gianlp.keras_layers.masked_embedding.MaskedEmbedding

class gianlp.keras_layers.masked_embedding.MaskedEmbedding(*args, **kwargs)

Bases: Embedding

Special class for a masked at 0 embedding. It guarantees that the index 0 always maps to a vector of zeros.

Parameters

• input_dim – Size of the vocabulary, i.e. maximum integer index + 1.

• output_dim – Integer. Dimension of the dense embedding.

• **kwargs –

  extra arguments to pass to Embedding init

Methods

add_loss	Add loss tensor(s), potentially dependent on layer inputs.
add_metric	Adds metric tensor to the layer.
add_update	Add update op(s), potentially dependent on layer inputs.
add_variable	Deprecated, do NOT use! Alias for add_weight.
add_weight	Adds a new variable to the layer.
build	Creates the variables of the layer (for subclass implementers).
build_from_config	Builds the layer's states with the supplied config dict.
call	Wraps the original call for guaranteeing masking :param inputs: inputs to forward the layer :return: output of the forward pass
compute_mask	Computes an output mask tensor.
compute_output_shape	Computes the output shape of the layer.
compute_output_signature	Compute the output tensor signature of the layer based on the inputs.
count_params	Count the total number of scalars composing the weights.
finalize_state	Finalizes the layers state after updating layer weights.
from_config	Creates a layer from its config.
get_build_config	Returns a dictionary with the layer's input shape.
get_config	Returns the config of the layer.
get_input_at	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at	Retrieves the input shape(s) of a layer at a given node.
get_output_at	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at	Retrieves the output shape(s) of a layer at a given node.
get_weights	Returns the current weights of the layer, as NumPy arrays.
load_own_variables	Loads the state of the layer.
save_own_variables	Saves the state of the layer.
set_weights	Sets the weights of the layer, from NumPy arrays.
with_name_scope	Decorator to automatically enter the module name scope.

Attributes

activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer's computations.
dtype	The dtype of the layer weights.
dtype_policy	The dtype policy associated with this layer.
dynamic	Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes	Return Functional API nodes upstream of this layer.
input	Retrieves the input tensor(s) of a layer.
input_mask	Retrieves the input mask tensor(s) of a layer.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	InputSpec instance(s) describing the input format for this layer.
losses	List of losses added using the add_loss() API.
metrics	List of metrics added using the add_metric() API.
name	Name of the layer (string), set in the constructor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables	Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights	List of all non-trainable weights tracked by this layer.
outbound_nodes	Return Functional API nodes downstream of this layer.
output	Retrieves the output tensor(s) of a layer.
output_mask	Retrieves the output mask tensor(s) of a layer.
output_shape	Retrieves the output shape(s) of a layer.
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable
trainable_variables	Sequence of trainable variables owned by this module and its submodules.
trainable_weights	List of all trainable weights tracked by this layer.
updates
variable_dtype	Alias of Layer.dtype, the dtype of the weights.
variables	Returns the list of all layer variables/weights.
weights	Returns the list of all layer variables/weights.

call(inputs)

Wraps the original call for guaranteeing masking :param inputs: inputs to forward the layer :return: output of the forward pass