networks package

Submodules

networks.cnn module

class networks.cnn.MobileNetV2(num_classes=10, width_mult=1.0)

Bases: Module

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.

training: bool

networks.cnn.mobilenet_v2(pretrained=False, progress=True, device='cpu', **kwargs)

Constructs a MobileNetV2 architecture from “MobileNetV2: Inverted Residuals and Linear Bottlenecks”. :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

networks.linear_net module

class networks.linear_net.LinearNet(input_dim, out_dim)

Bases: Module

Linear Classifier with out units and no softmax

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.

training: bool

class networks.linear_net.LinearNetDefer(input_dim, out_dim)

Bases: Module

Linear Classifier with out+1 units and no softmax

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.

training: bool

class networks.linear_net.Linear_net_sig(input_dim, out_dim=1)

Bases: Module

Linear binary classifier

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.

training: bool

networks.non_linear_net module

class networks.non_linear_net.NonLinearNet(input_dim, out_dim)

Bases: Module

NonLinear Classifier

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.

training: bool

Module contents