MultiLayer Perceptron

class olympus.models.mlp.MLP(input_size, num_classes, layers=(), non_linearity=<function relu>, bias=True)

Bases: torch.nn.modules.module.Module

An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable. More on wikipedia

Attributes:

input_size: Tuple[int, …]

Accepted size (any)

num_classes: int

Number of output neurons

layers: List[int]

Size of hidden layers

non_linearity: Callable[[tensor], tensor]

Non linearity or activation function to apply for each layers historically sigmoid or tanh but relu is the most popular since it does not have as many numerical problems as the others.

bias: bool

Add bias weights to each layers

Methods

add_module	Adds a child module to the current module.
apply	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, torch.device, NoneType] = None)	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)	Defines the computation performed at every call.
get_buffer(target)	Returns the buffer given by target if it exists, otherwise throws an error.
get_extra_state()	Returns any extra state to include in the module’s state_dict.
get_parameter(target)	Returns the parameter given by target if it exists, otherwise throws an error.
get_submodule(target)	Returns the submodule given by target if it exists, otherwise throws an error.
half()	Casts all floating point parameters and buffers to half datatype.
load_state_dict(state_dict, Tensor], 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	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	Registers a backward hook on the module.
register_buffer(name, tensor, NoneType], …)	Adds a buffer to the module.
register_forward_hook(hook, NoneType])	Registers a forward hook on the module.
register_forward_pre_hook(hook, NoneType])	Registers a forward pre-hook on the module.
register_full_backward_hook	Registers a backward hook on the module.
register_parameter(name, param, NoneType])	Adds a parameter to the module.
requires_grad_(requires_grad)	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	This function is called from load_state_dict() to handle any extra state found within the state_dict.
share_memory()	See torch.Tensor.share_memory_()
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.
to_empty(*, device, torch.device])	Moves the parameters and buffers to the specified device without copying storage.
train(mode)	Sets the module in training mode.
type(dst_type, str])	Casts all parameters and buffers to dst_type.
xpu(device, torch.device, NoneType] = None)	Moves all model parameters and buffers to the XPU.
zero_grad(set_to_none)	Sets gradients of all model parameters to zero.

__call__

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.