Function local_conv1d

tensorflow/python/keras/backend.py:5301–5332

Apply 1D conv with un-shared weights. Arguments: inputs: 3D tensor with shape: (batch_size, steps, input_dim) if data_format is "channels_last" or (batch_size, input_dim, steps) if data_format is "channels_first". kernel: the unshared weight for

(inputs, kernel, kernel_size, strides, data_format=None)

@keras_export('keras.backend.local_conv1d')
def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
  """Apply 1D conv with un-shared weights.

  Arguments:
      inputs: 3D tensor with shape:
          (batch_size, steps, input_dim)
          if data_format is "channels_last" or
          (batch_size, input_dim, steps)
          if data_format is "channels_first".
      kernel: the unshared weight for convolution,
          with shape (output_length, feature_dim, filters).
      kernel_size: a tuple of a single integer,
          specifying the length of the 1D convolution window.
      strides: a tuple of a single integer,
          specifying the stride length of the convolution.
      data_format: the data format, channels_first or channels_last.

  Returns:
      A 3d tensor with shape:
      (batch_size, output_length, filters)
      if data_format='channels_first'
      or 3D tensor with shape:
      (batch_size, filters, output_length)
      if data_format='channels_last'.
  """
  output_shape = (kernel.shape[0],)
  return local_conv(inputs,
                    kernel,
                    kernel_size,
                    strides,
                    output_shape,
                    data_format)


@keras_export('keras.backend.local_conv2d')