Question

我已分别训练了两个模型，我想加载它们的变量并对它们求平均值。但是tf.get_default graph（）

出错了

这是我的代码结构（我知道这是错误的，但如何正确编写？）

sess = tf.session()
saver_one = tf.train.import_meta_graph('./model1.ckpt.meta')
saver_one.restore(sess,'./model1.ckpt')
graph_one = tf.get_default_graph()
wc1 = graph_one.get_tensor_by_name('wc1:0')
……
saver_two = tf.train.import_meta_graph('./model2.ckpt.meta')
saver_two.restore(sess,'./model2.ckpt')
graph_two = tf.get_default_graph()
wc1_two = graph_two.get_tensor_by_name('wc1:0')
……

错误来自：

Traceback (most recent call last):
  File "/home/dan/Documents/deep-prior-master/src/ESB_ICVL_TEST_ALL.py", line 143, in <module>
    saver_two.restore(sess,'./cache/cnn_shallow/model2.ckpt')
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1548, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 789, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 997, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [27] rhs shape= [9]
     [[Node: save/Assign_6 = Assign[T=DT_FLOAT, _class=["loc:@outb"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](outb, save/RestoreV2_6/_1)]]

非常感谢您给我任何建议。 =（^。^）=

Answer 1

您正在尝试覆盖图形并且它会遇到不匹配（某些尺寸不匹配）。将它们分开可能更好。

graph_one = tf.Graph()
with graph_one.as_default():
  session_one = tf.Session()
  with session_one.as_default():
    saver_one = tf.train.import_meta_graph('./model1.ckpt.meta')
    wc1_one_value = session_one.run([graph_one.get_tensor_by_name('wc1:0')])

# Similar for graph_two
...

print (wc1_one_value + wc1_two_value) / 2  # Or whatever you want

要将它们分配回会话，构建图形，然后执行tf.assign operations。

with graph_one.as_default(), session_one.as_default():
   session_one.run([tf.assign(<variable>, (wc1_one_value + wc1_two_value) / 2 )])

要获取变量，您可以使用get_trainable_variables或使用reuse=True再次定义变量。然后再次导出模型。

如何恢复多个模型并在张量流中平均它们？

1 个答案: