FNetEncoder
classkeras_nlp.layers.FNetEncoder(
intermediate_dim,
dropout=0,
activation="relu",
layer_norm_epsilon=1e-05,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
**kwargs
)
FNet encoder.
This class follows the architecture of FNet encoder layer in the FNet paper. Users can instantiate multiple instances of this class to stack up the encoder.
Note on masking: In the official FNet code, padding tokens are added to the the input. However, the padding masks are deleted, i.e., mixing of all tokens is done. This is because certain frequencies will be zeroed out if we apply padding masks in every encoder layer. Hence, we don't take padding mask as input in the call() function.
Arguments
0.
.keras.activations
. The
activation function of feedforward network.
Defaults to "relu"
.1e-5
.str
or keras.initializers
initializer.
The kernel initializer for the dense layers.
Defaults to "glorot_uniform"
.keras.initializers
initializer.
The bias initializer for the dense layers.
Defaults to "zeros"
.keras.layers.Layer
,
including name
, trainable
, dtype
etc.Example
# Create a single FNet encoder layer.
encoder = keras_hub.layers.FNetEncoder(
intermediate_dim=64)
# Create a simple model containing the encoder.
input = keras.Input(shape=(10, 64))
output = encoder(input)
model = keras.Model(inputs=input, outputs=output)
# Call encoder on the inputs.
input_data = np.random.uniform(size=(1, 10, 64))
output = model(input_data)
References