MLP

class caldera.blocks.MLP(*latent_sizes, layer_norm=True, dropout=None, activation=<class 'torch.nn.modules.activation.LeakyReLU'>)[source]

A multilayer perceptron.

Parameters

  • latent_sizes (List[int]) –

  • layer_norm (bool) –

  • dropout (Optional[float]) –

  • activation (Callable) –

  • latent_sizes –

  • layer_norm –

  • dropout –

  • activation –

__init__(*latent_sizes, layer_norm=True, dropout=None, activation=<class 'torch.nn.modules.activation.LeakyReLU'>)[source]
Parameters

  • latent_sizes (List[int]) –

  • layer_norm (bool) –

  • dropout (Optional[float]) –

  • activation (Callable) –

Methods

__init__(*latent_sizes[, layer_norm, …])

type latent_sizes

List[int]

add_module(name, module)

Adds a child module to the current module.

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

buffers([recurse])

Returns an iterator over module buffers.

children()

Returns an iterator over immediate children modules.

cpu()

Moves all model parameters and buffers to the CPU.

cuda([device])

Moves all model parameters and buffers to the GPU.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Sets the module in evaluation mode.

extra_repr()

Set the extra representation of the module

float()

Casts all floating point parameters and buffers to float datatype.

forward(x)

half()

Casts all floating point parameters and buffers to half datatype.

load_state_dict(state_dict[, strict])

Copies parameters and buffers from state_dict into this module and its descendants.

modules()

Returns an iterator over all modules in the network.

named_buffers([prefix, recurse])

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix])

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse])

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Returns an iterator over module parameters.

register_backward_hook(hook)

Registers a backward hook on the module.

register_buffer(name, tensor[, persistent])

Adds a buffer to the module.

register_forward_hook(hook)

Registers a forward hook on the module.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

register_parameter(name, param)

Adds a parameter to the module.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

share_memory()

param self

state_dict([destination, prefix, keep_vars])

Returns a dictionary containing a whole state of the module.

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

train([mode])

Sets the module in training mode.

type(dst_type)

Casts all parameters and buffers to dst_type.

zero_grad()

Sets gradients of all model parameters to zero.

Attributes

T_destination

dump_patches

__init__(*latent_sizes, layer_norm=True, dropout=None, activation=<class 'torch.nn.modules.activation.LeakyReLU'>)[source]
Parameters

  • latent_sizes (List[int]) –

  • layer_norm (bool) –

  • dropout (Optional[float]) –

  • activation (Callable) –