flim.analysis.ml package

Submodules

flim.analysis.ml.autoencoder module

class flim.analysis.ml.autoencoder.AbsAutoencoder

Bases: torch.nn.modules.module.Module, sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X)

abstract get_description()

get_name()

training: bool

transform(X)

class flim.analysis.ml.autoencoder.Autoencoder_One_Six(nb_param=2, hidden_size=6)

Bases: flim.analysis.ml.autoencoder.AbsAutoencoder

fit(loaders)

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_description()

training: bool

transform(X)

class flim.analysis.ml.autoencoder.Autoencoder_One_Three(nb_param=2, hidden_size=3)

Bases: flim.analysis.ml.autoencoder.AbsAutoencoder

fit(loaders)

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_description()

training: bool

transform(X)

class flim.analysis.ml.autoencoder.Autoencoder_Two_Five(nb_param=2, hidden_size_1=5, hidden_size_2=1)

Bases: flim.analysis.ml.autoencoder.AbsAutoencoder

fit(loaders)

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_description()

training: bool

transform(X)

class flim.analysis.ml.autoencoder.Autoencoder_Two_Ten(nb_param=2, hidden_size_1=10, hidden_size_2=1)

Bases: flim.analysis.ml.autoencoder.AbsAutoencoder

fit(loaders)

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_description()

training: bool

transform(X)

flim.analysis.ml.autoencoder.create_instance(clazz, **kwargs)

flim.analysis.ml.autoencoder.get_autoencoder_classes()

flim.analysis.ml.autoencoder.init_autoencoders()

Module contents