neuralib.spikes.cascade.CascadeModelConfig

class neuralib.spikes.cascade.CascadeModelConfig

Bases: TypedDict

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
clear()
copy()
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()	Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

Attributes

model_name	Name of the model
sampling_rate	Sampling rate in Hz
training_datasets	Dataset of ground truth data (in folder 'Ground_truth')
placeholder_1	protect formatting
noise_levels	Noise levels for training (integers, normally 1-9)
placeholder_2	protect formatting
smoothing	Standard deviation of Gaussian smoothing in time (sec)
causal_kernel	Smoothing kernel is symmetric in time (0) or is causal (1)
windowsize	Windowsize in timepoints
before_frac	Fraction of timepoints before prediction point (0-1)
filter_sizes	Filter sizes for each convolutional layer
filter_numbers	Filter numbers for each convolutional layer
dense_expansion	For dense layer
loss_function	gradient-descent loss function
optimizer	Adagrad
nr_of_epochs	Number of training epochs per model
ensemble_size	Number of models trained for one noise level
batch_size	Batch size
training_finished	Yes / No / Running
verbose	level of status messages (0: minimal, 1: standard, 2: most, 3: all)

model_name: str

Name of the model

sampling_rate: Required[int]

Sampling rate in Hz

training_datasets: Required[list[str]]

Dataset of ground truth data (in folder ‘Ground_truth’)

placeholder_1: int

protect formatting

noise_levels: Required[list[int]]

Noise levels for training (integers, normally 1-9)

placeholder_2: int

protect formatting

smoothing: Required[float]

Standard deviation of Gaussian smoothing in time (sec)

causal_kernel: Required[int]

Smoothing kernel is symmetric in time (0) or is causal (1)

windowsize: Required[int]

Windowsize in timepoints

before_frac: Required[float]

Fraction of timepoints before prediction point (0-1)

filter_sizes: list[int]

Filter sizes for each convolutional layer

filter_numbers: list[int]

Filter numbers for each convolutional layer

dense_expansion: int

For dense layer

loss_function: str

gradient-descent loss function

optimizer: str

Adagrad

nr_of_epochs: int

Number of training epochs per model

ensemble_size: Required[int]

Number of models trained for one noise level

batch_size: Required[int]

Batch size

training_finished: Literal['Yes', 'No', 'Running']

Yes / No / Running

verbose: Required[int]

level of status messages (0: minimal, 1: standard, 2: most, 3: all)