tf.keras.layers.SpatialDropout2D

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Spatial 2D version of Dropout.

Inherits From: Dropout, Layer, Operation

tf.keras.layers.SpatialDropout2D(
    rate, data_format=None, seed=None, name=None, dtype=None
)

This version performs the same function as Dropout, however, it drops entire 2D feature maps instead of individual elements. If adjacent pixels 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, SpatialDropout2D will help promote independence between feature maps and should be used instead.

rate Float between 0 and 1. Fraction of the input units to drop. data_format "channels_first" or "channels_last". In "channels_first" mode, the channels dimension (the depth) is at index 1, in "channels_last" mode is it at index 3. It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".

inputs A 4D tensor. training Python boolean indicating whether the layer should behave in training mode (applying dropout) or in inference mode (pass-through).

Output shape: Same as input.

Reference:

• Tompson et al., 2014

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called. output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@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).

config A Python dictionary, typically the output of get_config.

symbolic_call

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symbolic_call(
    *args, **kwargs
)