tf.keras.layers.AveragePooling3D
    
    
      
    
    
      
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Average pooling operation for 3D data (spatial or spatio-temporal).
tf.keras.layers.AveragePooling3D(
    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) orchannels_first.
The ordering of the dimensions in the inputs.channels_lastcorresponds to inputs with shape(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)whilechannels_firstcorresponds to inputs with shape(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3).
It defaults to theimage_data_formatvalue 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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