skorch.toy

Contains toy functions and classes for quick prototyping and testing.

class skorch.toy.MLPModule(input_units=20, output_units=2, hidden_units=10, num_hidden=1, nonlin=ReLU(), output_nonlin=None, dropout=0, squeeze_output=False)[source]

A simple multi-layer perceptron module.

This can be adapted for usage in different contexts, e.g. binary and multi-class classification, regression, etc.

Parameters:
input_units : int (default=20)

Number of input units.

output_units : int (default=2)

Number of output units.

hidden_units : int (default=10)

Number of units in hidden layers.

num_hidden : int (default=1)

Number of hidden layers.

nonlin : torch.nn.Module instance (default=torch.nn.ReLU())

Non-linearity to apply after hidden layers.

output_nonlin : torch.nn.Module instance or None (default=None)

Non-linearity to apply after last layer, if any.

dropout : float (default=0)

Dropout rate. Dropout is applied between layers.

squeeze_output : bool (default=False)

Whether to squeeze output. Squeezing can be helpful if you wish your output to be 1-dimensional (e.g. for NeuralNetBinaryClassifier).

Methods

add_module(name, module) Adds a child module to the current module.
apply(fn, None]) 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, None] = 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.
half() Casts all floating point parameters and buffers to half datatype.
load_state_dict(state_dict, torch.Tensor], …) 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, None]) Registers a forward hook on the module.
register_forward_pre_hook(hook, None]) 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.
reset_params() (Re)set all parameters.
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, str]) Casts all parameters and buffers to dst_type.
zero_grad(set_to_none) Sets gradients of all model parameters to zero.
__call__  
share_memory  
forward(X)[source]

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.

reset_params()[source]

(Re)set all parameters.

skorch.toy.make_binary_classifier(squeeze_output=True, **kwargs)[source]

Return a multi-layer perceptron to be used with NeuralNetBinaryClassifier.

Parameters:
input_units : int (default=20)

Number of input units.

output_units : int (default=2)

Number of output units.

hidden_units : int (default=10)

Number of units in hidden layers.

num_hidden : int (default=1)

Number of hidden layers.

nonlin : torch.nn.Module instance (default=torch.nn.ReLU())

Non-linearity to apply after hidden layers.

dropout : float (default=0)

Dropout rate. Dropout is applied between layers.

skorch.toy.make_classifier(output_nonlin=Softmax(dim=-1), **kwargs)[source]

Return a multi-layer perceptron to be used with NeuralNetClassifier.

Parameters:
input_units : int (default=20)

Number of input units.

output_units : int (default=2)

Number of output units.

hidden_units : int (default=10)

Number of units in hidden layers.

num_hidden : int (default=1)

Number of hidden layers.

nonlin : torch.nn.Module instance (default=torch.nn.ReLU())

Non-linearity to apply after hidden layers.

dropout : float (default=0)

Dropout rate. Dropout is applied between layers.

skorch.toy.make_regressor(output_units=1, **kwargs)[source]

Return a multi-layer perceptron to be used with NeuralNetRegressor.

Parameters:
input_units : int (default=20)

Number of input units.

output_units : int (default=1)

Number of output units.

hidden_units : int (default=10)

Number of units in hidden layers.

num_hidden : int (default=1)

Number of hidden layers.

nonlin : torch.nn.Module instance (default=torch.nn.ReLU())

Non-linearity to apply after hidden layers.

dropout : float (default=0)

Dropout rate. Dropout is applied between layers.