData == null) {
return validate((MultiDataSetIterator) null);
} else {
return validate(new MultiDataSetIteratorAdapter(validationData));
}
}
/**
* Set the validation frequency. Validation will be preformed once every so many epochs.
*
* Specifically, validation will be preformed when i % validationFrequency == 0
*/
public FitConfig validationFrequency(int validationFrequency) {
this.validationFrequency = validationFrequency;
return this;
}
/**
* Set the validation data and frequency
*
* Specifically, validation will be preformed when i % validationFrequency == 0
*/
public FitConfig validate(MultiDataSetIterator validationData, int validationFrequency) {
return validate(validationData).validationFrequency(validationFrequency);
}
/**
* Set the validation data and frequency
*
* Specifically, validation will be preformed when i % validationFrequency == 0
*/
public FitConfig validate(DataSetIterator validationData, int validationFrequency) {
return validate(validationData).validationFrequency(validationFrequency);
}
/**
* Add listeners for this operation
*/
public FitConfig listeners(@NonNull Listener... listeners) {
this.listeners.addAll(Arrays.asList(listeners));
return this;
}
private void validateConfig() {
Preconditions.checkNotNull(trainingData, "Training data must not be null");
Preconditions.checkState(epochs > 0, "Epochs must be > 0, got %s", epochs);
if (validationData != null)
Preconditions.checkState(validationFrequency > 0, "Validation Frequency must be > 0 if validation data is given, got %s", validationFrequency);
}
/**
* Do the training.
*
* @return a {@link History} object containing the history information for this training operation
* (evaluations specified in the {@link TrainingConfig}, loss values, and timing information).
*/
public History exec() {
validateConfig();
return sd.fit(trainingData, epochs, validationData, validationFrequency, listeners.toArray(new Listener[0]));
}
}