恢复eval，无效形状问题的训练模型时出现Tensorflow错误？

Question

尝试恢复训练有素的模型进行评估时，出现错误，但仅在对测试集进行评估时出现错误。错误是：

InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [2325,11] rhs shape= [4891,11] 

注意LHS形状= [2325,11]和rhs形状= [4891,11]对应于在测试组2325倍的图像和在训练组4891倍的图像;而11是11个类的热门编码 - 所以这些可能与标签相对应。当我对训练集进行评估时，尺寸匹配并且没有错误结果。帮助将不胜感激！

完整堆栈跟踪如下：

Traceback (most recent call last): 
    File "eval.py", line 75, in <module> 
    main() 
    File "eval.py", line 70, in main 
    acc_annotation, acc_retrieval = evaluate(partition="test") 
    File "eval.py", line 34, in evaluate 
    restorer.restore(sess, tf.train.latest_checkpoint(SAVED_MODEL_DIR)) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1388, in restore 
    {self.saver_def.filename_tensor_name: save_path}) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 766, in run 
    run_metadata_ptr) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 964, in _run 
    feed_dict_string, options, run_metadata) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1014, in _do_run 
    target_list, options, run_metadata) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1034, 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= [2325,11] rhs shape= [4891,11] 
    [[Node: save/Assign_5 = Assign[T=DT_FLOAT, _class=["loc:@input/Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/cpu:0"](input/Variable_1, save/RestoreV2_5)]] 

Caused by op u'save/Assign_5', defined at: 
    File "eval.py", line 75, in <module> 
    main() 
    File "eval.py", line 70, in main 
    acc_annotation, acc_retrieval = evaluate(partition="test") 
    File "eval.py", line 25, in evaluate 
    restorer = tf.train.Saver() # For saving the model 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1000, in __init__ 
    self.build() 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1030, in build 
    restore_sequentially=self._restore_sequentially) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 624, in build 
    restore_sequentially, reshape) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 373, in _AddRestoreOps 
    assign_ops.append(saveable.restore(tensors, shapes)) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 130, in restore 
    self.op.get_shape().is_fully_defined()) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_state_ops.py", line 47, in assign 
    use_locking=use_locking, name=name) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op 
    op_def=op_def) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2240, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
    File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__ 
    self._traceback = _extract_stack() 

InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [2325,11] rhs shape= [4891,11] 
    [[Node: save/Assign_5 = Assign[T=DT_FLOAT, _class=["loc:@input/Variable_1"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/cpu:0"](input/Variable_1, save/RestoreV2_5)]] 

更新

我只是看着到张量从检查点文件中的形状，它看起来像保护程序节约了输入到模型中。我需要重新配置我的训练代码或以其他方式找出如何从检查点中排除的模型输入（标签和图像）：

('tensor_name: ', 'conv2-layer/bias/Adam_1') 
(512,) 
('tensor_name: ', 'input/Variable_1') 
(4891, 11) 
('tensor_name: ', 'conv2-layer/weights_1/Adam') 
(5, 1, 64, 512) 
('tensor_name: ', 'conv1-layer/weights_1') 
(5, 23, 1, 64) 
('tensor_name: ', 'conv2-layer/weights_1') 
(5, 1, 64, 512) 
('tensor_name: ', 'conv2-layer/weights_1/Adam_1') 
(5, 1, 64, 512) 
('tensor_name: ', 'input/Variable') 
(4891, 100, 23, 1) 
('tensor_name: ', 'conv1-layer/weights_1/Adam_1') 
(5, 23, 1, 64) 
('tensor_name: ', 'conv1-layer/bias/Adam') 
(64,) 
('tensor_name: ', 'beta2_power') 
() 
('tensor_name: ', 'conv2-layer/bias/Adam') 
(512,) 
('tensor_name: ', 'conv1-layer/bias/Adam_1') 
(64,) 
('tensor_name: ', 'conv2-layer/bias') 
(512,) 
('tensor_name: ', 'conv1-layer/bias') 
(64,) 
('tensor_name: ', 'beta1_power') 
() 
('tensor_name: ', 'conv1-layer/weights_1/Adam') 
(5, 23, 1, 64) 
('tensor_name: ', 'Variable') 
() 

Answer 1

这将是看到代码定义你的模型有用的;但从我能理解的看起来，您可能已经将输入定义为tf.Variable。变量是允许优化器更改以最大限度地减少损失函数的值。变量是模型的学习权重，这就是为什么Tensorflow正在保存它们，以便以后可以恢复它们。

您应该使用tf.Placeholder将输入数据提供给图表。