smash.Model.set_nn_parameters_bias#

Model.set_nn_parameters_bias(value=None, initializer='zeros', random_state=None)[source]#

Set the values of the bias in the parameterization neural network.

Parameters:

valuelist[float or numpy.ndarray] or None, default None: The list of values to set to the biases of trainable layers. If an element of the list is a numpy.ndarray, its shape must be broadcastable into the bias shape of that layer. If not used, a default or specified initialization method will be used.
initializerstr, default ‘zeros’: Bias initialization method. Should be one of 'uniform', 'glorot_uniform', 'he_uniform', 'normal', 'glorot_normal', 'he_normal', 'zeros'. Only used if value is not set.
random_stateint or None, default None: Random seed used for the initialization in case of using initializer.

Note

If not given, the neural network parameters will be initialized with a random seed.

See also

Model.nn_parameters: The weight and bias of the parameterization neural network.
Model.get_nn_parameters_bias: Get the bias of the parameterization neural network.

Examples

>>> from smash.factory import load_dataset
>>> setup, mesh = load_dataset("cance")

Set the hydrological module to 'gr4_mlp' (hybrid hydrological model with multilayer perceptron)

>>> setup["hydrological_module"] = "gr4_mlp"

Set the number of neurons in the hidden layer to 6 (the default value is 16, if not set)

>>> setup["hidden_neuron"] = 6
>>> model = smash.Model(setup, mesh)

Set random biases using Glorot normal initializer

>>> model.set_nn_parameters_bias(initializer="glorot_normal", random_state=0)
>>> model.get_nn_parameters_bias()
[array([ 0.94292563,  0.21389303,  0.5231575 ,  1.1978078 ,  0.99825174, -0.522377  ],
    dtype=float32),
array([ 0.60088867, -0.09572671, -0.06528133,  0.2596853 ],
    dtype=float32)]

The output contains a list of bias values for trainable layers.

Set biases with specified values

>>> import numpy as np
>>> np.random.seed(0)
>>> model.set_nn_parameters_bias([np.random.normal(size=6), 0])
>>> model.get_nn_parameters_bias()
[array([ 1.7640524,  0.4001572,  0.978738 ,  2.2408931,  1.867558 ,
        -0.9772779], dtype=float32), array([0., 0., 0., 0.], dtype=float32)]

smash.Model.set_nn_parameters_bias#

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