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【Ubuntu-Tensorflow】TF1.0到TF1.2出現“Key LSTM/basic_lstm_cell/bias not found in checkpoin”問題

問題詳情：

【Ubuntu-Tensorflow】TF1.0到TF1.2出現“Key LSTM/basic_lstm_cell/bias not found in checkpoin”問題

Caused by op u’save/RestoreV2’, defined at:

File “demo.py”, line 25, in

result_dict = news_demo.newsAggreg({image_path})

File “/home/rszj/liutao/news_aggreg/news_demo.py”, line 32, in newsAggreg

predict = news_predict.run(images_path)

File “/home/rszj/liutao/news_aggreg/news_predict.py”, line 179, in run

saver = tf.train.Saver(restore_dict) # when you want to save model

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py”, line 1139, in init

self.build()

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py”, line 1170, in build

restore_sequentially=self._restore_sequentially)

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py”, line 691, in build

restore_sequentially, reshape)

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py”, line 407, in _AddRestoreOps

tensors = self.restore_op(filename_tensor, saveable, preferred_shard)

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py”, line 247, in restore_op

[spec.tensor.dtype])[0])

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_io_ops.py”, line 640, in restore_v2

dtypes=dtypes, name=name)

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py”, line 767, in apply_op

op_def=op_def)

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py”, line 2506, in create_op

original_op=self._default_original_op, op_def=op_def)

File “/home/rszj/liutao/virtualenv/liutao_py2/mpy2tf1.2/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py”, line 1269, in init

self._traceback = _extract_stack()

NotFoundError (see above for traceback): Key LSTM/basic_lstm_cell/bias not found in checkpoint

[[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT], _device=”/job:localhost/replica:0/task:0/cpu:0”](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

[[Node: save/RestoreV2_26/_101 = _Recvclient_terminated=false, recv_device=”/job:localhost/replica:0/task:0/gpu:0”, send_device=”/job:localhost/replica:0/task:0/cpu:0”, send_device_incarnation=1, tensor_name=”edge_212_save/RestoreV2_26”, tensor_type=DT_FLOAT, _device=”/job:localhost/replica:0/task:0/gpu:0”]]

參考一：參考下面這篇部落格進行解決

tensorflow1.x版本加載saver.restore目錄報錯

在 ubuntu源代碼 news_predict.py中

saver = tf.train.Saver(restore_dict)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
saver.restore(sess, r'model/model.ckpt')

TF1.0正确運作結果如下

【Ubuntu-Tensorflow】TF1.0到TF1.2出現“Key LSTM/basic_lstm_cell/bias not found in checkpoin”問題

ubuntu中修改上述代碼如下：

saver = tf.train.Saver(restore_dict)
init = tf.global_variables_initializer()
savsess = tf.Session()
sess.run(init)
module_file = tf.train.latest_checkpoint('news_tf_model/model.ckpt') #ckpt路徑抽調出來
if module_file is not None:          # 添加一個判斷語句，判斷ckpt的路徑檔案
   saver.restore(sess, module_file)

TF1.0和TF1.2運作結果全部是下面情況

【Ubuntu-Tensorflow】TF1.0到TF1.2出現“Key LSTM/basic_lstm_cell/bias not found in checkpoin”問題

參考一分析：

“if module_file is not None”該判斷僅僅是做了一個檔案是否存在的判斷，并沒有從根本上解決LSTM的報錯問題，而代碼不執行“ saver.restore(sess, module_file)”，就造成最後得到的結果為空了。

問題分析：

其實，仔細檢視提示，會發現，報錯的是指出“Key LSTM/basic_lstm_cell/bias not found in checkpoint”，那必然是LSTM中的bias定義出現了問題。是以筆者列印了saver = tf.train.Saver(restore_dict)中的“restore_dict”，發現TF1.0中和TF1.2中參數存在差異如下表

TF1.0 TF1.2
lstm/basic_lstm_cell/weights lstm/basic_lstm_cell/kernel
lstm/basic_lstm_cell/biases lstm/basic_lstm_cell/bias

原來問題确實出現在了LSTM上了，TF1.0和TF1.2的LSTM竟然在命名上出現了差異，好吧，看來要在TF1.2上使用TF1.0訓練好的ckpt模型，必須要對應LSTM的上面兩個參數了。

要對應參數其實有兩種辦法，第一種，修改ckpt模型中LSTM兩個變量名；第二種，在predict時，做符合TF版本的LSTM變量名的對應。

參考二：

接下來，先介紹第一種方法，根據 基于tensorflow 1.0的圖像叙事功能測試（model/im2txt) 這篇部落格的内容，修改代碼如下

# 由于版本不同，需要進行修改
def RenameCkpt():
# 1.0.1 : 1.2.1
    vars_to_rename = {
    "lstm/basic_lstm_cell/weights": "lstm/basic_lstm_cell/kernel",
    "lstm/basic_lstm_cell/biases": "lstm/basic_lstm_cell/bias",
    }
    new_checkpoint_vars = {}
    reader = tf.train.NewCheckpointReader(FLAGS.checkpoint_path)
    for old_name in reader.get_variable_to_shape_map():
      if old_name in vars_to_rename:
        new_name = vars_to_rename[old_name]
      else:
        new_name = old_name
      new_checkpoint_vars[new_name] = tf.Variable(reader.get_tensor(old_name))

    init = tf.global_variables_initializer()
    saver = tf.train.Saver(new_checkpoint_vars)

    with tf.Session() as sess:
      sess.run(init)
      saver.save(sess, "/home/ndscbigdata/work/change/tf/gan/im2txt/ckpt/newmodel.ckpt-2000000")
    print("checkpoint file rename successful... ")

上述方法是修改ckpt模型中的lstm/basic_lstm_cell/kernel 和 lstm/basic_lstm_cell/bias，修改完成後的ckpt僅僅能夠在1.2.1上正常運作，同樣因為參數名修改了變得版本不對應，而無法在1.0.1上運作。

參考三：

根據以上描述，筆者想到了方法二，按照正常邏輯，修改restore_dict中的lstm/basic_lstm_cell/kernel 和 lstm/basic_lstm_cell/bias

restore_dict = {}
for i in variables[:]:  # the first is global step
    #restore_dict[i.name.replace(':0', '')] = i
    if i.name.replace(':0', '')=='LSTM/basic_lstm_cell/biases':
        print('LSTM/basic_lstm_cell/bias========================================')
        restore_dict[i.name.replace('LSTM/basic_lstm_cell/biases:0',
                                    'LSTM/basic_lstm_cell/bias')] = tf.get_variable('LSTM/basic_lstm_cell/bias',[,])

    elif i.name.replace(':0', '')=='LSTM/basic_lstm_cell/weights':
        print('LSTM/basic_lstm_cell/kernel========================================')
        restore_dict[i.name.replace('LSTM/basic_lstm_cell/weights:0',
                                    'LSTM/basic_lstm_cell/kernel')] = tf.get_variable('LSTM/basic_lstm_cell/kernel',[, ])
    else:
        restore_dict[i.name.replace(':0', '')] = i

原本以為可以相容1.0.1和1.2.1版本了，但是出現一個問題，對同一張圖檔分别在tf1.0.1和tf1.2.1兩個版本下進行多标簽預測，見如下兩圖

【Ubuntu-Tensorflow】TF1.0到TF1.2出現“Key LSTM/basic_lstm_cell/bias not found in checkpoin”問題

圖1—-tf1.2.1環境下運作結果（這是正确的結果）

【Ubuntu-Tensorflow】TF1.0到TF1.2出現“Key LSTM/basic_lstm_cell/bias not found in checkpoin”問題

圖2 —-tf1.0.1環境下運作結果（發現隻能預測第二個标簽，第一個丢失了）

至于為何丢失的問題，我在做測試中，發現，盡管修改了對應于目前tf版本的 “lstm/basic_lstm_cell/weights” 和”lstm/basic_lstm_cell/biases”，但是并沒有起到作用，這個可以通過注釋下面兩行代碼運作程式，發現也是上述結果

【Ubuntu-Tensorflow】TF1.0到TF1.2出現“Key LSTM/basic_lstm_cell/bias not found in checkpoin”問題

總結：

總的來說，1.0.1和1.2.1在使用saver的時候，存在着ckpt模型參數和saver初始化restore_dict中的參數的一一對應的情況，其中以LSTM中的兩個參數：lstm/basic_lstm_cell/weights 和 lstm/basic_lstm_cell/biases 容易出現因為版本的不同，ckpt與預測代碼中自定義的restore_dict中兩個參數不比對的情況，就會報出本錯誤。

附加：

上述問題，筆者有在github-tensorflow官方進行問題提問，成員 skye 給了筆者一個位址作為參考，位址如下：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py

這個位址中給出了checkpoint_convert的詳細代碼，内涵不同版本之間不同命名的轉化問題。

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