Question

当尝试在训练有素的多个时间段上加载保存的权重时使用以下代码返回网络：

import tensorflow as tf
from returnn.Config import Config
from returnn.TFNetwork import TFNetwork

for i in range(1,11):
    modelFilePath = path/to/model/ + 'network.' + '%03d' % (i,)

    returnnConfig = Config()
    returnnConfig.load_file(path/to/configFile)
    returnnTfNetwork = TFNetwork(config=path/to/configFile, train_flag=False, eval_flag=True)

    returnnTfNetwork.construct_from_dict(returnnConfig.typed_value('network'))

    with tf.Session() as sess:
        returnnTfNetwork.load_params_from_file(modelFilePath, sess)

我收到以下错误：

Variables to restore which are not in checkpoint:
global_step_1

Variables in checkpoint which are not needed for restore:
global_step

Probably we can restore these:
(None)

Error, some entry is missing in the checkpoint

Answer 1

问题是您每次在循环中都重新创建TFNetwork，并且在那里每次全局步骤也都会创建一个新变量，由于每个变量必须具有唯一的名称，因此必须将其称为不同。

您可以在循环中执行以下操作：

tf.reset_default_graph()

如何使用TensorFlow在Returnn中加载经过训练的网络的权重

1 个答案: