99 lines
4.0 KiB
Java
99 lines
4.0 KiB
Java
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.optimize.api;
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import org.deeplearning4j.nn.api.Model;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
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import java.util.List;
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import java.util.Map;
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/**
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* A listener interface for training DL4J models.<br>
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* The methods here will be called at various points during training, and only during training.<br>
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* Note that users can extend {@link BaseTrainingListener} and selectively override the required methods,
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* instead of implementing TrainingListener directly and having a number of no-op methods.
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*
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* @author Alex Black
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*/
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public interface TrainingListener {
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/**
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* Event listener for each iteration. Called once, after each parameter update has ocurred while training the network
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* @param iteration the iteration
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* @param model the model iterating
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*/
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void iterationDone(Model model, int iteration, int epoch);
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/**
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* Called once at the start of each epoch, when using methods such as {@link org.deeplearning4j.nn.multilayer.MultiLayerNetwork#fit(DataSetIterator)},
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* {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(DataSetIterator)} or {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(MultiDataSetIterator)}
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*/
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void onEpochStart(Model model);
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/**
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* Called once at the end of each epoch, when using methods such as {@link org.deeplearning4j.nn.multilayer.MultiLayerNetwork#fit(DataSetIterator)},
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* {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(DataSetIterator)} or {@link org.deeplearning4j.nn.graph.ComputationGraph#fit(MultiDataSetIterator)}
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*/
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void onEpochEnd(Model model);
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/**
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* Called once per iteration (forward pass) for activations (usually for a {@link org.deeplearning4j.nn.multilayer.MultiLayerNetwork}),
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* only at training time
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*
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* @param model Model
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* @param activations Layer activations (including input)
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*/
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void onForwardPass(Model model, List<INDArray> activations);
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/**
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* Called once per iteration (forward pass) for activations (usually for a {@link org.deeplearning4j.nn.graph.ComputationGraph}),
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* only at training time
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*
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* @param model Model
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* @param activations Layer activations (including input)
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*/
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void onForwardPass(Model model, Map<String, INDArray> activations);
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/**
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* Called once per iteration (backward pass) <b>before the gradients are updated</b>
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* Gradients are available via {@link Model#gradient()}.
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* Note that gradients will likely be updated in-place - thus they should be copied or processed synchronously
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* in this method.
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* <p>
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* For updates (gradients post learning rate/momentum/rmsprop etc) see {@link #onBackwardPass(Model)}
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*
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* @param model Model
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*/
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void onGradientCalculation(Model model);
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/**
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* Called once per iteration (backward pass) after gradients have been calculated, and updated
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* Gradients are available via {@link Model#gradient()}.
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* <p>
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* Unlike {@link #onGradientCalculation(Model)} the gradients at this point will be post-update, rather than
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* raw (pre-update) gradients at that method call.
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*
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* @param model Model
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*/
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void onBackwardPass(Model model);
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}
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