Question

我试图使用tensorflow。输入属性类似于 census 示例，但LABEL列是连续值。我执行了以下命令：

test-server＃：〜/ aaaml-samples / arbitrator $ gcloud ml-engine local train --module-name trainer.task --package-path trainer / --train-files $ TRAIN_DATA --eval- files $ EVAL_DATA --train-steps 1000 --job-dir $ MODEL_DIR

Filename:  ['/home/madhukar_mhraju/aaaml-samples/arbitrator/data/aaa.data.csv']
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
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.
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.
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.
Filename:  ['/home/madhukar_mhraju/aaaml-samples/arbitrator/data/aaa.test.csv']
Filename:  ['/home/madhukar_mhraju/aaaml-samples/arbitrator/data/aaa.test.csv']

Traceback (most recent call last):
  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 "/home/madhukar_mhraju/aaaml-samples/arbitrator/trainer/task.py", line 193, in <module>
    learn_runner.run(generate_experiment_fn(**arguments), job_dir)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/learn_runner.py", line 106, in run
    return task()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/experiment.py", line 465, in train_and_evaluate
    export_results = self._maybe_export(eval_result)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/experiment.py", line 484, in _maybe_export
    compat.as_bytes(strategy.name))))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/export_strategy.py", line 32, in export
    return self.export_fn(estimator, export_path)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py", line 283, in export_fn
    exports_to_keep=exports_to_keep)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/framework/experimental.py", line 64, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1264, in export_savedmodel
    model_fn_lib.ModeKeys.INFER)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1133, in _call_model_fn
    model_fn_results = self._model_fn(features, labels, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py", line 268, in _dnn_linear_combined_model_fn
    scope=scope)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 531, in weighted_sum_from_feature_columns
    transformed_tensor = transformer.transform(column)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 879, in transform
    feature_column.insert_transformed_feature(self._columns_to_tensors)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py", line 528, in insert_transformed_feature
    sparse_values = string_ops.as_string(input_tensor.values)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_string_ops.py", line 51, in as_string
    width=width, fill=fill, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 585, in apply_op
    param_name=input_name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 61, in _SatisfiesTypeConstraint
    ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))

TypeError：传递给参数＆＃39;输入＆＃39;没有DataType字符串在允许值列表中：int32，int64，complex64，float32，float64， bool，int8

是tensorflow的新手。我知道在处理评估文件（aaa.test.csv）时会出现此问题。正确定义评估文件数据和格式。并且列数据类型也已正确映射。但我不确定错误发生的原因。

Answer 1

1）训练数据csv中有列标题。当我生成数据时，我随机重新排序它们，这导致列标题被移动到中间的某个位置。因此类型错误。由于训练数据很大，很难找到。

TypeError：传递给参数＆＃39;输入＆＃39; DataType字符串不在允许值列表中：int32，int64，complex64，float32，float64，bool，int8

1 个答案: