(self, filters, kernel_size,
               kernel_initializer=init.GlorotUniformInitializer(),
               bias_initializer=init.RandomNormalInitializer(1e-6),
               use_bias=True, padding='VALID')
Returns a locally-connected conv-like layer.

    Args:
      filters: Number of output filters in the convolution.
      kernel_size: A length of the convolution window. Must be an odd number.
      kernel_initializer: Function that creates a matrix of (random) initial
          connection weights `W` for the layer.
      bias_initializer: Function that creates a vector of (random) initial
          bias weights `b` for the layer.
      use_bias: If `True`, the layer uses a bias vector.
      padding: The type of padding to use; must be 'VALID', 'SAME', or 'WRAP'.
Returns a locally-connected conv-like layer.
Returns a locally - connected conv - like layer .
def __init__(self, filters, kernel_size,
               kernel_initializer=init.GlorotUniformInitializer(),
               bias_initializer=init.RandomNormalInitializer(1e-6),
               use_bias=True, padding='VALID'):
    """Returns a locally-connected conv-like layer.

    Args:
      filters: Number of output filters in the convolution.
      kernel_size: A length of the convolution window. Must be an odd number.
      kernel_initializer: Function that creates a matrix of (random) initial
          connection weights `W` for the layer.
      bias_initializer: Function that creates a vector of (random) initial
          bias weights `b` for the layer.
      use_bias: If `True`, the layer uses a bias vector.
      padding: The type of padding to use; must be 'VALID', 'SAME', or 'WRAP'.
    """
    super().__init__(name=f'LocallyConnected1d_{filters}_{kernel_size}')
    self._filters = filters
    self._kernel_size = kernel_size
    assert self._kernel_size % 2 == 1  # kernel size has to be odd
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._use_bias = use_bias
    self._padding = padding
def __init__ ( self , filters , kernel_size , kernel_initializer = init . GlorotUniformInitializer ( ) , bias_initializer = init . RandomNormalInitializer ( 1e-6 ) , use_bias = True , padding = 'VALID' ) : super ( ) . __init__ ( name = f'LocallyConnected1d_{filters}_{kernel_size}' ) self . _filters = filters self . _kernel_size = kernel_size assert self . _kernel_size % 2 == 1 # kernel size has to be odd self . _kernel_initializer = kernel_initializer self . _bias_initializer = bias_initializer self . _use_bias = use_bias self . _padding = padding