langml.transformer

Submodules

Package Contents

Classes

FeedForward

Feed Forward Layer

Attributes

TF_KERAS

custom_objects

langml.transformer.TF_KERAS[source]
class langml.transformer.FeedForward(units, activation: langml.tensor_typing.Activation = 'relu', kernel_initializer: langml.tensor_typing.Initializer = 'glorot_normal', kernel_regularizer: Optional[langml.tensor_typing.Regularizer] = None, kernel_constraint: Optional[langml.tensor_typing.Constraint] = None, bias_initializer: langml.tensor_typing.Initializer = 'zeros', bias_regularizer: Optional[langml.tensor_typing.Regularizer] = None, bias_constraint: Optional[langml.tensor_typing.Constraint] = None, use_bias: bool = True, dropout_rate: float = 0.0, **kwargs)[source]

Bases: tensorflow.keras.layers.Layer

Feed Forward Layer https://arxiv.org/pdf/1706.03762.pdf

get_config(self) → dict
build(self, input_shape: langml.tensor_typing.Tensors)
call(self, inputs: langml.tensor_typing.Tensors, mask: Optional[langml.tensor_typing.Tensors] = None, training: Optional[Any] = None, **kwargs) → Union[List[langml.tensor_typing.Tensors], langml.tensor_typing.Tensors]
compute_mask(self, inputs: langml.tensor_typing.Tensors, mask: Optional[Union[langml.tensor_typing.Tensors, List[langml.tensor_typing.Tensors]]] = None) → Union[List[Union[langml.tensor_typing.Tensors, None]], langml.tensor_typing.Tensors]
static get_custom_objects() → dict
compute_output_shape(self, input_shape: langml.tensor_typing.Tensors) → langml.tensor_typing.Tensors
langml.transformer.custom_objects[source]