Index

A

AdditionalModule (class in nnbma.layers.additional_module) AdditionalModuleFromExisting (class in nnbma.layers.additional_module)

apply_transf() (nnbma.dataset.regression_dataset.RegressionDataset method) asinh() (in module nnbma.operators.base)

B

BatchScheduler (class in nnbma.learning.batch_scheduler)

C

CauchyLoss (class in nnbma.learning.loss_functions) ColumnwiseOperator (class in nnbma.operators.operator) ConstantBatch (class in nnbma.learning.batch_scheduler)

copy() (nnbma.networks.neural_network.NeuralNetwork method) count_bytes() (nnbma.networks.neural_network.NeuralNetwork method) count_parameters() (nnbma.networks.neural_network.NeuralNetwork method)

D

dataset (nnbma.dataset.regression_dataset.RegressionSubset attribute)

DenselyConnected (class in nnbma.networks.densely_connected)

E

EmbeddingNetwork (class in nnbma.networks.embedding_network) evaluate() (nnbma.networks.neural_network.NeuralNetwork method)

expanded_features() (nnbma.layers.polynomial_expansion.PolynomialExpansion static method) ExponentialBatchScheduler (class in nnbma.learning.batch_scheduler)

F

features_names (nnbma.dataset.mask_dataset.MaskDataset property) features_size() (nnbma.dataset.mask_dataset.MaskDataset method) forward() (nnbma.layers.additional_module.AdditionalModule method) (nnbma.layers.additional_module.AdditionalModuleFromExisting method) (nnbma.layers.polynomial_expansion.PolynomialExpansion method) (nnbma.layers.restrictable_linear.RestrictableLinear method) (nnbma.learning.loss_functions.CauchyLoss method) (nnbma.learning.loss_functions.MaskedLossFunction method) (nnbma.learning.loss_functions.MaskedMSELoss method) (nnbma.learning.loss_functions.MaskOverlay method) (nnbma.learning.loss_functions.SmoothL1Loss method) (nnbma.networks.densely_connected.DenselyConnected method) (nnbma.networks.embedding_network.EmbeddingNetwork method) (nnbma.networks.fully_connected.FullyConnected method) (nnbma.networks.merging_network.MergingNetwork method) (nnbma.networks.neural_network.NeuralNetwork method) (nnbma.networks.polynomial_network.PolynomialNetwork method)

from_pandas() (nnbma.dataset.mask_dataset.MaskDataset static method) (nnbma.dataset.regression_dataset.RegressionDataset static method) FullyConnected (class in nnbma.networks.fully_connected)

G

get_batch_size() (nnbma.learning.batch_scheduler.BatchScheduler method) (nnbma.learning.batch_scheduler.ConstantBatch method) (nnbma.learning.batch_scheduler.ExponentialBatchScheduler method) (nnbma.learning.batch_scheduler.LinearBatchScheduler method)

getall() (nnbma.dataset.mask_dataset.MaskDataset method) (nnbma.dataset.regression_dataset.RegressionDataset method)

H

has_nan() (nnbma.dataset.regression_dataset.RegressionDataset method)

has_nonfinite() (nnbma.dataset.regression_dataset.RegressionDataset method)

I

id() (in module nnbma.operators.base) in_features (nnbma.layers.restrictable_linear.RestrictableLinear attribute) indices (nnbma.dataset.regression_dataset.RegressionSubset attribute) inputs_names (nnbma.dataset.regression_dataset.RegressionDataset property)

inputs_size() (nnbma.dataset.regression_dataset.RegressionDataset method) InverseNormalizer (class in nnbma.operators.normalization) issubsetof() (nnbma.dataset.mask_dataset.MaskSubset method) (nnbma.dataset.regression_dataset.RegressionSubset method)

J

join() (nnbma.dataset.mask_dataset.MaskDataset method) (nnbma.dataset.regression_dataset.RegressionDataset method)

L

learning_procedure() (in module nnbma.learning.network_learning) LearningParameters (class in nnbma.learning.network_learning) LinearBatchScheduler (class in nnbma.learning.batch_scheduler)

load() (nnbma.dataset.mask_dataset.MaskDataset static method) (nnbma.dataset.regression_dataset.RegressionDataset static method) (nnbma.networks.neural_network.NeuralNetwork class method) log10() (in module nnbma.operators.base)

M

m (nnbma.dataset.mask_dataset.MaskDataset property) (nnbma.dataset.mask_dataset.MaskSubset property) MaskDataset (class in nnbma.dataset.mask_dataset) MaskedLossFunction (class in nnbma.learning.loss_functions) MaskedMSELoss (class in nnbma.learning.loss_functions) MaskOverlay (class in nnbma.learning.loss_functions) MaskSubset (class in nnbma.dataset.mask_dataset) MEAN0 (nnbma.operators.normalization.NormTypes attribute) MEAN0STD1 (nnbma.operators.normalization.NormTypes attribute) MergingNetwork (class in nnbma.networks.merging_network) MIN0MAX1 (nnbma.operators.normalization.NormTypes attribute) MIN1MAX1 (nnbma.operators.normalization.NormTypes attribute) module nnbma nnbma.dataset nnbma.dataset.mask_dataset nnbma.dataset.regression_dataset nnbma.layers nnbma.layers.additional_module nnbma.layers.polynomial_expansion nnbma.layers.restrictable_linear nnbma.learning nnbma.learning.batch_scheduler nnbma.learning.loss_functions nnbma.learning.network_learning nnbma.networks nnbma.networks.densely_connected nnbma.networks.embedding_network nnbma.networks.fully_connected nnbma.networks.merging_network nnbma.networks.neural_network nnbma.networks.polynomial_network nnbma.operators nnbma.operators.base nnbma.operators.normalization nnbma.operators.operator

N

n_features (nnbma.dataset.mask_dataset.MaskDataset property) n_inputs (nnbma.dataset.regression_dataset.RegressionDataset property) n_outputs (nnbma.dataset.regression_dataset.RegressionDataset property) NeuralNetwork (class in nnbma.networks.neural_network) nnbma module nnbma.dataset module nnbma.dataset.mask_dataset module nnbma.dataset.regression_dataset module nnbma.layers module nnbma.layers.additional_module module nnbma.layers.polynomial_expansion module nnbma.layers.restrictable_linear module nnbma.learning module nnbma.learning.batch_scheduler module nnbma.learning.loss_functions module

nnbma.learning.network_learning module nnbma.networks module nnbma.networks.densely_connected module nnbma.networks.embedding_network module nnbma.networks.fully_connected module nnbma.networks.merging_network module nnbma.networks.neural_network module nnbma.networks.polynomial_network module nnbma.operators module nnbma.operators.base module nnbma.operators.normalization module nnbma.operators.operator module NONE (nnbma.operators.normalization.NormTypes attribute) Normalizer (class in nnbma.operators.normalization) NormTypes (class in nnbma.operators.normalization)

O

Operator (class in nnbma.operators.operator) out_features (nnbma.layers.restrictable_linear.RestrictableLinear attribute)

outputs_names (nnbma.dataset.regression_dataset.RegressionDataset property) outputs_size() (nnbma.dataset.regression_dataset.RegressionDataset method)

P

PolynomialExpansion (class in nnbma.layers.polynomial_expansion)

PolynomialNetwork (class in nnbma.networks.polynomial_network) pow10() (in module nnbma.operators.base)

R

RegressionDataset (class in nnbma.dataset.regression_dataset) RegressionSubset (class in nnbma.dataset.regression_dataset) restrict_to_output_subset() (nnbma.layers.restrictable_linear.RestrictableLinear method) (nnbma.networks.densely_connected.DenselyConnected method) (nnbma.networks.fully_connected.FullyConnected method) (nnbma.networks.merging_network.MergingNetwork method) (nnbma.networks.neural_network.NeuralNetwork method) (nnbma.networks.polynomial_network.PolynomialNetwork method)

RestrictableLinear (class in nnbma.layers.restrictable_linear)

S

save() (nnbma.dataset.mask_dataset.MaskDataset method) (nnbma.dataset.regression_dataset.RegressionDataset method) (nnbma.networks.neural_network.NeuralNetwork method) SequentialOperator (class in nnbma.operators.operator) set_device() (nnbma.networks.neural_network.NeuralNetwork method) set_epoch() (nnbma.learning.batch_scheduler.BatchScheduler method) sinh() (in module nnbma.operators.base)

SmoothL1Loss (class in nnbma.learning.loss_functions) stats() (nnbma.dataset.mask_dataset.MaskDataset method) (nnbma.dataset.regression_dataset.RegressionDataset method) STD1 (nnbma.operators.normalization.NormTypes attribute) step() (nnbma.learning.batch_scheduler.BatchScheduler method) substract() (nnbma.dataset.mask_dataset.MaskDataset method) (nnbma.dataset.regression_dataset.RegressionDataset method)

T

time() (nnbma.networks.neural_network.NeuralNetwork method) to_pandas() (nnbma.dataset.mask_dataset.MaskDataset method) (nnbma.dataset.regression_dataset.RegressionDataset method) train() (nnbma.layers.restrictable_linear.RestrictableLinear method) (nnbma.networks.neural_network.NeuralNetwork method) training (nnbma.layers.additional_module.AdditionalModule attribute) (nnbma.layers.additional_module.AdditionalModuleFromExisting attribute) (nnbma.layers.polynomial_expansion.PolynomialExpansion attribute) (nnbma.learning.loss_functions.CauchyLoss attribute) (nnbma.learning.loss_functions.MaskedLossFunction attribute) (nnbma.learning.loss_functions.MaskedMSELoss attribute) (nnbma.learning.loss_functions.MaskOverlay attribute) (nnbma.learning.loss_functions.SmoothL1Loss attribute) (nnbma.networks.densely_connected.DenselyConnected attribute) (nnbma.networks.embedding_network.EmbeddingNetwork attribute) (nnbma.networks.fully_connected.FullyConnected attribute) (nnbma.networks.merging_network.MergingNetwork attribute) (nnbma.networks.neural_network.NeuralNetwork attribute) (nnbma.networks.polynomial_network.PolynomialNetwork attribute)

U

update_standardization() (nnbma.layers.polynomial_expansion.PolynomialExpansion method) (nnbma.networks.polynomial_network.PolynomialNetwork method)

W

weight (nnbma.layers.restrictable_linear.RestrictableLinear attribute)

X

x (nnbma.dataset.regression_dataset.RegressionDataset property) (nnbma.dataset.regression_dataset.RegressionSubset property)

Y

y (nnbma.dataset.regression_dataset.RegressionDataset property) (nnbma.dataset.regression_dataset.RegressionSubset property)