dgbpy.mlmodel_keras_dGB
Module Contents
Classes
Abstract base class for user defined Keras machine learning models |
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Abstract base class for user defined Keras machine learning models |
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Abstract base class for user defined Keras machine learning models |
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Abstract base class for user defined Keras machine learning models |
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Abstract base class for user defined Keras machine learning models |
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Abstract base class for user defined Keras machine learning models |
Functions
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- dgbpy.mlmodel_keras_dGB._to_tensor(x, dtype)
- dgbpy.mlmodel_keras_dGB.root_mean_squared_error(y_true, y_pred)
- dgbpy.mlmodel_keras_dGB.getAdamOpt(learning_rate=0.0001)
- dgbpy.mlmodel_keras_dGB.cross_entropy_balanced(y_true, y_pred)
- dgbpy.mlmodel_keras_dGB.compile_model(model, nroutputs, isregression, isunet, learnrate)
- dgbpy.mlmodel_keras_dGB.dGBUNet(model_shape, nroutputs, predtype)
- class dgbpy.mlmodel_keras_dGB.dGB_UnetSeg
Bases:
dgbpy.keras_classes.UserModelAbstract base class for user defined Keras machine learning models
This module provides support for users to add their own machine learning models to OpendTect.
It defines an abstract base class. Users derive there own model classes from this base class and implement the _make_model static method to define the structure of the keras model. The users model definition should be saved in a file name with “mlmodel_keras” as a prefix and be at the top level of the module search path so it can be discovered.
The “mlmodel_keras” class should also define some class variables describing the class: uiname : str - this is the name that will appear in the user interface uidescription : str - this is a short description which may be displayed to help the user predtype : DataPredType enum - type of prediction (must be member of DataPredType enum) outtype: OutputType enum - output shape type (OutputType.Pixel or OutputType.Image) dimtype : DimType enum - the input dimensions supported by model (must be member of DimType enum)
Examples
from dgbpy.keras_classes import UserModel, DataPredType, OutputType, DimType
- class myModel(UserModel):
uiname = ‘mymodel’ uidescription = ‘short description of model’ predtype = DataPredType.Classification outtype = OutputType.Pixel dimtype = DimType.D3
- def _make_model(self, input_shape, nroutputs, learnrate, data_format):
inputs = Input(input_shape) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(inputs) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(conv1) pool1 = MaxPooling3D(pool,size=(2,2,2))(conv1) … conv8 = Conv3D(1, (1,1,1,), activation=’sigmoid’)(conv7)
model = Model(inputs=[inputs], outputs=[conv8]) model.compile(optimizer = Adam(lr = 1e-4), loss = cross_entropy_balanced, metrics = [‘accuracy’]) return model
- uiname = dGB UNet Segmentation
- uidescription = dGBs Unet image segmentation
- predtype
- outtype
- dimtype
- _make_model(self, model_shape, nroutputs, learnrate)
Abstract static method that defines a machine learning model.
Must be implemented in the user’s derived class
Parameters
input_shape : tuple Defines input data shape in the Keras default data_format for the current backend. For the TensorFlow backend the default data_format is ‘channels_last’ nroutputs : int (number of discrete classes for a classification) Number of outputs learnrate : float The step size applied at each iteration to move toward a minimum of the loss function
Returns
a compiled keras model
- class dgbpy.mlmodel_keras_dGB.dGB_UnetReg
Bases:
dgbpy.keras_classes.UserModelAbstract base class for user defined Keras machine learning models
This module provides support for users to add their own machine learning models to OpendTect.
It defines an abstract base class. Users derive there own model classes from this base class and implement the _make_model static method to define the structure of the keras model. The users model definition should be saved in a file name with “mlmodel_keras” as a prefix and be at the top level of the module search path so it can be discovered.
The “mlmodel_keras” class should also define some class variables describing the class: uiname : str - this is the name that will appear in the user interface uidescription : str - this is a short description which may be displayed to help the user predtype : DataPredType enum - type of prediction (must be member of DataPredType enum) outtype: OutputType enum - output shape type (OutputType.Pixel or OutputType.Image) dimtype : DimType enum - the input dimensions supported by model (must be member of DimType enum)
Examples
from dgbpy.keras_classes import UserModel, DataPredType, OutputType, DimType
- class myModel(UserModel):
uiname = ‘mymodel’ uidescription = ‘short description of model’ predtype = DataPredType.Classification outtype = OutputType.Pixel dimtype = DimType.D3
- def _make_model(self, input_shape, nroutputs, learnrate, data_format):
inputs = Input(input_shape) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(inputs) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(conv1) pool1 = MaxPooling3D(pool,size=(2,2,2))(conv1) … conv8 = Conv3D(1, (1,1,1,), activation=’sigmoid’)(conv7)
model = Model(inputs=[inputs], outputs=[conv8]) model.compile(optimizer = Adam(lr = 1e-4), loss = cross_entropy_balanced, metrics = [‘accuracy’]) return model
- uiname = dGB UNet Regression
- uidescription = dGBs Unet image regression
- predtype
- outtype
- dimtype
- _make_model(self, model_shape, nroutputs, learnrate)
Abstract static method that defines a machine learning model.
Must be implemented in the user’s derived class
Parameters
input_shape : tuple Defines input data shape in the Keras default data_format for the current backend. For the TensorFlow backend the default data_format is ‘channels_last’ nroutputs : int (number of discrete classes for a classification) Number of outputs learnrate : float The step size applied at each iteration to move toward a minimum of the loss function
Returns
a compiled keras model
- dgbpy.mlmodel_keras_dGB.dGBLeNet(model_shape, nroutputs, predtype)
- class dgbpy.mlmodel_keras_dGB.dGB_LeNet_Classifier
Bases:
dgbpy.keras_classes.UserModelAbstract base class for user defined Keras machine learning models
This module provides support for users to add their own machine learning models to OpendTect.
It defines an abstract base class. Users derive there own model classes from this base class and implement the _make_model static method to define the structure of the keras model. The users model definition should be saved in a file name with “mlmodel_keras” as a prefix and be at the top level of the module search path so it can be discovered.
The “mlmodel_keras” class should also define some class variables describing the class: uiname : str - this is the name that will appear in the user interface uidescription : str - this is a short description which may be displayed to help the user predtype : DataPredType enum - type of prediction (must be member of DataPredType enum) outtype: OutputType enum - output shape type (OutputType.Pixel or OutputType.Image) dimtype : DimType enum - the input dimensions supported by model (must be member of DimType enum)
Examples
from dgbpy.keras_classes import UserModel, DataPredType, OutputType, DimType
- class myModel(UserModel):
uiname = ‘mymodel’ uidescription = ‘short description of model’ predtype = DataPredType.Classification outtype = OutputType.Pixel dimtype = DimType.D3
- def _make_model(self, input_shape, nroutputs, learnrate, data_format):
inputs = Input(input_shape) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(inputs) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(conv1) pool1 = MaxPooling3D(pool,size=(2,2,2))(conv1) … conv8 = Conv3D(1, (1,1,1,), activation=’sigmoid’)(conv7)
model = Model(inputs=[inputs], outputs=[conv8]) model.compile(optimizer = Adam(lr = 1e-4), loss = cross_entropy_balanced, metrics = [‘accuracy’]) return model
- uiname = dGB LeNet classifier
- uidescription = dGBs LeNet classifier Keras model in UserModel form
- predtype
- outtype
- dimtype
- _make_model(self, input_shape, nroutputs, learnrate)
Abstract static method that defines a machine learning model.
Must be implemented in the user’s derived class
Parameters
input_shape : tuple Defines input data shape in the Keras default data_format for the current backend. For the TensorFlow backend the default data_format is ‘channels_last’ nroutputs : int (number of discrete classes for a classification) Number of outputs learnrate : float The step size applied at each iteration to move toward a minimum of the loss function
Returns
a compiled keras model
- class dgbpy.mlmodel_keras_dGB.dGB_LeNet_Regressor
Bases:
dgbpy.keras_classes.UserModelAbstract base class for user defined Keras machine learning models
This module provides support for users to add their own machine learning models to OpendTect.
It defines an abstract base class. Users derive there own model classes from this base class and implement the _make_model static method to define the structure of the keras model. The users model definition should be saved in a file name with “mlmodel_keras” as a prefix and be at the top level of the module search path so it can be discovered.
The “mlmodel_keras” class should also define some class variables describing the class: uiname : str - this is the name that will appear in the user interface uidescription : str - this is a short description which may be displayed to help the user predtype : DataPredType enum - type of prediction (must be member of DataPredType enum) outtype: OutputType enum - output shape type (OutputType.Pixel or OutputType.Image) dimtype : DimType enum - the input dimensions supported by model (must be member of DimType enum)
Examples
from dgbpy.keras_classes import UserModel, DataPredType, OutputType, DimType
- class myModel(UserModel):
uiname = ‘mymodel’ uidescription = ‘short description of model’ predtype = DataPredType.Classification outtype = OutputType.Pixel dimtype = DimType.D3
- def _make_model(self, input_shape, nroutputs, learnrate, data_format):
inputs = Input(input_shape) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(inputs) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(conv1) pool1 = MaxPooling3D(pool,size=(2,2,2))(conv1) … conv8 = Conv3D(1, (1,1,1,), activation=’sigmoid’)(conv7)
model = Model(inputs=[inputs], outputs=[conv8]) model.compile(optimizer = Adam(lr = 1e-4), loss = cross_entropy_balanced, metrics = [‘accuracy’]) return model
- uiname = dGB LeNet regressor
- uidescription = dGBs LeNet regressor Keras model in UserModel form
- predtype
- outtype
- dimtype
- _make_model(self, input_shape, nroutputs, learnrate)
Abstract static method that defines a machine learning model.
Must be implemented in the user’s derived class
Parameters
input_shape : tuple Defines input data shape in the Keras default data_format for the current backend. For the TensorFlow backend the default data_format is ‘channels_last’ nroutputs : int (number of discrete classes for a classification) Number of outputs learnrate : float The step size applied at each iteration to move toward a minimum of the loss function
Returns
a compiled keras model
- dgbpy.mlmodel_keras_dGB.UNet_VGG19(model_shape, nroutputs, predtype)
- class dgbpy.mlmodel_keras_dGB.dGB_UNetSeg_VGG19
Bases:
dgbpy.keras_classes.UserModelAbstract base class for user defined Keras machine learning models
This module provides support for users to add their own machine learning models to OpendTect.
It defines an abstract base class. Users derive there own model classes from this base class and implement the _make_model static method to define the structure of the keras model. The users model definition should be saved in a file name with “mlmodel_keras” as a prefix and be at the top level of the module search path so it can be discovered.
The “mlmodel_keras” class should also define some class variables describing the class: uiname : str - this is the name that will appear in the user interface uidescription : str - this is a short description which may be displayed to help the user predtype : DataPredType enum - type of prediction (must be member of DataPredType enum) outtype: OutputType enum - output shape type (OutputType.Pixel or OutputType.Image) dimtype : DimType enum - the input dimensions supported by model (must be member of DimType enum)
Examples
from dgbpy.keras_classes import UserModel, DataPredType, OutputType, DimType
- class myModel(UserModel):
uiname = ‘mymodel’ uidescription = ‘short description of model’ predtype = DataPredType.Classification outtype = OutputType.Pixel dimtype = DimType.D3
- def _make_model(self, input_shape, nroutputs, learnrate, data_format):
inputs = Input(input_shape) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(inputs) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(conv1) pool1 = MaxPooling3D(pool,size=(2,2,2))(conv1) … conv8 = Conv3D(1, (1,1,1,), activation=’sigmoid’)(conv7)
model = Model(inputs=[inputs], outputs=[conv8]) model.compile(optimizer = Adam(lr = 1e-4), loss = cross_entropy_balanced, metrics = [‘accuracy’]) return model
- uiname = dGB UNet VGG19 Segmentation
- uidescription = dGB UNet VGG19 Segmentation Keras model in UserModel form
- predtype
- outtype
- dimtype
- _make_model(self, input_shape, nroutputs, learnrate)
Abstract static method that defines a machine learning model.
Must be implemented in the user’s derived class
Parameters
input_shape : tuple Defines input data shape in the Keras default data_format for the current backend. For the TensorFlow backend the default data_format is ‘channels_last’ nroutputs : int (number of discrete classes for a classification) Number of outputs learnrate : float The step size applied at each iteration to move toward a minimum of the loss function
Returns
a compiled keras model
- class dgbpy.mlmodel_keras_dGB.dGB_UNetReg_VGG19
Bases:
dgbpy.keras_classes.UserModelAbstract base class for user defined Keras machine learning models
This module provides support for users to add their own machine learning models to OpendTect.
It defines an abstract base class. Users derive there own model classes from this base class and implement the _make_model static method to define the structure of the keras model. The users model definition should be saved in a file name with “mlmodel_keras” as a prefix and be at the top level of the module search path so it can be discovered.
The “mlmodel_keras” class should also define some class variables describing the class: uiname : str - this is the name that will appear in the user interface uidescription : str - this is a short description which may be displayed to help the user predtype : DataPredType enum - type of prediction (must be member of DataPredType enum) outtype: OutputType enum - output shape type (OutputType.Pixel or OutputType.Image) dimtype : DimType enum - the input dimensions supported by model (must be member of DimType enum)
Examples
from dgbpy.keras_classes import UserModel, DataPredType, OutputType, DimType
- class myModel(UserModel):
uiname = ‘mymodel’ uidescription = ‘short description of model’ predtype = DataPredType.Classification outtype = OutputType.Pixel dimtype = DimType.D3
- def _make_model(self, input_shape, nroutputs, learnrate, data_format):
inputs = Input(input_shape) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(inputs) conv1 = Conv3D(2, (3,3,3), activation=’relu’, padding=’same’)(conv1) pool1 = MaxPooling3D(pool,size=(2,2,2))(conv1) … conv8 = Conv3D(1, (1,1,1,), activation=’sigmoid’)(conv7)
model = Model(inputs=[inputs], outputs=[conv8]) model.compile(optimizer = Adam(lr = 1e-4), loss = cross_entropy_balanced, metrics = [‘accuracy’]) return model
- uiname = dGB UNet VGG19 Regression
- uidescription = dGB UNet VGG19 Regression Keras model in UserModel form
- predtype
- outtype
- dimtype
- _make_model(self, model_shape, nroutputs, learnrate)
Abstract static method that defines a machine learning model.
Must be implemented in the user’s derived class
Parameters
input_shape : tuple Defines input data shape in the Keras default data_format for the current backend. For the TensorFlow backend the default data_format is ‘channels_last’ nroutputs : int (number of discrete classes for a classification) Number of outputs learnrate : float The step size applied at each iteration to move toward a minimum of the loss function
Returns
a compiled keras model