python - cloudml retraining inception - 收到超出有效范围的标签值

Question

我正在关注鲜花教程，以便在 google cloud ml 上重新训练 inception。我可以运行教程、训练、预测，就好了。

然后我用鲜花数据集替换了我自己的测试数据集。图像数字的光学字符识别。

训练模型时，我收到错误：

Invalid argument: Received a label value of 13 which is outside the valid range of [0, 6). Label values: 6 3 2 7 3 7 6 6 12 6 5 2 3 6 8 8 8 8 4 6 5 13 7 4 8 12 5 2 4 12 12 8 8 8 12 6 4 2 12 4 3 8 2 6 8 12 2 8 4 6 2 4 12 5 5 7 6 2 2 3 2 8 2 5 2 8 2 7 4 12 8 4 2 4 8 2 2 8 2 8 7 6 8 3 5 5 5 8 8 2 5 3 9 8 5 8 3 2 5 4

训练和评估数据集的格式如下所示：

root@e925cd9502c0:~/MeerkatReader/cloudML# head training_dataGCS.csv
gs://api-project-773889352370-ml/TrainingData/0_2.jpg,H
gs://api-project-773889352370-ml/TrainingData/0_4.jpg,One
gs://api-project-773889352370-ml/TrainingData/0_5.jpg,Five

dict文件看起来像这样

$ cat cloudML/dict.txt
Eight
F
Five
Forward_slash
Four
H
Nine
One
Seven
Six
Three
Two
Zero

我最初有 1、2、3、4 和 / 之类的标签，但我将它们更改为字符串，以防它们是特殊字符（尤其是 /）。我可以在这里看到一条类似的消息，但这与基于 0 的索引有关。

该消息的奇怪之处在于，确实有 13 种标签类型。不知何故，张量流只寻找 7 (0-6)。我的问题是，什么样的格式错误可能会使 tensorflow 认为标签数量少于实际数量。我可以确认测试和训练数据 80-20 拆分都具有所有标签类（尽管频率不同）。

我正在从 google 提供的最新 docker build 运行。

`docker run -it -p "127.0.0.1:8080:8080" --entrypoint=/bin/bash  gcr.io/cloud-datalab/datalab:local-20161227

我正在使用提交培训作业

  # Submit training job.
gcloud beta ml jobs submit training "$JOB_ID" \
  --module-name trainer.task \
  --package-path trainer \
  --staging-bucket "$BUCKET" \
  --region us-central1 \
  -- \
  --output_path "${GCS_PATH}/training" \
  --eval_data_paths "${GCS_PATH}/preproc/eval*" \
  --train_data_paths "${GCS_PATH}/preproc/train*"

完整错误：

Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, Received a label value of 13 which is outside the valid range of [0, 6). Label values: 6 3 2 7 3 7 6 6 12 6 5 2 3 6 8 8 8 8 4 6 5 13 7 4 8 12 5 2 4 12 12 8 8 8 12 6 4 2 12 4 3 8 2 6 8 12 2 8 4 6 2 4 12 5 5 7 6 2 2 3 2 8 2 5 2 8 2 7 4 12 8 4 2 4 8 2 2 8 2 8 7 6 8 3 5 5 5 8 8 2 5 3 9 8 5 8 3 2 5 4 [[Node: evaluate/xentropy/xentropy = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:master/replica:0/task:0/cpu:0"](final_ops/input/Wx_plus_b/fully_connected_1/BiasAdd, inputs/Squeeze)]] Caused by op u'evaluate/xentropy/xentropy', defined at: File "/usr/lib/python2.7/runpy.py", line 162, in _run_module_as_main "__main__", fname, loader, pkg_name) File "/usr/lib/python2.7/runpy.py", line 72, in _run_code exec code in run_globals File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 545, in <module> tf.app.run() File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 43, in run sys.exit(main(sys.argv[:1] + flags_passthrough)) File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 308, in main run(model, argv) File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 439, in run dispatch(args, model, cluster, task) File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 480, in dispatch Trainer(args, model, cluster, task).run_training() File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 187, in run_training self.args.batch_size) File "/root/.local/lib/python2.7/site-packages/trainer/model.py", line 278, in build_train_graph return self.build_graph(data_paths, batch_size, GraphMod.TRAIN) File "/root/.local/lib/python2.7/site-packages/trainer/model.py", line 256, in build_graph loss_value = loss(logits, labels) File "/root/.local/lib/python2.7/site-packages/trainer/model.py", line 396, in loss logits, labels, name='xentropy') File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 1544, in sparse_softmax_cross_entropy_with_logits precise_logits, labels, name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 2376, in _sparse_softmax_cross_entropy_with_logits features=features, labels=labels, name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2238, 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 1130, in __init__ self._traceback = _extract_stack() InvalidArgumentError (see above for traceback): Received a label value of 13 which is outside the valid range of [0, 6). Label values: 6 3 2 7 3 7 6 6 12 6 5 2 3 6 8 8 8 8 4 6 5 13 7 4 8 12 5 2 4 12 12 8 8 8 12 6 4 2 12 4 3 8 2 6 8 12 2 8 4 6 2 4 12 5 5 7 6 2 2 3 2 8 2 5 2 8 2 7 4 12 8 4 2 4 8 2 2 8 2 8 7 6 8 3 5 5 5 8 8 2 5 3 9 8 5 8 3 2 5 4 [[Node: evaluate/xentropy/xentropy = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:master/replica:0/task:0/cpu:0"](final_ops/input/Wx_plus_b/fully_connected_1/BiasAdd, inputs/Squeeze)]]

在我的桶里一切看起来都很好

并保存我的日志事件。

score 1 · Accepted Answer

我认为您需要--label_count 13在提交培训工作时指定。该标志应该在之后的第二组标志中--，因为它需要传递给您正在执行的代码，而不是 gcloud/Cloud ML。

问题是 TensorFlow 训练代码需要知道在开始遍历数据之前要输出多少 logit；因此它无法从预处理步骤检查中间文件。

让我知道这是否有帮助。

score 0 · Accepted Answer

--model specific 之后的标志在model.py文件中指定：

def create_model():
  """Factory method that creates model to be used by generic task.py."""
  parser = argparse.ArgumentParser()
  # Label count needs to correspond to nubmer of labels in dictionary used
  # during preprocessing.
  parser.add_argument('--label_count', type=int, default=5)
  parser.add_argument('--dropout', type=float, default=0.5)
  parser.add_argument(
      '--inception_checkpoint_file',
      type=str,
      default=DEFAULT_INCEPTION_CHECKPOINT)
  args, task_args = parser.parse_known_args()
  override_if_not_in_args('--max_steps', '1000', task_args)
  override_if_not_in_args('--batch_size', '100', task_args)
  override_if_not_in_args('--eval_set_size', '370', task_args)
  override_if_not_in_args('--eval_interval_secs', '2', task_args)
  override_if_not_in_args('--log_interval_secs', '2', task_args)
  override_if_not_in_args('--min_train_eval_rate', '2', task_args)
  return Model(args.label_count, args.dropout,
               args.inception_checkpoint_file), task_args

请注意，您可以更改 label_count、dropout、max_steps 等内容以影响模型训练。

HTH。

python - cloudml retraining inception - 收到超出有效范围的标签值

2 回答 2

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