Function conv1d

tensorflow/python/keras/backend.py:4672–4718

1D convolution. Arguments: x: Tensor or variable. kernel: kernel tensor. strides: stride integer. padding: string, `"same"`, `"causal"` or `"valid"`. data_format: string, one of "channels_last", "channels_first". dilation_rate: integer dilate rate. Returns:

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

@keras_export('keras.backend.conv1d')
def conv1d(x,
           kernel,
           strides=1,
           padding='valid',
           data_format=None,
           dilation_rate=1):
  """1D convolution.

  Arguments:
      x: Tensor or variable.
      kernel: kernel tensor.
      strides: stride integer.
      padding: string, `"same"`, `"causal"` or `"valid"`.
      data_format: string, one of "channels_last", "channels_first".
      dilation_rate: integer dilate rate.

  Returns:
      A tensor, result of 1D 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))

  kernel_shape = kernel.shape.as_list()
  if padding == 'causal':
    # causal (dilated) convolution:
    left_pad = dilation_rate * (kernel_shape[0] - 1)
    x = temporal_padding(x, (left_pad, 0))
    padding = 'valid'
  padding = _preprocess_padding(padding)

  x, tf_data_format = _preprocess_conv1d_input(x, data_format)
  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 == 'NWC':
    x = array_ops.transpose(x, (0, 2, 1))  # NWC -> NCW
  return x


@keras_export('keras.backend.conv2d')