langml.transformer.layers

Yet another transformer implementation.

Module Contents

Classes

FeedForward

Feed Forward Layer
class langml.transformer.layers.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[source]
build(self, input_shape: langml.tensor_typing.Tensors)[source]
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][source]
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][source]
static get_custom_objects() → dict[source]
compute_output_shape(self, input_shape: langml.tensor_typing.Tensors) → langml.tensor_typing.Tensors[source]