multiml.task.keras.modules.softmax_dense_layer module

class multiml.task.keras.modules.softmax_dense_layer.SoftMaxDenseLayer(*args, **kwargs)

Bases: Layer

__init__(kernel_initializer='zeros', kernel_regularizer=None, dropout_rate=None, **kwargs)

Constructor.

Parameters:

• kernel_initializer (str) – initializer for softmax weights

• kernel_regularizer (str) – regularizer for softmax weights

• dropout_rate (float) – dropout rate

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters:

input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

call(inputs, training=None)

This is where the layer’s logic lives.

Note here that call() method in tf.keras is little bit different from keras API. In keras API, you can pass support masking for layers as additional arguments. Whereas tf.keras has compute_mask() method to support masking.

Parameters:

• inputs – Input tensor, or list/tuple of input tensors.

• **kwargs – Additional keyword arguments. Currently unused.

Returns:

A tensor or list/tuple of tensors.

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.