tf.nn.pool( input, window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None, data_format=None )
Defined in tensorflow/python/ops/nn_ops.py
.
See the guide: Neural Network > Pooling
Performs an N-D pooling operation.
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 comment here. 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))
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 comment here
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".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 output_spatial_shape
depends on the value of padding:
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]).
ValueError
: if arguments are invalid.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/pool