Configuration Parameters

For each mode, common configuration parameters, TaskParams, are required to run the task. These parameters are used to define the task, datasets, model, and other settings. Rather than defining separate configuration files for each mode, a single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.

QuantizationParams

Quantization parameters define the quantization-aware training (QAT) and post-training quantization (PTQ) settings. This is used for modes: train, evaluate, export, and demo.

Argument	Type	Opt/Req	Default	Description
enabled	bool	Optional	False	Enable quantization
qat	bool	Optional	False	Enable quantization aware training (QAT)
format	Literal[“int8”, “int16”, “float16”]	Optional	int8	Quantization mode
io_type	str	Optional	int8	I/O type
conversion	Literal[“keras”, “tflite”]	Optional	keras	Conversion method
debug	bool	Optional	False	Debug quantization
fallback	bool	Optional	False	Fallback to float32

NamedParams

Named parameters are used to provide custom parameters for a given object or callable where parameter types are not known ahead of time. For example, a dataset, ‘CustomDataset’, may require custom parameters such as ‘path’, ‘label’, ‘sampling_rate’, etc. When a task loads the dataset using name, the task will then unpack the custom parameters and pass them to the dataset initializer.

Argument	Type	Opt/Req	Default	Description
name	str	Required		Named parameters name
params	dict[str, Any]	Optional	{}	Named parameters

Python example

import sleepkit as sk

class CustomDataset(sk.Dataset):

    def __init__(self, a: int = 1, b: int = 2) -> None:
        self.a = a
        self.b = b

sk.DatasetFactory.register("custom", CustomDataset)

params = sk.TaskParams(
    datasets=[
        sk.NamedParams(
            name="custom",
            params=dict(a=1, b=2)
        )
    ]
)

FeatureParams

Argument	Type	Opt/Req	Default	Description
name	str	Required	feature	Feature set name
sampling_rate	float	Optional	250	Target sampling rate (Hz)
frame_size	int	Optional	1250	Frame size in samples
loader	str	Optional	hdf5	Data loader
feat_key	str	Optional	features	Feature key
label_key	str	Optional	labels	Label key
mask_key	str	Optional	mask	Mask key
feat_cols	list[str]|None	Optional	None	Feature columns
save_path	Path	Optional	tempfile.gettempdir	Save path
params	dict[str, Any]	Optional	{}	Feature Parameters

TaskParams

These parameters are supplied to a Task when running a given mode such as train, evaluate, export, or demo. A single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.

Argument	Type	Opt/Req	Default	Description	Mode
name	str	Required	experiment	Experiment name	All
project	str	Required	sleepkit	Project name	All
job_dir	Path	Optional	tempfile.gettempdir	Job output directory	All
datasets	list[NamedParams]	Optional	[]	Datasets	All
dataset_weights	list[float]|None	Optional	None	Dataset weights	All
force_download	bool	Optional	False	Force download dataset- overriding existing files	download
feature	FeatureParams	Optional	{}	Feature configuration	feature
sampling_rate	float	Optional	250	Target sampling rate (Hz)	feature
frame_size	int	Optional	1250	Frame size in samples	feature
samples_per_subject	int|list[int]	Optional	1000	Number train samples per subject	All
val_samples_per_subject	int|list[int]	Optional	1000	Number validation samples per subject	All
test_samples_per_subject	int|list[int]	Optional	1000	Number test samples per subject	All
preprocesses	list[NamedParams]	Optional	[]	Preprocesses	All
augmentations	list[NamedParams]	Optional	[]	Augmentations	All
num_classes	int	Optional	1	# of classes	All
class_map	dict[int, int]	Optional	{1: 1}	Class/label mapping	All
class_names	list[str]|None	Optional	None	Class names	All
train_subjects	float|None	Optional	None	# or proportion of subjects for training	All
val_subjects	float|None	Optional	None	# or proportion of subjects for validation	All
test_subjects	float|None	Optional	None	# or proportion of subjects for testing	All
val_file	Path|None	Optional	None	Path to load/store TFDS validation data	All
test_file	Path|None	Optional	None	Path to load/store TFDS test data	All
val_size	int|None	Optional	None	Number of samples for validation	All
test_size	int	Optional	10000	# samples for testing	All
resume	bool	Optional	False	Resume training	train
architecture	NamedParams|None	Optional	None	Custom model architecture	train
model_file	Path|None	Optional	None	Path to load/save model file (.keras)	All
use_logits	bool	Optional	True	Use logits output or softmax	All
weights_file	Path|None	Optional	None	Path to a checkpoint weights to load/save	All
quantization	QuantizationParams	Optional	{}	Quantization parameters	train, evaluate, export, demo
lr_rate	float	Optional	1e-3	Learning rate	train
lr_cycles	int	Optional	3	Number of learning rate cycles	train
lr_decay	float	Optional	0.9	Learning rate decay	train
label_smoothing	float	Optional	0	Label smoothing	train
batch_size	int	Optional	32	Batch size	train
buffer_size	int	Optional	100	Buffer cache size	train
epochs	int	Optional	50	Number of epochs	train
steps_per_epoch	int	Optional	10	Number of steps per epoch	train
val_steps_per_epoch	int	Optional	10	Number of validation steps	train
val_metric	Literal[“loss”, “acc”, “f1”]	Optional	loss	Performance metric	train
class_weights	Literal[“balanced”, “fixed”]	Optional	fixed	Class weights	train
threshold	float|None	Optional	None	Model output threshold	evaluate
test_metric	Literal[“loss”, “acc”, “f1”]	Optional	acc	Test metric	evaluate
test_metric_threshold	float|None	Optional	0.98	Validation metric threshold	evaluate
tflm_var_name	str	Optional	g_model	TFLite Micro C variable name	export
tflm_file	Path|None	Optional	None	Path to copy TFLM header file (e.g. ./model_buffer.h)	export
backend	str	Optional	pc	Backend	demo
demo_size	int|None	Optional	1000	# samples for demo	demo
display_report	bool	Optional	True	Display report	demo
seed	int|None	Optional	None	Random state seed	All
num_workers	int	Optional	os.cpu_count() or 1	Number of workers for parallel processing	All
verbose	int	Optional	1	Verbosity level	All