TensorFlow translate.pyチュートリアル

Question

コードブロックの最後にある値のエラーを参照してください。このエラーは翻訳チュートリアルの実行中に発生しました。これがなぜ壊れたのか？私はCUDAとCuDNNが正しくインストールされたpython3を実行しています。インストールの指示に従ってTensorFlowのインストールを確認できたので、CuDNN/CUDAの基本機能が動作するはずです。私はUbuntu 16.04でpython3を使用しています。

最近、翻訳チュートリアルを使用している他の人にこの問題がありましたか？このチュートリアルが他の人のために働いていると思っているとき、私がなぜこの問題を抱えているのか知っていますか？

`(tensorflow) nathan@nathan1:~/repos/tensorflow/models/tutorials/rnn/translate$ python3 translate.py --data_dir ~/data/tensorflow/translate/ 

Preparing WMT data in /home/nathan/data/tensorflow/translate/ 
2017-05-16 22:18:50.664841: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 
2017-05-16 22:18:50.664859: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 
2017-05-16 22:18:50.664864: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 
2017-05-16 22:18:50.664868: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 
2017-05-16 22:18:50.664872: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 
2017-05-16 22:18:50.665996: E tensorflow/stream_executor/cuda/cuda_driver.cc:405] failed call to cuInit: CUDA_ERROR_UNKNOWN 
2017-05-16 22:18:50.666149: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:158] retrieving CUDA diagnostic information for host: nathan1 
2017-05-16 22:18:50.666157: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: nathan1 
2017-05-16 22:18:50.666177: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: 375.66.0 
2017-05-16 22:18:50.666323: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:369] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.66 Mon May 1 15:29:16 PDT 2017 
GCC version: gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.4) 
""" 
2017-05-16 22:18:50.666338: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: 375.66.0 
2017-05-16 22:18:50.666343: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:300] kernel version seems to match DSO: 375.66.0 
Creating 3 layers of 1024 units. 
Traceback (most recent call last): 
    File "translate.py", line 322, in <module> 
    tf.app.run() 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 48, in run 
    _sys.exit(main(_sys.argv[:1] + flags_passthrough)) 
    File "translate.py", line 319, in main 
    train() 
    File "translate.py", line 178, in train 
    model = create_model(sess, False) 
    File "translate.py", line 136, in create_model 
    dtype=dtype) 
    File "/home/nathan/repos/tensorflow/models/tutorials/rnn/translate/seq2seq_model.py", line 179, in __init__ 
    softmax_loss_function=softmax_loss_function) 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 1201, in model_with_buckets 
    decoder_inputs[:bucket[1]]) 
    File "/home/nathan/repos/tensorflow/models/tutorials/rnn/translate/seq2seq_model.py", line 178, in <lambda> 
    lambda x, y: seq2seq_f(x, y, False), 
    File "/home/nathan/repos/tensorflow/models/tutorials/rnn/translate/seq2seq_model.py", line 142, in seq2seq_f 
    dtype=dtype) 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 855, in embedding_attention_seq2seq 
    encoder_cell, encoder_inputs, dtype=dtype) 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn.py", line 197, in static_rnn 
    (output, state) = call_cell() 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn.py", line 184, in <lambda> 
    call_cell = lambda: cell(input_, state) 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 881, in __call__ 
    return self._cell(embedded, state) 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 953, in __call__ 
    cur_inp, new_state = cell(cur_inp, cur_state) 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 146, in __call__ 
    with _checked_scope(self, scope or "gru_cell", reuse=self._reuse): 
    File "/usr/lib/python3.5/contextlib.py", line 59, in __enter__ 
    return next(self.gen) 
    File "/home/nathan/.local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 77, in _checked_scope 
    type(cell).__name__)) 
ValueError: Attempt to reuse RNNCell <tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.GRUCell object at 0x7f0b66e04b70> with a different variable scope than its first use. First use of cell was with scope 'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/multi_rnn_cell/cell_0/gru_cell', this attempt is with scope 'embedding_attention_seq2seq/rnn/multi_rnn_cell/cell_0/gru_cell'. Please create a new instance of the cell if you would like it to use a different set of weights. If before you were using: MultiRNNCell([GRUCell(...)] * num_layers), change to: MultiRNNCell([GRUCell(...) for _ in range(num_layers)]). If before you were using the same cell instance as both the forward and reverse cell of a bidirectional RNN, simply create two instances (one for forward, one for reverse). In May 2017, we will start transitioning this cell's behavior to use existing stored weights, if any, when it is called with scope=None (which can lead to silent model degradation, so this error will remain until then.` 

Answer 1

この問題は、テンソルフローそのものの更新が原因です。最近の更新では、tensorflowは以前許可されたrnnセルを再利用することはできませんでした。

rnn_cell = tf.contrib.rnn.LSTMCell(300) 

output, _ = tf.nn.bidirectional_dynamic_rnn(rnn_cell, rnn_cell, data, dtype = tf.float32) 

#^^^^allowed before but not now 

fw_rnn_cell = tf.contrib.rnn.LSTMCell(300) 
bw_rnn_cell = tf.contrib.rnn.LSTMCell(300) 
output, _ = tf.nn.bidirectional_dynamic_rnn(fw_rnn_cell, bw_rnn_cell, data, dtype = tf.float32) 

#^^^^allowed now 

#Another example 

rnn_cell = tf.contrib.rnn.LSTMCell(300) 
output_layer_1, _ = tf.nn.dynamic_rnn(rnn_cell, data, dtype = tf.float32, scope = "rnn_layer_1") 
output_layer_2, _ = tf.nn.dynamic_rnn(rnn_cell, output_layer_1, dtype = tf.float32, scope = "rnn_layer_2") 

#^^^^allowed before but not now 

rnn_cell_1 = tf.contrib.rnn.LSTMCell(300) 
output_layer_1, _ = tf.nn.dynamic_rnn(rnn_cell_1, data, dtype = tf.float32, scope = "rnn_layer_1") 
rnn_cell_2 = tf.contrib.rnn.LSTMCell(300) 
output_layer_2, _ = tf.nn.dynamic_rnn(rnn_cell_2, output_layer_1, dtype = tf.float32, scope = "rnn_layer_2") 

#^^^^allowed now 

あなたは何ができますか？あなたは選ぶことができます：別の新しいチュートリアル

へ

1. 変更は

2. は、古いバージョンを使用して、自分でコードを修正tensorflow