(var_to_value_dict: dict)
Set the values of given tf.Variables.

    Equivalent to the following, but more efficient and does not bloat the tf graph:
    tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
Set the values of given tf.Variables.
Set the values of given tf . Variables .
def set_vars(var_to_value_dict: dict) -> None:
    """Set the values of given tf.Variables.

    Equivalent to the following, but more efficient and does not bloat the tf graph:
    tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
    """
    assert_tf_initialized()
    ops = []
    feed_dict = {}

    for var, value in var_to_value_dict.items():
        assert is_tf_expression(var)

        try:
            setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0"))  # look for existing op
        except KeyError:
            with absolute_name_scope(var.name.split(":")[0]):
                with tf.control_dependencies(None):  # ignore surrounding control_dependencies
                    setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter")  # create new setter

        ops.append(setter)
        feed_dict[setter.op.inputs[1]] = value

    run(ops, feed_dict)
def set_vars ( var_to_value_dict : dict ) -> None : assert_tf_initialized ( ) ops = [ ] feed_dict = { } for var , value in var_to_value_dict . items ( ) : assert is_tf_expression ( var ) try : setter = tf . get_default_graph ( ) . get_tensor_by_name ( var . name . replace ( ":0" , "/setter:0" ) ) # look for existing op except KeyError : with absolute_name_scope ( var . name . split ( ":" ) [ 0 ] ) : with tf . control_dependencies ( None ) : # ignore surrounding control_dependencies setter = tf . assign ( var , tf . placeholder ( var . dtype , var . shape , "new_value" ) , name = "setter" ) # create new setter ops . append ( setter ) feed_dict [ setter . op . inputs [ 1 ] ] = value run ( ops , feed_dict )