tf.keras.layers.SpatialDropout3D

Spatial 3D version of Dropout.

Inherits From: Dropout, Layer, Operation

tf.keras.layers.SpatialDropout3D(
    rate, data_format=None, seed=None, name=None, dtype=None
)

This version performs the same function as Dropout, however, it drops entire 3D feature maps instead of individual elements. If adjacent voxels 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, SpatialDropout3D 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.
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 4. 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".

Call arguments
inputs	A 5D tensor.
training	Python boolean indicating whether the layer should behave in training mode (applying dropout) or in inference mode (pass-through).

Input shape
5D tensor with shape: (samples, channels, dim1, dim2, dim3) if data_format='channels_first' or 5D tensor with shape: (samples, dim1, dim2, dim3, channels) if data_format='channels_last'.

Output shape: Same as input.

Reference:

• Tompson et al., 2014

Attributes
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

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.