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Performs an N-D pooling operation.
tf.compat.v1.nn.pool(
input,
window_shape,
pooling_type,
padding,
dilation_rate=None,
strides=None,
name=None,
data_format=None,
dilations=None
)
In the case that data_format
does not start with "NC", computes for
0 <= b < batch_size,
0 <= x[i] < output_spatial_shape[i],
0 <= c < num_channels:
output[b, x[0], ..., x[N-1], c] =
REDUCE_{z[0], ..., z[N-1]}
input[b,
x[0] * strides[0] - pad_before[0] + dilation_rate[0]*z[0],
...
x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1],
c],
where the reduction function REDUCE depends on the value of pooling_type
,
and pad_before is defined based on the value of padding
as described in
the "returns" section of tf.nn.convolution
for details.
The reduction never includes out-of-bounds positions.
In the case that data_format
starts with "NC"
, the input
and output are
simply transposed as follows:
pool(input, data_format, **kwargs) =
tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]),
**kwargs),
[0, N+1] + range(1, N+1))
Args | |
---|---|
input
|
Tensor of rank N+2, of shape
[batch_size] + input_spatial_shape + [num_channels] if data_format does
not start with "NC" (default), or
[batch_size, num_channels] + input_spatial_shape if data_format starts
with "NC". Pooling happens over the spatial dimensions only.
|
window_shape
|
Sequence of N ints >= 1. |
pooling_type
|
Specifies pooling operation, must be "AVG" or "MAX". |
padding
|
The padding algorithm, must be "SAME" or "VALID".
See the "returns" section of tf.nn.convolution for details.
|
dilation_rate
|
Optional. Dilation rate. List of N ints >= 1.
Defaults to [1]*N . If any value of dilation_rate is > 1, then all
values of strides must be 1.
|
strides
|
Optional. Sequence of N ints >= 1. Defaults to [1]*N .
If any value of strides is > 1, then all values of dilation_rate must be
1.
|
name
|
Optional. Name of the op. |
data_format
|
A string or None. Specifies whether the channel dimension of
the input and output is the last dimension (default, or if data_format
does not start with "NC"), or the second dimension (if data_format
starts with "NC"). For N=1, the valid values are "NWC" (default) and
"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW".
For N=3, the valid values are "NDHWC" (default) and "NCDHW".
|
dilations
|
Alias for dilation_rate |
Returns | |
---|---|
Tensor of rank N+2, of shape
[batch_size] + output_spatial_shape + [num_channels]
if data_format is None or does not start with "NC", or [batch_size, num_channels] + output_spatial_shape if data_format starts with "NC",
where If padding = "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i]) If padding = "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (window_shape[i] - 1) * dilation_rate[i]) / strides[i]). |
Raises | |
---|---|
ValueError
|
if arguments are invalid. |