Python 还原中的InvalidArgumentError:Assign要求两个张量的形状都匹配

首先，我想提到我是Tensorflow的新手，我正在使用CTC（连接主义时间分类）和LSTM（长-短期记忆）进行OCR项目。我已经完成了培训，当我试图纠正错误时，我发现了一个错误，该错误通常发布在互联网上，但提供了不同的分析

错误是：-

 2018-01-10 13:42:43.179534: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-01-10 13:42:43.179939: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties: 
name: Quadro M4000
major: 5 minor: 2 memoryClockRate (GHz) 0.7725
pciBusID 0000:00:05.0
Total memory: 7.93GiB
Free memory: 7.56GiB
2018-01-10 13:42:43.179974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 
2018-01-10 13:42:43.179986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0:   Y 
2018-01-10 13:42:43.180002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro M4000, pci bus id: 0000:00:05.0)
2018-01-10 13:42:43.316563: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.319682: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.332996: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.333927: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.334583: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.379830: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.380081: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.380189: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.380188: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.380343: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.380554: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
2018-01-10 13:42:43.415117: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]
Traceback (most recent call last):
  File "detect.py", line 62, in <module>
    print(detect(test_inputs, test_targets, test_seq_len))
  File "detect.py", line 23, in detect
    saver.restore(sess,'models/ocr.model-100000')
  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= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]

Caused by op u'save/Assign_1', defined at:
  File "detect.py", line 62, in <module>
    print(detect(test_inputs, test_targets, test_seq_len))
  File "detect.py", line 20, in detect
    saver = tf.train.Saver()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1139, in __init__
    self.build()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1170, in build
    restore_sequentially=self._restore_sequentially)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 691, in build
    restore_sequentially, reshape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 419, in _AddRestoreOps
    assign_ops.append(saveable.restore(tensors, shapes))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 155, in restore
    self.op.get_shape().is_fully_defined())
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/state_ops.py", line 271, in assign
    validate_shape=validate_shape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_state_ops.py", line 45, in assign
    use_locking=use_locking, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [48] rhs shape= [5,5,1,48]
     [[Node: save/Assign_1 = Assign[T=DT_FLOAT, _class=["loc:@Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_1, save/RestoreV2_1/_25)]]

---

我好奇的是这个保护程序是如何获得大小的，以及如何调试代码

看起来您在某个时候更改了图形定义中变量的顺序：

[5,5,1,48]

是保存的检查点中变量1的形状，

[48]

是变量2的形状

命名表示您没有为变量指定明确的名称，因此它们得到了名称

变量

，

变量_1

，

变量_2

。。。后缀是根据tensorflow看到它们的顺序确定的，因此如果在代码中交换两个变量，它们将得到不同的名称。在此之后，您将无法再导入先前保存的检查点，因为tensorflow会在同一名称下看到不同的张量

最佳做法是通过

name

属性显式指定每个变量的名称：

W_conv1 = `tf.Variable(..., name='W_conv1')
b_conv1 = `tf.Variable(..., name='b_conv1')
...

---

通过这种方式，代码对模型中的小扰动将更加健壮。

感谢您的响应，我发现在模型中它自己提到了名称，这是您的意思还是其他什么，我有点困惑

W_conv1 = `tf.Variable(..., name='W_conv1')
b_conv1 = `tf.Variable(..., name='b_conv1')
...