tfm.nlp.layers.BlockDiagFeedforward

Block diagonal feedforward layer.

tfm.nlp.layers.BlockDiagFeedforward(
    intermediate_size: int,
    intermediate_activation: str,
    dropout: float,
    num_blocks: int = 1,
    apply_mixing: bool = True,
    kernel_initializer: str = 'glorot_uniform',
    bias_initializer: str = 'zeros',
    kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    activity_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    kernel_constraint: Optional[tf.keras.constraints.Constraint] = None,
    bias_constraint: Optional[tf.keras.constraints.Constraint] = None,
    **kwargs
)

This layer replaces the weight matrix of the output_dense layer with a block diagonal matrix to save layer parameters and FLOPs. A linear mixing layer can be added optionally to improve layer expressibility.

intermediate_size Size of the intermediate layer.
intermediate_activation Activation for the intermediate layer.
dropout Dropout probability for the output dropout.
num_blocks The number of blocks for the block diagonal matrix of the output_dense layer.
apply_mixing Apply linear mixing if True.
kernel_initializer Initializer for dense layer kernels.
bias_initializer Initializer for dense layer biases.
kernel_regularizer Regularizer for dense layer kernels.
bias_regularizer Regularizer for dense layer biases.
activity_regularizer Regularizer for dense layer activity.
kernel_constraint Constraint for dense layer kernels.
bias_constraint Constraint for dense layer kernels.

Methods

call

View source

call(
    inputs
)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances, in __init__(), or in the build() method that is called automatically before call() executes for the first time.

Args
inputs Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules:

  • inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument.
  • NumPy array or Python scalar values in inputs get cast as tensors.
  • Keras mask metadata is only collected from inputs.
  • Layers are built (build(input_shape) method) using shape info from inputs only.
  • input_spec compatibility is only checked against inputs.
  • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
  • The SavedModel input specification is generated using inputs only.
  • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved:
  • training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
  • mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

    • Returns
    A tensor or list/tuple of tensors.