tf.keras.ops.max_pool
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Max pooling operation.
tf.keras.ops.max_pool(
inputs,
pool_size,
strides=None,
padding='valid',
data_format=None
)
Args |
inputs
|
Tensor of rank N+2. inputs has shape
(batch_size,) + inputs_spatial_shape + (num_channels,) if
data_format="channels_last" , or
(batch_size, num_channels) + inputs_spatial_shape if
data_format="channels_first" . Pooling happens over the spatial
dimensions only.
|
pool_size
|
int or tuple/list of integers of size
len(inputs_spatial_shape) , specifying the size of the pooling
window for each spatial dimension of the input tensor. If
pool_size is int, then every spatial dimension shares the same
pool_size .
|
strides
|
int or tuple/list of integers of size
len(inputs_spatial_shape) . The stride of the sliding window for
each spatial dimension of the input tensor. If strides is int,
then every spatial dimension shares the same strides .
|
padding
|
string, either "valid" or "same" . "valid" means no
padding is applied, and "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 when strides=1 .
|
data_format
|
A string, either "channels_last" or "channels_first" .
data_format determines the ordering of the dimensions in the
inputs. If data_format="channels_last" , inputs is of shape
(batch_size, ..., channels) while if
data_format="channels_first" , inputs is of shape
(batch_size, channels, ...) .
|
Returns |
A tensor of rank N+2, the result of the max pooling operation.
|
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Last updated 2024-06-07 UTC.
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