cuvis_ai.supervised.sklearn_wrapped.MLP

class cuvis_ai.supervised.sklearn_wrapped.MLP(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)

Bases: Node, BaseSupervised, SklearnWrapped

__init__(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)

Methods

__init__([hidden_layer_sizes, activation, ...])
check_input_dim(X)	Check that the parameters for the input data data match user expectations
check_output_dim(X)	Check that the parameters for the output data data match user expectations
fit(X, Y[, warm_start])
forward(X)	Transform the input data.
get_fit_requested_meta()
get_forward_requested_meta()
load(params, data_dir)	Load from serialized format into an object
serialize(data_dir)	Convert the class into a serialized representation
set_fit_meta_request(**kwargs)
set_forward_meta_request(**kwargs)

Attributes

input_dim	Returns the needed shape for the input data.
output_dim	Returns the shape for the output data.

check_input_dim(X)

Check that the parameters for the input data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

check_output_dim(X)

Check that the parameters for the output data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

fit(X: ndarray, Y: ndarray, warm_start=False)

forward(X: ndarray)

Transform the input data.

Parameters: X (array-like): Input data.

Returns: Transformed data.

get_fit_requested_meta()

get_forward_requested_meta()

property input_dim

Returns the needed shape for the input data. If a dimension is not important, it will return -1 in the specific position.

Returns: (tuple) needed shape for data

load(params: dict, data_dir: Path) → None

Load from serialized format into an object

property output_dim

Returns the shape for the output data. If a dimension is dependent on the input, it will return -1 in the specific position.

Returns: (tuple) expected output shape for data

serialize(data_dir: Path) → dict

Convert the class into a serialized representation

set_fit_meta_request(**kwargs)

set_forward_meta_request(**kwargs)