在tensorflow中重置图

巨大的 但形状不同 .现在我试图在同一个过程中运行评估和训练,我偶然发现,我无法真正删除tensorflow图中定义的变量。例如,建议的解决方法 here reset_default_graph() 但这似乎与图形上下文管理器无关。

import numpy as np
import tensorflow as tf

GRAPH = tf.Graph()

def train(examples):
    with GRAPH.as_default() as g:
        # actually this is huge variable
        global_var = tf.get_variable('global_var',
                                     initializer=np.full((examples, 32), 0.0),
                                     trainable=False)

def evaluate(examples):
    # tf.reset_default_graph() # ValueError: Variable input_var already exists
    with GRAPH.as_default() as g: # initialized to some other size
        tf.reset_default_graph() 
        global_var = tf.get_variable('global_var',
                                     initializer=np.full((examples, 32), 0.0),
                                     trainable=False)
       # in fact tensorflow creates a new graph and does not use GRAPH to define global_var

train(32)
evaluate(8)

结果:

Traceback (most recent call last):
  File "C:/Users/MrD/.PyCharm2017.1/config/scratches/scratch_44.py", line 22, in <module>
    evaluate(8)
  File "C:/Users/MrD/.PyCharm2017.1/config/scratches/scratch_44.py", line 19, in evaluate
    trainable=False)
  File "C:\_\Python35\lib\contextlib.py", line 66, in __exit__
    next(self.gen)
  File "C:\_\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 3616, in get_controller
    if self.stack[-1] is not default:
IndexError: list index out of range

那么,使用reset\u default\u graph()的正确方法是什么?真的没有办法抛弃旧的潜在巨大初始值设定项来重新定义变量吗?