tf.compat.v1.nn.pool

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 `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]).

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.

tf.compat.v1.nn.pool

Args

Returns

Raises