org.deeplearning4j.nn.api.Model Maven / Gradle / Ivy
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* 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.nn.api;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.optimize.api.ConvexOptimizer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.Collection;
import java.util.Map;
/**
* A Model is meant for predicting something from data.
* Note that this is not like supervised learning where
* there are labels attached to the examples.
*
*/
public interface Model {
/**
* Init the model
*/
void init();
/**
* Set the trainingListeners for the ComputationGraph (and all layers in the network)
*/
void setListeners(Collection listeners);
/**
* Set the trainingListeners for the ComputationGraph (and all layers in the network)
*/
void setListeners(TrainingListener... listeners);
/**
* This method ADDS additional TrainingListener to existing listeners
*
* @param listener
*/
void addListeners(TrainingListener... listener);
/**
* All models have a fit method
*/
@Deprecated
void fit();
/**
* Update layer weights and biases with gradient change
*/
void update(Gradient gradient);
/**
* Perform one update applying the gradient
* @param gradient the gradient to apply
*/
void update(INDArray gradient, String paramType);
/**
* The score for the model
* @return the score for the model
*/
double score();
/**
* Update the score
*/
void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr);
/**
* Parameters of the model (if any)
* @return the parameters of the model
*/
INDArray params();
/**
* the number of parameters for the model
* @return the number of parameters for the model
*
*/
long numParams();
/**
* the number of parameters for the model
* @return the number of parameters for the model
*
*/
long numParams(boolean backwards);
/**
* Set the parameters for this model.
* This expects a linear ndarray which then be unpacked internally
* relative to the expected ordering of the model
* @param params the parameters for the model
*/
void setParams(INDArray params);
/**
* Set the initial parameters array as a view of the full (backprop) network parameters
* NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
* @param params a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array
*/
void setParamsViewArray(INDArray params);
INDArray getGradientsViewArray();
/**
* Set the gradients array as a view of the full (backprop) network parameters
* NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
* @param gradients a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array
*/
void setBackpropGradientsViewArray(INDArray gradients);
/**
* Fit the model to the given data
* @param data the data to fit the model to
*/
void fit(INDArray data, LayerWorkspaceMgr workspaceMgr);
/**
* Get the gradient. Note that this method will not calculate the gradient, it will rather return the gradient
* that has been computed before.
* For calculating the gradient, see {@link Model#computeGradientAndScore(LayerWorkspaceMgr)} } .
* @return the gradient for this model, as calculated before
*/
Gradient gradient();
/**
* Get the gradient and score
* @return the gradient and score
*/
Pair gradientAndScore();
/**
* The current inputs batch size
* @return the current inputs batch size
*/
int batchSize();
/**
* The configuration for the neural network
* @return the configuration for the neural network
*/
NeuralNetConfiguration conf();
/**
* Setter for the configuration
* @param conf
*/
void setConf(NeuralNetConfiguration conf);
/**
* The input/feature matrix for the model
* @return the input/feature matrix for the model
*/
INDArray input();
/**
* Returns this models optimizer
* @return this models optimizer
*/
ConvexOptimizer getOptimizer();
/**
* Get the parameter
* @param param the key of the parameter
* @return the parameter vector/matrix with that particular key
*/
INDArray getParam(String param);
/**
* The param table
* @return
*/
Map paramTable();
/**
* Table of parameters by key, for backprop
* For many models (dense layers, etc) - all parameters are backprop parameters
* @param backpropParamsOnly If true, return backprop params only. If false: return all params (equivalent to
* paramsTable())
*/
Map paramTable(boolean backpropParamsOnly);
/**
* Setter for the param table
* @param paramTable
*/
void setParamTable(Map paramTable);
/**
* Set the parameter with a new ndarray
* @param key the key to se t
* @param val the new ndarray
*/
void setParam(String key, INDArray val);
/**
* Clear input
*/
void clear();
/**
* Apply any constraints to the model
*/
void applyConstraints(int iteration, int epoch);
}