fann_cascadetrain_on_data(3) [php man page]
FANN_CASCADETRAIN_ON_DATA(3) 1 FANN_CASCADETRAIN_ON_DATA(3) fann_cascadetrain_on_data - Trains on an entire dataset, for a period of time using the Cascade2 training algorithm SYNOPSIS
bool fann_cascadetrain_on_data (resource $ann, resource $data, int $max_neurons, int $neurons_between_reports, float $desired_error) DESCRIPTION
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE(3) value should change within fann_get_cascade_output_stagnation_epochs(3) during training of the output connections, in order for the training not to stagnate. If the training stagnates, the training of the output connections will be ended and new candidates will be prepared. This training uses the parameters set using the fann_set_cascade_..., but it also uses another training algorithm as it's internal train- ing algorithm. This algorithm can be set to either FANN_TRAIN_RPROP or FANN_TRAIN_QUICKPROP by fann_set_training_algorithm(3), and the parameters set for these training algorithms will also affect the cascade training. PARAMETERS
o $ann -Neural network resource. o $data -Neural network training data resource. o $max_neurons - The maximum number of neurons to be added to neural network. o $neurons_between_reports - The number of neurons between printing a status report. A value of zero means no reports should be printed. o $desired_error - The desired fann_get_MSE(3) or fann_get_bit_fail(3), depending on which stop function is chosen by fann_set_train_stop_func- tion(3) RETURN VALUES
Returns TRUE on success, or FALSE otherwise. SEE ALSO
fann_train_on_data(3), fann_cascadetrain_on_file(3). PHP Documentation Group FANN_CASCADETRAIN_ON_DATA(3)
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MPSCNNBatchNormalizationNode(3) MetalPerformanceShaders.framework MPSCNNBatchNormalizationNode(3) NAME
MPSCNNBatchNormalizationNode SYNOPSIS
#import <MPSNNGraphNodes.h> Inherits MPSNNFilterNode. Instance Methods (nonnull instancetype) - initWithSource:dataSource: Class Methods (nonnull instancetype) + nodeWithSource:dataSource: Properties MPSCNNBatchNormalizationFlags flags Detailed Description A node representing batch normalization for inference or training Batch normalization operates differently for inference and training. For inference, the normalization is done according to a static statistical representation of data saved during training. For training, this representation is ever evolving. In the low level MPS batch normalization interface, during training, the batch normalization is broken up into two steps: calculation of the statistical representation of input data, followed by normalization once the statistics are known for the entire batch. These are MPSCNNBatchNormalizationStatistics and MPSCNNBatchNormalization, respectively. When this node appears in a graph and is not required to produce a MPSCNNBatchNormalizationState -- that is, MPSCNNBatchNormalizationNode.resultState is not used within the graph -- then it operates in inference mode and new batch-only statistics are not calculated. When this state node is consumed, then the node is assumed to be in training mode and new statistics will be calculated and written to the MPSCNNBatchNormalizationState and passed along to the MPSCNNBatchNormalizationGradient and MPSCNNBatchNormalizationStatisticsGradient as necessary. This should allow you to construct an identical sequence of nodes for inference and training and expect the to right thing happen. Method Documentation - (nonnull instancetype) initWithSource: (MPSNNImageNode *__nonnull) source(nonnull id< MPSCNNBatchNormalizationDataSource >) dataSource + (nonnull instancetype) nodeWithSource: (MPSNNImageNode *__nonnull) source(nonnull id< MPSCNNBatchNormalizationDataSource >) dataSource Property Documentation - (MPSCNNBatchNormalizationFlags) flags [read], [write], [nonatomic], [assign] Options controlling how batch normalization is calculated Default: MPSCNNBatchNormalizationFlagsDefault Author Generated automatically by Doxygen for MetalPerformanceShaders.framework from the source code. Version MetalPerformanceShaders-100 Thu Feb 8 2018 MPSCNNBatchNormalizationNode(3)