CNN

class kgpy.ctis.inversion.cnn.CNN(model_forward, model_inverse)

Bases: Inversion, Pickleable

Parameters:

  • model_forward (Callable) –

  • model_inverse (Sequential) –

__init__(model_forward, model_inverse)
Parameters:

  • model_forward (Callable) –

  • model_inverse (Sequential) –

Return type:

None

Attributes

model_forward

model_inverse

Methods

__init__(model_forward, model_inverse)

from_pickle([path])

model_inverse_initial(input_shape[, ...])

rtype:

keras.src.engine.sequential.Sequential

to_pickle([path])

train(model_forward, cube_training, ...)

rtype:

kgpy.ctis.inversion.cnn.CNN

Inheritance Diagram

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classmethod from_pickle(path=None)
Parameters:

path (Path | None) –

static model_inverse_initial(input_shape, n_filters=32, kernel_size=7, growth_factor=2, alpha=0.1, dropout_rate=0.01)
Return type:

keras.src.engine.sequential.Sequential

Parameters:

  • input_shape (Tuple[int | None, int | None, int | None, int | None]) –

  • n_filters (int) –

  • kernel_size (int) –

  • growth_factor (int) –

  • alpha (float) –

  • dropout_rate (float) –

to_pickle(path=None)
Parameters:

path (Path | None) –

classmethod train(model_forward, cube_training, cube_validation)
Return type:

kgpy.ctis.inversion.cnn.CNN

Parameters:

  • model_forward (Callable) –

  • cube_training (Cube) –

  • cube_validation (Cube) –

model_forward: typing.Callable
model_inverse: keras.src.engine.sequential.Sequential