niteshade.models.MNISTClassifier

class niteshade.models.MNISTClassifier(optimizer='sgd', loss_func='nll', lr=0.01, optim_kwargs={}, loss_kwargs={}, seed=None)

Bases: niteshade.models.BaseModel

Pre-defined classifier for the MNIST dataset. The architecture consists of two convolutional layers (using MaxPool2d and Dropout2d) and two dense layers with a LogSoftmax output activation. By default, the model uses torch.optim.SGD() as an optimiser with a learning rate of 0.01 and nn.NLLLoss() as a loss function. Additional parameters may be passed for these through the arguments optim_kwargs and loss_kwargs, respectively.

Parameters

  • optimizer (str) –

    String specifying optimizer to use in training neural network (Default = “sgd”). Options:

    ”adam”: torch.optim.Adam(), “adagrad”: torch.optim.Adagrad(), “sgd”: torch.optim.SGD()

  • loss_func (str) –

    String specifying loss function to use in training neural network (Default = “nll”). Options:

    ”mse”: nn.MSELoss(), “nll”: nn.NLLLoss(), “bce”: nn.BCELoss(), “cross_entropy”: nn.CrossEntropyLoss().

  • lr (float) – Learning rate to use in training neural network (Default = 0.01).

  • optim_kwargs (dict) – dictionary containing additional optimizer key-word arguments (Default = {}).

  • loss_kwargs (dict) – dictionary containing additional key-word arguments for the loss function (Default = {}).

__init__(optimizer='sgd', loss_func='nll', lr=0.01, optim_kwargs={}, loss_kwargs={}, seed=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__([optimizer, loss_func, lr, ...])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

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.

evaluate(X_test, y_test[, batch_size])

Test the accuracy of the iris classifier on a test set.

extra_repr()

Set the extra representation of the module

float()

Casts all floating point parameters and buffers to float datatype.

forward(x)

Forward method for model (needed as subclass of nn.Module).

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[, 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, remove_duplicate])

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.

predict(x)

Predict on a data sample.

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_full_backward_hook(hook)

Registers a backward hook on the module.

register_module(name, module)

Alias for add_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.

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.

step(X_batch, y_batch)

Perform a step of gradient descent on the passed inputs (X_batch) and labels (y_batch).

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

to_empty(*, device)

Moves the parameters and buffers to the specified device without copying storage.

train([mode])

Sets the module in training mode.

type(dst_type)

Casts all parameters and buffers to dst_type.

xpu([device])

Moves all model parameters and buffers to the XPU.

zero_grad([set_to_none])

Sets gradients of all model parameters to zero.

Attributes

T_destination

alias of TypeVar('T_destination', bound=Mapping[str, torch.Tensor])

dump_patches

This allows better BC support for load_state_dict().
evaluate(X_test, y_test, batch_size=32)

Test the accuracy of the iris classifier on a test set.

Parameters

  • X_test (np.ndarray) – test input data.

  • y_test (np.ndarray) – test target data.

forward(x)

Forward method for model (needed as subclass of nn.Module).

predict(x)

Predict on a data sample.