(args,
            output_size,
            bias,
            bias_initializer=None,
            kernel_initializer=None)
Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
  Args:
    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
    output_size: int, second dimension of W[i].
    bias: boolean, whether to add a bias term or not.
    bias_initializer: starting value to initialize the bias
      (default is all zeros).
    kernel_initializer: starting value to initialize the weight.
  Returns:
    A 2D Tensor with shape [batch x output_size] equal to
    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
  Raises:
    ValueError: if some of the arguments has unspecified or wrong shape.
Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
  Args:
    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
    output_size: int, second dimension of W[i].
    bias: boolean, whether to add a bias term or not.
    bias_initializer: starting value to initialize the bias
      (default is all zeros).
    kernel_initializer: starting value to initialize the weight.
  Returns:
    A 2D Tensor with shape [batch x output_size] equal to
    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
  Raises:
    ValueError: if some of the arguments has unspecified or wrong shape.
Linear map : sum_i ( args [ i ] * W [ i ] ) where W [ i ] is a variable . Args : args : a 2D Tensor or a list of 2D batch x n Tensors . output_size : int second dimension of W [ i ] . bias : boolean whether to add a bias term or not . bias_initializer : starting value to initialize the bias ( default is all zeros ) . kernel_initializer : starting value to initialize the weight . Returns : A 2D Tensor with shape [ batch x output_size ] equal to sum_i ( args [ i ] * W [ i ] ) where W [ i ] s are newly created matrices . Raises : ValueError : if some of the arguments has unspecified or wrong shape .
def linear(args,
            output_size,
            bias,
            bias_initializer=None,
            kernel_initializer=None):
  """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
  Args:
    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
    output_size: int, second dimension of W[i].
    bias: boolean, whether to add a bias term or not.
    bias_initializer: starting value to initialize the bias
      (default is all zeros).
    kernel_initializer: starting value to initialize the weight.
  Returns:
    A 2D Tensor with shape [batch x output_size] equal to
    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
  Raises:
    ValueError: if some of the arguments has unspecified or wrong shape.
  """
  if args is None or (nest.is_sequence(args) and not args):
    raise ValueError("`args` must be specified")
  if not nest.is_sequence(args):
    args = [args]

  # Calculate the total size of arguments on dimension 1.
  total_arg_size = 0
  shapes = [a.get_shape() for a in args]
  for shape in shapes:
    if shape.ndims != 2:
      raise ValueError("linear is expecting 2D arguments: %s" % shapes)
    if shape[1].value is None:
      raise ValueError("linear expects shape[1] to be provided for shape %s, "
                       "but saw %s" % (shape, shape[1]))
    else:
      total_arg_size += shape[1].value

  dtype = [a.dtype for a in args][0]

  # Now the computation.
  scope = vs.get_variable_scope()
  with vs.variable_scope(scope) as outer_scope:
    weights = vs.get_variable(
        _WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
        dtype=dtype,
        initializer=kernel_initializer)
    if len(args) == 1:
      res = math_ops.matmul(args[0], weights)
    else:
      res = math_ops.matmul(array_ops.concat(args, 1), weights)
    if not bias:
      return res
    with vs.variable_scope(outer_scope) as inner_scope:
      inner_scope.set_partitioner(None)
      if bias_initializer is None:
        bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
      biases = vs.get_variable(
          _BIAS_VARIABLE_NAME, [output_size],
          dtype=dtype,
          initializer=bias_initializer)
    return nn_ops.bias_add(res, biases)
def linear ( args , output_size , bias , bias_initializer = None , kernel_initializer = None ) : if args is None or ( nest . is_sequence ( args ) and not args ) : raise ValueError ( "`args` must be specified" ) if not nest . is_sequence ( args ) : args = [ args ] # Calculate the total size of arguments on dimension 1. total_arg_size = 0 shapes = [ a . get_shape ( ) for a in args ] for shape in shapes : if shape . ndims != 2 : raise ValueError ( "linear is expecting 2D arguments: %s" % shapes ) if shape [ 1 ] . value is None : raise ValueError ( "linear expects shape[1] to be provided for shape %s, " "but saw %s" % ( shape , shape [ 1 ] ) ) else : total_arg_size += shape [ 1 ] . value dtype = [ a . dtype for a in args ] [ 0 ] # Now the computation. scope = vs . get_variable_scope ( ) with vs . variable_scope ( scope ) as outer_scope : weights = vs . get_variable ( _WEIGHTS_VARIABLE_NAME , [ total_arg_size , output_size ] , dtype = dtype , initializer = kernel_initializer ) if len ( args ) == 1 : res = math_ops . matmul ( args [ 0 ] , weights ) else : res = math_ops . matmul ( array_ops . concat ( args , 1 ) , weights ) if not bias : return res with vs . variable_scope ( outer_scope ) as inner_scope : inner_scope . set_partitioner ( None ) if bias_initializer is None : bias_initializer = init_ops . constant_initializer ( 0.0 , dtype = dtype ) biases = vs . get_variable ( _BIAS_VARIABLE_NAME , [ output_size ] , dtype = dtype , initializer = bias_initializer ) return nn_ops . bias_add ( res , biases )