flip.covariance.emulators.nnmatrix

Attributes

log
torch_install
default_regression_object
_emulator_type

Classes

RegressionNet

Functions

train_torch_model(number_epochs, model, ...)
train(square_covariance, output_variance, ...[, ...])
evaluate(model, evaluation_value, evaluation_dictionary)

Module Contents

flip.covariance.emulators.nnmatrix.log

flip.covariance.emulators.nnmatrix.torch_install = True

flip.covariance.emulators.nnmatrix.default_regression_object

flip.covariance.emulators.nnmatrix._emulator_type = 'matrix'

class flip.covariance.emulators.nnmatrix.RegressionNet(input_dimension, dimension_hidden_layers=64, number_hidden_layers=3, output_dimension=1, activation_function=default_activation_function)

Bases: default_regression_object

model

forward(x)

flip.covariance.emulators.nnmatrix.train_torch_model(number_epochs, model, normalized_input, normalized_output, optimizer, loss_function, verbose, model_name)

flip.covariance.emulators.nnmatrix.train(square_covariance, output_variance, output_non_diagonal, parameter_values, verbose=False, dimension_hidden_layers=64, number_hidden_layers=3, number_epochs=3000, activation_function=default_activation_function, loss_function=default_loss_function, tolerance_optimizer=0.001)

flip.covariance.emulators.nnmatrix.evaluate(model, evaluation_value, evaluation_dictionary)