parameters: (tvars, init_checkpoint)
return_statement: return (assignment_map, initialized_variable_names)
docstring: Compute the union of the current variables and checkpoint variables.
docstring_summary: Compute the union of the current variables and checkpoint variables.
docstring_tokens: Compute the union of the current variables and checkpoint variables .
function: def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
    """Compute the union of the current variables and checkpoint variables."""
    assignment_map = {}
    initialized_variable_names = {}
  
    name_to_variable = collections.OrderedDict()
    for var in tvars:
      name = var.name
      m = re.match("^(.*):\\d+$", name)
      if m is not None:
        name = m.group(1)
      name_to_variable[name] = var
  
    init_vars = tf.train.list_variables(init_checkpoint)
  
    assignment_map = collections.OrderedDict()
    for x in init_vars:
      (name, var) = (x[0], x[1])
      if name not in name_to_variable:
        continue
      assignment_map[name] = name
      initialized_variable_names[name] = 1
      initialized_variable_names[name + ":0"] = 1
  
    return (assignment_map, initialized_variable_names)
function_tokens: def get_assignment_map_from_checkpoint ( tvars , init_checkpoint ) : assignment_map = { } initialized_variable_names = { } name_to_variable = collections . OrderedDict ( ) for var in tvars : name = var . name m = re . match ( "^(.*):\\d+$" , name ) if m is not None : name = m . group ( 1 ) name_to_variable [ name ] = var init_vars = tf . train . list_variables ( init_checkpoint ) assignment_map = collections . OrderedDict ( ) for x in init_vars : ( name , var ) = ( x [ 0 ] , x [ 1 ] ) if name not in name_to_variable : continue assignment_map [ name ] = name initialized_variable_names [ name ] = 1 initialized_variable_names [ name + ":0" ] = 1 return ( assignment_map , initialized_variable_names )