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). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. |
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". |
Input shape:
- 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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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/layers/MaxPool3D