ModelTrainingAndValidation Namespace Reference

Functions
def	make_variable (tuple, initializer)
def	shape (tensor)
def	plot_interpol (xesnew, ynew)
def	ApplyInterpolation (y_data, y_data_interpolation)
def	Interpolation_MSE (y_true, y_pred)
	Custom Loss function evaluating the error of an interpolation. More...
def	MSE_plus_MAE (y_true, y_pred)
def	MSE_plus_LogMSE (y_true, y_pred)
def	LogCosh_plus_MAE (y_true, y_pred)
def	LogCosh_plus_LogMSE (y_true, y_pred)
def	LogCosh_plus_MSE (y_true, y_pred)
def	LogCosh_plus_LogMSE_plus_MAE (y_true, y_pred)
def	main (*args, **kwargs)
def	ModelEvaluation (*args)

Variables
	threshold
	xgrid = np.arange(0.0,1.0*inp.Npoints)
	ygrid = np.arange(0.0,1.0*inp.Npoints)
	test_predictions = ModelEvaluation(model, validation_generator)
	test data: More...
int	size_predictions = 1
int	size_labels = 1
	y_val = y_val.reshape(np.shape(test_predictions))

Function Documentation

ApplyInterpolation()

def ModelTrainingAndValidation.ApplyInterpolation	(		y_data,
			y_data_interpolation
	)

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Interpolation_MSE()

def ModelTrainingAndValidation.Interpolation_MSE	(		y_true,
			y_pred
	)

Custom Loss function evaluating the error of an interpolation.

LogCosh_plus_LogMSE()

def ModelTrainingAndValidation.LogCosh_plus_LogMSE	(		y_true,
			y_pred
	)

LogCosh_plus_LogMSE_plus_MAE()

def ModelTrainingAndValidation.LogCosh_plus_LogMSE_plus_MAE	(		y_true,
			y_pred
	)

LogCosh_plus_MAE()

def ModelTrainingAndValidation.LogCosh_plus_MAE	(		y_true,
			y_pred
	)

LogCosh_plus_MSE()

def ModelTrainingAndValidation.LogCosh_plus_MSE	(		y_true,
			y_pred
	)

main()

def ModelTrainingAndValidation.main	(	*	args,
		**	kwargs
	)

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make_variable()

def ModelTrainingAndValidation.make_variable	(		tuple,
			initializer
	)

ModelEvaluation()

def ModelTrainingAndValidation.ModelEvaluation

(

*

args

)

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MSE_plus_LogMSE()

def ModelTrainingAndValidation.MSE_plus_LogMSE	(		y_true,
			y_pred
	)

MSE_plus_MAE()

def ModelTrainingAndValidation.MSE_plus_MAE	(		y_true,
			y_pred
	)

plot_interpol()

def ModelTrainingAndValidation.plot_interpol	(		xesnew,
			ynew
	)

shape()

def ModelTrainingAndValidation.shape

(

tensor

)

Variable Documentation

size_labels

int ModelTrainingAndValidation.size_labels = 1

size_predictions

int ModelTrainingAndValidation.size_predictions = 1

test_predictions

ModelTrainingAndValidation.test_predictions = ModelEvaluation(model, validation_generator)

test data:

OBTAIN PREDICTIONS ###

threshold

ModelTrainingAndValidation.threshold

xgrid

ModelTrainingAndValidation.xgrid = np.arange(0.0,1.0*inp.Npoints)

y_val

ModelTrainingAndValidation.y_val = y_val.reshape(np.shape(test_predictions))

ygrid

ModelTrainingAndValidation.ygrid = np.arange(0.0,1.0*inp.Npoints)