tf.nn.pool(input, window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None, data_format=None)

tf.nn.pool(input, window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None, data_format=None)

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))

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 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 value is "NDHWC".

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 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]).

Raises:

  • ValueError: if arguments are invalid.

Defined in tensorflow/python/ops/nn_ops.py.