View source

Computes sums of N-D convolutions (actually cross-correlation).


  • tf.compat.v2.nn.convolution

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilations parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input免费成长做爱直播有哪些 Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filters Tensor of shape

免费成长做爱直播有哪些 [spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilations tensor of shape [N] (defaulting to [1]N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]N), this computes for each N-D spatial output position (x[0], ..., x[N-1]):

  output[b, x[0], ..., x[N-1], k] =
      sum_{z[0], ..., z[N-1], q}
          filter[z[0], ..., z[N-1], q, k] *
                       x[0]*strides[0] + dilation_rate[0]*z[0],
                       x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1],

where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides免费成长做爱直播有哪些 as described in the .

In the case that data_format does start with "NC", the input and output (but not the filters) are simply transposed as follows:

convolution(input, data_format, kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), 免费成长做爱直播有哪些kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.


  • input: An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
  • filters: An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
  • padding: A string, either "VALID" or "SAME". The padding algorithm.
  • strides: Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
  • dilations: Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
  • name: Optional name for the returned tensor.
  • 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".
  • filters: Alias of filter.
  • dilations: Alias of dilation_rate.


A Tensor with the same type as input of shape

`[batch_size] + output_spatial_shape + [out_channels]`

if data_format is None or does not start with "NC", or

`[batch_size, out_channels] + output_spatial_shape`

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

免费成长做爱直播有哪些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] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).


  • ValueError: If input/output depth does not match filters shape, if padding is other than "VALID" or "SAME", or if data_format is invalid.

results matching ""

    No results matching ""