Function conv3d

tensorflow/python/keras/backend.py:5010–5049

3D convolution. Arguments: x: Tensor or variable. kernel: kernel tensor. strides: strides tuple. padding: string, `"same"` or `"valid"`. data_format: string, `"channels_last"` or `"channels_first"`. dilation_rate: tuple of 3 integers. Returns: A tensor

(x,
           kernel,
           strides=(1, 1, 1),
           padding='valid',
           data_format=None,
           dilation_rate=(1, 1, 1))

@keras_export('keras.backend.conv3d')
def conv3d(x,
           kernel,
           strides=(1, 1, 1),
           padding='valid',
           data_format=None,
           dilation_rate=(1, 1, 1)):
  """3D convolution.

  Arguments:
      x: Tensor or variable.
      kernel: kernel tensor.
      strides: strides tuple.
      padding: string, `"same"` or `"valid"`.
      data_format: string, `"channels_last"` or `"channels_first"`.
      dilation_rate: tuple of 3 integers.

  Returns:
      A tensor, result of 3D convolution.

  Raises:
      ValueError: if `data_format` is neither `channels_last` or
      `channels_first`.
  """
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format: ' + str(data_format))

  x, tf_data_format = _preprocess_conv3d_input(x, data_format)
  padding = _preprocess_padding(padding)
  x = nn.convolution(
      input=x,
      filter=kernel,
      dilation_rate=dilation_rate,
      strides=strides,
      padding=padding,
      data_format=tf_data_format)
  if data_format == 'channels_first' and tf_data_format == 'NDHWC':
    x = array_ops.transpose(x, (0, 4, 1, 2, 3))
  return x


def conv3d_transpose(x,