![]() |
Transformer layer with ReZero.
tfm.nlp.layers.ReZeroTransformer(
num_attention_heads,
intermediate_size,
intermediate_activation,
dropout_rate=0.0,
attention_dropout_rate=0.0,
output_range=None,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
use_layer_norm=False,
share_rezero=True,
**kwargs
)
This layer implements the Transformer from "Attention Is All You Need". (https://arxiv.org/abs/1706.03762). The residual connection implements the ReZero method. (https://arxiv.org/abs/2003.04887)
Args | |
---|---|
num_attention_heads
|
Number of attention heads. |
intermediate_size
|
Size of the intermediate layer. |
intermediate_activation
|
Activation for the intermediate layer. |
dropout_rate
|
Dropout probability for the post-attention and output dropout. |
attention_dropout_rate
|
Dropout probability for within the attention layer. |
output_range
|
the sequence output range, [0, output_range) by slicing the
target sequence. None means the target sequence is not sliced.
|
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. |
use_layer_norm
|
If add layer_norm on top of the ReZero. |
share_rezero
|
If attention layer and FFN layer share the same alpha. |
Methods
call
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 in __init__()
, or the build()
method
that is called automatically before call()
executes the first time.
Args | |
---|---|
inputs
|
Input tensor, or dict/list/tuple of input tensors.
The first positional inputs argument is subject to special rules:
|
*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. |
reset_rezero
reset_rezero()