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Spatial 1D version of Dropout.
Inherits From: Dropout
, Layer
, Operation
tf.keras.layers.SpatialDropout1D(
rate, seed=None, name=None, dtype=None
)
This layer performs the same function as Dropout, however, it drops
entire 1D feature maps instead of individual elements. If adjacent frames
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, SpatialDropout1D
will help promote independence
between feature maps and should be used instead.
Args | |
---|---|
rate
|
Float between 0 and 1. Fraction of the input units to drop. |
Call arguments | |
---|---|
inputs
|
A 3D tensor. |
training
|
Python boolean indicating whether the layer should behave in training mode (applying dropout) or in inference mode (pass-through). |
Input shape | |
---|---|
3D tensor with shape: (samples, timesteps, channels)
|
Output shape: Same as input.
Reference:
Methods
from_config
@classmethod
from_config( config )
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
symbolic_call
symbolic_call(
*args, **kwargs
)