恢復eval，無效形狀問題的訓練模型時出現Tensorflow錯誤？

Question

嘗試恢復訓練有素的模型進行評估時，出現錯誤，但僅在對測試集進行評估時出現錯誤。錯誤是：

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

注意LHS形狀= [2325,11]和rhs形狀= [4891,11]對應於在測試組2325倍的圖像和在訓練組4891倍的圖像;而11是11個類的熱門編碼 - 所以這些可能與標籤相對應。當我對訓練集進行評估時，尺寸匹配並且沒有錯誤結果。幫助將不勝感激！

完整堆棧跟蹤如下：

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

我只是看着到張量從檢查點文件中的形狀，它看起來像保護程序節約了輸入到模型中。我需要重新配置我的訓練代碼或以其他方式找出如何從檢查點中排除的模型輸入（標籤和圖像）：

('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

這將是看到代碼定義你的模型有用的;但從我能理解的看起來，您可能已經將輸入定義爲tf.Variable。變量是允許優化器更改以最大限度地減少損失函數的值。變量是模型的學習權重，這就是爲什麼Tensorflow正在保存它們，以便以後可以恢復它們。

您應該使用tf.Placeholder將輸入數據提供給圖表。