python.dgbpy.mlmodel_keras_dGB

Module Contents

Classes

dGB_UnetSeg
dGB_UnetReg
dGB_LeNet_Classifier
dGB_LeNet_Regressor

Functions

_to_tensor(x, dtype)
root_mean_squared_error(y_true, y_pred)
getAdamOpt(learning_rate=0.0001)
cross_entropy_balanced(y_true, y_pred)
compile_model(model, nroutputs, isregression, isunet, learnrate)
dGBUNet(model_shape, nroutputs, predtype)
dGBLeNet(model_shape, nroutputs, predtype)

python.dgbpy.mlmodel_keras_dGB._to_tensor(x, dtype)

python.dgbpy.mlmodel_keras_dGB.root_mean_squared_error(y_true, y_pred)

python.dgbpy.mlmodel_keras_dGB.getAdamOpt(learning_rate=0.0001)

python.dgbpy.mlmodel_keras_dGB.cross_entropy_balanced(y_true, y_pred)

python.dgbpy.mlmodel_keras_dGB.compile_model(model, nroutputs, isregression, isunet, learnrate)

python.dgbpy.mlmodel_keras_dGB.dGBUNet(model_shape, nroutputs, predtype)

class python.dgbpy.mlmodel_keras_dGB.dGB_UnetSeg

Bases: dgbpy.keras_classes.UserModel

uiname = dGB UNet Segmentation

uidescription = dGBs Unet image segmentation

predtype

outtype

dimtype

_make_model(self, model_shape, nroutputs, learnrate)

class python.dgbpy.mlmodel_keras_dGB.dGB_UnetReg

Bases: dgbpy.keras_classes.UserModel

uiname = dGB UNet Regression

uidescription = dGBs Unet image regression

predtype

outtype

dimtype

_make_model(self, model_shape, nroutputs, learnrate)

python.dgbpy.mlmodel_keras_dGB.dGBLeNet(model_shape, nroutputs, predtype)

class python.dgbpy.mlmodel_keras_dGB.dGB_LeNet_Classifier

Bases: dgbpy.keras_classes.UserModel

uiname = dGB LeNet classifier

uidescription = dGBs LeNet classifier Keras model in UserModel form

predtype

outtype

dimtype

_make_model(self, input_shape, nroutputs, learnrate)

class python.dgbpy.mlmodel_keras_dGB.dGB_LeNet_Regressor

Bases: dgbpy.keras_classes.UserModel

uiname = dGB LeNet regressor

uidescription = dGBs LeNet regressor Keras model in UserModel form

predtype

outtype

dimtype

_make_model(self, input_shape, nroutputs, learnrate)