niteshade.models.CifarClassifier

class niteshade.models.CifarClassifier(optimizer='adam', loss_func='cross_entropy', lr=0.0001, optim_kwargs={'weight_decay': 1e-06}, loss_kwargs={}, seed=None)

Bases: niteshade.models.BaseModel

ResNet-18 classifier inheriting from BaseModel for the torchvision CIFAR10 dataset. Achieves 80% accuracy on the held out test set in 20 epochs using mini-batches of size 32. More details on ResNet-18 can be found on the paper “Deep Residual Learning for Image Recognition” by Zhang et al (https://arxiv.org/abs/1512.03385).

Parameters

• optimizer (str) –

  String specifying optimizer to use in training neural network (Default = “adam”). 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 = “cross_entropy”). 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 = 0.0001).

• optim_kwargs (dict) – dictionary containing additional optimizer key-word arguments (Default = {“weight_decay”: 1e-6}).

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

__init__(optimizer='adam', loss_func='cross_entropy', lr=0.0001, optim_kwargs={'weight_decay': 1e-06}, 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 CIFAR10 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)	Perform a forward pass through the model.
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 CIFAR10 classifier on a test set.

Parameters

• X_test (np.ndarray, torch.Tensor) – test input data.

• y_test (np.ndarray, torch.Tensor) – test target data.

• batch_size (int) – Size of mini-batches to test model on.

Returns

Accuracy on test set.

Return type

accuracy (float)

forward(x)

Perform a forward pass through the model.

Parameters

x (np.ndarray or torch.Tensor) – Input array of shape (batch_size, input_shape).

Returns

Predictions from current state of

the model.

Return type

(np.ndarray or torch.Tensor)

predict(x)

Predict on a data sample.