org.deeplearning4j.optimize.api.TrainingListener Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 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.deeplearning4j.optimize.api;
import org.deeplearning4j.nn.api.Model;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import java.util.List;
import java.util.Map;
/**
* A listener interface for training DL4J models.
* The methods here will be called at various points during training, and only during training.
* Note that users can extend {@link BaseTrainingListener} and selectively override the required methods,
* instead of implementing TrainingListener directly and having a number of no-op methods.
*
* @author Alex Black
*/
public interface TrainingListener {
/**
* Event listener for each iteration. Called once, after each parameter update has ocurred while training the network
* @param iteration the iteration
* @param model the model iterating
*/
void iterationDone(Model model, int iteration, int epoch);
/**
* Called once at the start of each epoch, when using methods such as {@link org.deeplearning4j.nn.multilayer.MultiLayerNetwork#fit(DataSetIterator)},
* {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(DataSetIterator)} or {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(MultiDataSetIterator)}
*/
void onEpochStart(Model model);
/**
* Called once at the end of each epoch, when using methods such as {@link org.deeplearning4j.nn.multilayer.MultiLayerNetwork#fit(DataSetIterator)},
* {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(DataSetIterator)} or {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(MultiDataSetIterator)}
*/
void onEpochEnd(Model model);
/**
* Called once per iteration (forward pass) for activations (usually for a {@link org.deeplearning4j.nn.multilayer.MultiLayerNetwork}),
* only at training time
*
* @param model Model
* @param activations Layer activations (including input)
*/
void onForwardPass(Model model, List activations);
/**
* Called once per iteration (forward pass) for activations (usually for a {@link org.deeplearning4j.nn.graph.ComputationGraph}),
* only at training time
*
* @param model Model
* @param activations Layer activations (including input)
*/
void onForwardPass(Model model, Map activations);
/**
* Called once per iteration (backward pass) before the gradients are updated
* Gradients are available via {@link Model#gradient()}.
* Note that gradients will likely be updated in-place - thus they should be copied or processed synchronously
* in this method.
*
* For updates (gradients post learning rate/momentum/rmsprop etc) see {@link #onBackwardPass(Model)}
*
* @param model Model
*/
void onGradientCalculation(Model model);
/**
* Called once per iteration (backward pass) after gradients have been calculated, and updated
* Gradients are available via {@link Model#gradient()}.
*
* Unlike {@link #onGradientCalculation(Model)} the gradients at this point will be post-update, rather than
* raw (pre-update) gradients at that method call.
*
* @param model Model
*/
void onBackwardPass(Model model);
}