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package org.nd4j.autodiff.samediff;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.learning.config.IUpdater;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* TrainingConfig is a simple configuration class for defining settings for training a {@link SameDiff} instance.
* It defines the following settings:
*
*
The {@link IUpdater} to use (i.e., {@link org.nd4j.linalg.learning.config.Adam}, {@link org.nd4j.linalg.learning.config.Nesterovs} etc.
* The IUpdater instance is also how the learning rate (or learning rate schedule) is set.
*
The L1 and L2 regularization coefficients (set to 0.0 by default)
*
The DataSet feature and label mapping - which defines how the feature/label arrays from the DataSet/MultiDataSet
* should be associated with SameDiff variables (usually placeholders)
*
Optional: The names of the trainable parameters. The trainable parameters are inferred automatically if not set here, though
* can be overridden if some parameters should not be modified during training (or if the automatic inference of the trainable
* parameters is not suitable/correct)
*
* The TrainingConfig instance also stores the iteration count and the epoch count - these values are updated during training
* and are used for example in learning rate schedules.
*
* @author Alex Black
*/
@Data
@Builder
@NoArgsConstructor
@AllArgsConstructor
public class TrainingConfig {
private IUpdater updater;
private double l1;
private double l2;
private boolean minimize = true;
private List dataSetFeatureMapping;
private List dataSetLabelMapping;
private List dataSetFeatureMaskMapping;
private List dataSetLabelMaskMapping;
private List trainableParams; //Will be inferred automatically if null
private int iterationCount;
private int epochCount;
/**
* Create a training configuration suitable for training a single input, single output network.
* See also the {@link Builder} for creating a TrainingConfig
*
* @param updater The updater configuration to use
* @param l1 The L1 regularization coefficient for the trainable parameters (set 0.0 to disable L1 regularization)
* @param l2 The L2 regularization coefficient for the trainable parameters (set 0.0 to disable L2 regularization)
* @param dataSetFeatureMapping The name of the placeholder/variable that should be set using the feature INDArray from the DataSet
* (or the first/only feature from a MultiDataSet). For example, if the network input placeholder was
* called "input" then this should be set to "input"
* @param dataSetLabelMapping The name of the placeholder/variable that should be set using the label INDArray from the DataSet
* (or the first/only feature from a MultiDataSet). For example, if the network input placeholder was
* called "input" then this should be set to "input"
*/
public TrainingConfig(IUpdater updater, double l1, double l2, String dataSetFeatureMapping, String dataSetLabelMapping) {
this(updater, l1, l2, true, Collections.singletonList(dataSetFeatureMapping), Collections.singletonList(dataSetLabelMapping),
Collections.emptyList(), Collections.emptyList(), null);
}
/**
* Create a training configuration suitable for training both single input/output and multi input/output networks.
* See also the {@link Builder} for creating a TrainingConfig
*
* @param updater The updater configuration to use
* @param l1 The L1 regularization coefficient for the trainable parameters (set 0.0 to disable L1 regularization)
* @param l2 The L2 regularization coefficient for the trainable parameters (set 0.0 to disable L2 regularization)
* @param minimize Set to true if the loss function should be minimized (usually true). False to maximize
* @param dataSetFeatureMapping The name of the placeholders/variables that should be set using the feature INDArray(s) from the
* DataSet or MultiDataSet. For example, if the network had 2 inputs called "input1" and "input2"
* and the MultiDataSet features should be mapped with {@code MultiDataSet.getFeatures(0)->"input1"}
* and {@code MultiDataSet.getFeatures(1)->"input2"}, then this should be set to {@code List<>("input1", "input2")}.
* @param dataSetLabelMapping As per dataSetFeatureMapping, but for the DataSet/MultiDataSet labels
* @param dataSetFeatureMaskMapping May be null. If non-null, the variables that the MultiDataSet feature mask arrays should be associated with.
* @param dataSetLabelMaskMapping May be null. If non-null, the variables that the MultiDataSet label mask arrays should be associated with.
* @param trainableParams May be null. If null: the set of trainable parameters will automatically be inferred from the SameDiff structure.
* If non-null, this defines the set of parameters that should be modified during training
*/
public TrainingConfig(IUpdater updater, double l1, double l2, boolean minimize, List dataSetFeatureMapping, List dataSetLabelMapping,
List dataSetFeatureMaskMapping, List dataSetLabelMaskMapping, List trainableParams) {
this.updater = updater;
this.l1 = l1;
this.l2 = l2;
this.minimize = minimize;
this.dataSetFeatureMapping = dataSetFeatureMapping;
this.dataSetLabelMapping = dataSetLabelMapping;
this.dataSetFeatureMaskMapping = dataSetFeatureMaskMapping;
this.dataSetLabelMaskMapping = dataSetLabelMaskMapping;
this.trainableParams = trainableParams;
}
/**
* Increment the iteration count by 1
*/
public void incrementIterationCount(){
iterationCount++;
}
/**
* Increment the epoch count by 1
*/
public void incrementEpochCount(){
epochCount++;
}
public static Builder builder(){
return new Builder();
}
/**
* Get the index of the label array that the specified variable is associated with
* @param s Name of the variable
* @return The index of the label variable, or -1 if not found
*/
public int labelIdx(String s){
return dataSetLabelMapping.indexOf(s);
}
public static class Builder {
private IUpdater updater;
private double l1;
private double l2;
private boolean minimize = true;
private List dataSetFeatureMapping;
private List dataSetLabelMapping;
private List dataSetFeatureMaskMapping;
private List dataSetLabelMaskMapping;
private List trainableParams; //Will be inferred automatically if null
/**
* Set the updater (such as {@link org.nd4j.linalg.learning.config.Adam}, {@link org.nd4j.linalg.learning.config.Nesterovs}
* etc. This is also how the learning rate (or learning rate schedule) is set.
* @param updater Updater to set
*/
public Builder updater(IUpdater updater){
this.updater = updater;
return this;
}
/**
* Sets the L1 regularization coefficient for all trainable parameters. Must be >= 0.
* Default value: 0.0 (disabled)
* @param l1 L1 regularization coefficient
*/
public Builder l1(double l1){
Preconditions.checkState(l1 >= 0, "L1 regularization coefficient must be >= 0. Got %s", l1);
this.l1 = l1;
return this;
}
/**
* Sets the L2 regularization coefficient for all trainable parameters. Must be >= 0.
* Default value: 0.0 (disabled)
* @param l2 L2 regularization coefficient
*/
public Builder l2(double l2){
Preconditions.checkState(l2 >= 0, "L2 regularization coefficient must be >= 0. Got %s", l2);
this.l2 = l2;
return this;
}
/**
* Sets whether the loss function should be minimized (true) or maximized (false).
* The loss function is usually minimized in SGD.
* Default: true.
* @param minimize True to minimize, false to maximize
* @return
*/
public Builder minimize(boolean minimize){
this.minimize = minimize;
return this;
}
/**
* Set the name of the placeholders/variables that should be set using the feature INDArray(s) from the
* DataSet or MultiDataSet. For example, if the network had 2 inputs called "input1" and "input2"
* and the MultiDataSet features should be mapped with {@code MultiDataSet.getFeatures(0)->"input1"}
* and {@code MultiDataSet.getFeatures(1)->"input2"}, then this should be set to {@code List<>("input1", "input2")}.
*
* @param dataSetFeatureMapping Name of the variables/placeholders that the feature arrays should be mapped to
*/
public Builder dataSetFeatureMapping(String... dataSetFeatureMapping){
return dataSetFeatureMapping(Arrays.asList(dataSetFeatureMapping));
}
/**
* Set the name of the placeholders/variables that should be set using the feature INDArray(s) from the
* DataSet or MultiDataSet. For example, if the network had 2 inputs called "input1" and "input2"
* and the MultiDataSet features should be mapped with {@code MultiDataSet.getFeatures(0)->"input1"}
* and {@code MultiDataSet.getFeatures(1)->"input2"}, then this should be set to {@code "input1", "input2"}.
*
* @param dataSetFeatureMapping Name of the variables/placeholders that the feature arrays should be mapped to
*/
public Builder dataSetFeatureMapping(List dataSetFeatureMapping){
Preconditions.checkNotNull(dataSetFeatureMapping != null && dataSetFeatureMapping.size() > 0, "No feature mapping was provided");
this.dataSetFeatureMapping = dataSetFeatureMapping;
return this;
}
/**
* Set the name of the placeholders/variables that should be set using the labels INDArray(s) from the
* DataSet or MultiDataSet. For example, if the network had 2 labels called "label1" and "label2"
* and the MultiDataSet labels should be mapped with {@code MultiDataSet.getLabel(0)->"label1"}
* and {@code MultiDataSet.getLabels(1)->"label"}, then this should be set to {@code "label1", "label2"}.
*
* @param dataSetLabelMapping Name of the variables/placeholders that the label arrays should be mapped to
*/
public Builder dataSetLabelMapping(String... dataSetLabelMapping){
return dataSetLabelMapping(Arrays.asList(dataSetLabelMapping));
}
/**
* Set the name of the placeholders/variables that should be set using the labels INDArray(s) from the
* DataSet or MultiDataSet. For example, if the network had 2 labels called "label1" and "label2"
* and the MultiDataSet labels should be mapped with {@code MultiDataSet.getLabel(0)->"label1"}
* and {@code MultiDataSet.getLabels(1)->"label"}, then this should be set to {@code "label1", "label2"}.
*
* @param dataSetLabelMapping Name of the variables/placeholders that the label arrays should be mapped to
*/
public Builder dataSetLabelMapping(List dataSetLabelMapping){
Preconditions.checkNotNull(dataSetLabelMapping != null && dataSetLabelMapping.size() > 0, "No label mapping was provided");
this.dataSetLabelMapping = dataSetLabelMapping;
return this;
}
/**
* Set the name of the placeholders/variables that should be set using the feature mask INDArray(s) from the
* DataSet or MultiDataSet. For example, if the network had 2 mask variables called "mask1" and "mask2"
* and the MultiDataSet features masks should be mapped with {@code MultiDataSet.getFeatureMaskArray(0)->"mask1"}
* and {@code MultiDataSet.getFeatureMaskArray(1)->"mask2"}, then this should be set to {@code "mask2", "mask2"}.
*
* @param dataSetFeatureMaskMapping Name of the variables/placeholders that the feature arrays should be mapped to
*/
public Builder dataSetFeatureMaskMapping(List dataSetFeatureMaskMapping){
this.dataSetFeatureMaskMapping = dataSetFeatureMaskMapping;
return this;
}
/**
* Set the name of the placeholders/variables that should be set using the label mask INDArray(s) from the
* DataSet or MultiDataSet. For example, if the network had 2 mask variables called "mask1" and "mask2"
* and the MultiDataSet label masks should be mapped with {@code MultiDataSet.getLabelMaskArray(0)->"mask1"}
* and {@code MultiDataSet.getLabelMaskArray(1)->"mask2"}, then this should be set to {@code "mask2", "mask2"}.
*
* @param dataSetLabelMaskMapping Name of the variables/placeholders that the feature arrays should be mapped to
*/
public Builder dataSetLabelMaskMapping(List dataSetLabelMaskMapping){
this.dataSetLabelMaskMapping = dataSetLabelMaskMapping;
return this;
}
/**
* Define the set of trainable parameters for the network.
* The trainable parameters are not set by default, which means they will be inferred automatically.
* The set of trainable parameters (variables) can be set here - any excluded from being set here won't be
* modified during training
* @param trainableParams Set of parameters/variables to train
*/
public Builder trainableParams(String... trainableParams){
return trainableParams(Arrays.asList(trainableParams));
}
/**
* Define the set of trainable parameters for the network.
* The trainable parameters are not set by default, which means they will be inferred automatically.
* The set of trainable parameters (variables) can be set here - any excluded from being set here won't be
* modified during training
* @param trainableParams Set of parameters/variables to train
*/
public Builder trainableParams(List trainableParams) {
this.trainableParams = trainableParams;
return this;
}
public TrainingConfig build(){
Preconditions.checkState(updater != null, "Updater (optimizer) must not be null. Use updater(IUpdater) to set an updater");
Preconditions.checkState(dataSetFeatureMapping != null, "No DataSet feature mapping has been provided. A " +
"mapping between DataSet array positions and variables/placeholders must be provided - use dateSetFeatureMapping(...) to set this");
Preconditions.checkState(dataSetLabelMapping != null, "No DataSet label mapping has been provided. A " +
"mapping between DataSet array positions and variables/placeholders must be provided - use dateSetLabelMapping(...) to set this");
return new TrainingConfig(updater, l1, l2, minimize, dataSetFeatureMapping, dataSetLabelMapping,
dataSetFeatureMaskMapping, dataSetLabelMaskMapping, trainableParams);
}
}
}
