tfm.nlp.layers.SpectralNormalizationConv2D

Implements spectral normalization for Conv2D layer based on [3].

tfm.nlp.layers.SpectralNormalizationConv2D(
    layer,
    iteration=1,
    norm_multiplier=0.95,
    training=True,
    aggregation=tf.VariableAggregation.MEAN,
    legacy_mode=False,
    **kwargs
)

layer (tf.keras.layers.Layer) A TF Keras layer to apply normalization to.
iteration (int) The number of power iteration to perform to estimate weight matrix's singular value.
norm_multiplier (float) Multiplicative constant to threshold the normalization. Usually under normalization, the singular value will converge to this value.
training (bool) Whether to perform power iteration to update the singular value estimate.
aggregation (tf.VariableAggregation) Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation.
legacy_mode (bool) Whether to use the legacy implementation where the dimension of the u and v vectors are set to the batch size. It should not be enabled unless for backward compatibility reasons.
**kwargs (dict) Other keyword arguments for the layers.Wrapper class.

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.

    restore_weights

    View source

    restore_weights()

    Restores layer weights to maintain gradient update (See Alg 1 of [1]).

    update_weights

    View source

    update_weights()

    Computes power iteration for convolutional filters based on [3].