multiml.task.keras.modules.mlp module

class multiml.task.keras.modules.mlp.MLPBlock(*args, **kwargs)

Bases: Model

__init__(layers=None, activation=None, activation_last=None, kernel_regularizer=None, bias_regularizer=None, batch_norm=False, *args, **kwargs)

Constructor.

Parameters:

  • layers (list) – list of hidden layers

  • activation (str) – activation function for MLP

  • activation_last (str) – activation function for the MLP last layer

  • batch_norm (bool) – use batch normalization

  • kernel_regularizer (str) – kernel regularizer

  • bias_regularizer (str) – bias regularizer

  • *args – Variable length argument list

  • **kwargs – Arbitrary keyword arguments

call(input_tensor, training=False)

Calls the model on new inputs.

In this case call just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Parameters:

  • inputs – A tensor or list of tensors.

  • training – Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.

  • mask – A mask or list of masks. A mask can be either a tensor or None (no mask).

Returns:

A tensor if there is a single output, or a list of tensors if there are more than one outputs.