tf.keras.layers.MaxPool3D
Max pooling operation for 3D data (spatial or spatio-temporal).
tf.keras.layers.MaxPool3D(
pool_size=(2, 2, 2), strides=None, padding='valid', data_format=None, **kwargs
)
Arguments |
pool_size
|
Tuple of 3 integers,
factors by which to downscale (dim1, dim2, dim3).
(2, 2, 2) will halve the size of the 3D input in each dimension.
|
strides
|
tuple of 3 integers, or None. Strides values.
|
padding
|
One of "valid" or "same" (case-insensitive).
|
data_format
|
A string,
one of channels_last (default) or channels_first .
The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape
(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)
while channels_first corresponds to inputs with shape
(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3) .
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".
|
- If
data_format='channels_last'
:
5D tensor with shape:
(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)
- If
data_format='channels_first'
:
5D tensor with shape:
(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)
Output shape:
- If
data_format='channels_last'
:
5D tensor with shape:
(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)
- If
data_format='channels_first'
:
5D tensor with shape:
(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)
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Last updated 2020-10-01 UTC.
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