在CSV中读取时，Tensorflow错误“UnimplementedError:不支持将字符串转换为浮点”

我目前正在使用我自己的数据来扩展我对如何在谷歌云平台上使用数据流和ML引擎的理解。我使用了，因为这类似于我计划对自己的数据所做的。执行时，我收到以下错误：

    <ipython-input-8-17576dd9a3da> in <lambda>(fn)
     12         # Create dataset from file list
     13         filenames = tf.data.Dataset.from_tensor_slices(file_list)
---> 14         dataset = filenames.flat_map(lambda fn: tf.data.TextLineDataset(fn).skip(1))
     15         dataset = dataset.map(decode_csv)
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/data/ops/readers.pyc in __init__(self, filenames, compression_type, buffer_size)
         46     super(TextLineDataset, self).__init__()
         47     self._filenames = ops.convert_to_tensor(
    ---> 48         filenames, dtype=dtypes.string, name="filenames")
         49     self._compression_type = convert.optional_param_to_tensor(
         50         "compression_type",

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in convert_to_tensor(value, dtype, name, preferred_dtype)
        930       name=name,
        931       preferred_dtype=preferred_dtype,
    --> 932       as_ref=False)
        933 
        934 

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
       1020 
       1021     if ret is None:
    -> 1022       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
       1023 
       1024     if ret is NotImplemented:

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in _TensorTensorConversionFunction(t, dtype, name, as_ref)
        864     raise ValueError(
        865         "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %
    --> 866         (dtype.name, t.dtype.name, str(t)))
        867   return t
        868 

    ValueError: Tensor conversion requested dtype string for Tensor with dtype float32: 'Tensor("arg0:0", shape=(), dtype=float32)'

INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f5127451fd0>, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'logs_trained', '_save_summary_steps': 100}
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 300 secs (eval_spec.throttle_secs) or training is finished.
INFO:tensorflow:Create CheckpointSaverHook.

UnimplementedErrorTraceback (most recent call last)
<ipython-input-13-9982390b7e4a> in <module>()
      1 # Run the model
      2 shutil.rmtree('logs_trained', ignore_errors = True) # start fresh each time
----> 3 train_and_evaluate('logs_trained')

<ipython-input-12-b456e07a6c7d> in train_and_evaluate(output_dir)
     17                        throttle_secs = 300,  # evaluate every N seconds
     18                        exporters = exporter)
---> 19   tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.pyc in train_and_evaluate(estimator, train_spec, eval_spec)
    430       config.task_type != run_config_lib.TaskType.EVALUATOR):
    431     logging.info('Running training and evaluation locally (non-distributed).')
--> 432     executor.run_local()
    433     return
    434 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.pyc in run_local(self)
    609           input_fn=self._train_spec.input_fn,
    610           max_steps=self._train_spec.max_steps,
--> 611           hooks=train_hooks)
    612 
    613       # Final export signal: For any eval result with global_step >= train

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    312 
    313     saving_listeners = _check_listeners_type(saving_listeners)
--> 314     loss = self._train_model(input_fn, hooks, saving_listeners)
    315     logging.info('Loss for final step: %s.', loss)
    316     return self

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model(self, input_fn, hooks, saving_listeners)
    813         loss = None
    814         while not mon_sess.should_stop():
--> 815           _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
    816       return loss
    817 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    537                           feed_dict=feed_dict,
    538                           options=options,
--> 539                           run_metadata=run_metadata)
    540 
    541   def run_step_fn(self, step_fn):

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
   1011                               feed_dict=feed_dict,
   1012                               options=options,
-> 1013                               run_metadata=run_metadata)
   1014       except _PREEMPTION_ERRORS as e:
   1015         logging.info('An error was raised. This may be due to a preemption in '

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
   1102         raise six.reraise(*original_exc_info)
   1103       else:
-> 1104         raise six.reraise(*original_exc_info)
   1105 
   1106 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
   1087   def run(self, *args, **kwargs):
   1088     try:
-> 1089       return self._sess.run(*args, **kwargs)
   1090     except _PREEMPTION_ERRORS:
   1091       raise

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
   1159                                   feed_dict=feed_dict,
   1160                                   options=options,
-> 1161                                   run_metadata=run_metadata)
   1162 
   1163     for hook in self._hooks:

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
    939 
    940   def run(self, *args, **kwargs):
--> 941     return self._sess.run(*args, **kwargs)
    942 
    943   def run_step_fn(self, step_fn, raw_session, run_with_hooks):

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    893     try:
    894       result = self._run(None, fetches, feed_dict, options_ptr,
--> 895                          run_metadata_ptr)
    896       if run_metadata:
    897         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1126     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1127       results = self._do_run(handle, final_targets, final_fetches,
-> 1128                              feed_dict_tensor, options, run_metadata)
   1129     else:
   1130       results = []

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1342     if handle is None:
   1343       return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1344                            options, run_metadata)
   1345     else:
   1346       return self._do_call(_prun_fn, self._session, handle, feeds, fetches)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
   1361         except KeyError:
   1362           pass
-> 1363       raise type(e)(node_def, op, message)
   1364 
   1365   def _extend_graph(self):

UnimplementedError: Cast string to float is not supported
     [[Node: head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](head/labels)]]

Caused by op u'head/ToFloat', defined at:
  File "/usr/local/envs/py2env/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/local/envs/py2env/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/ioloop.py", line 887, in start
    handler_func(fd_obj, events)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2882, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-13-9982390b7e4a>", line 3, in <module>
    train_and_evaluate('logs_trained')
  File "<ipython-input-12-b456e07a6c7d>", line 19, in train_and_evaluate
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.py", line 432, in train_and_evaluate
    executor.run_local()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.py", line 611, in run_local
    hooks=train_hooks)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 314, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 743, in _train_model
    features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 725, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 528, in _model_fn
    config=config)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 216, in _dnn_linear_combined_model_fn
    logits=logits)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/head.py", line 1078, in create_estimator_spec
    features=features, mode=mode, logits=logits, labels=labels)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/head.py", line 1026, in create_loss
    labels = math_ops.to_float(labels)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 807, in to_float
    return cast(x, dtypes.float32, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 758, in cast
    return gen_math_ops.cast(x, base_type, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 919, in cast
    "Cast", x=x, DstT=DstT, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
    op_def=op_def)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

UnimplementedError (see above for traceback): Cast string to float is not supported
     [[Node: head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](head/labels)]]

UnimplementedError:不支持将字符串强制转换为浮点 [[Node:head/ToFloat=Cast[DstT=DT_FLOAT，SrcT=DT_STRING，_device=/job:localhost/replica:0/task:0/device:CPU:0]head/labels]]

在我的预处理步骤中，csv文件被拆分为许多较小的csv，因此需要将它们合并，以便创建数据集

我的代码：

# In[1]:


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import shutil
import numpy as np
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)

#excluded for Stack question
BUCKET = '<my bucket>'
PROJECT = '<my project>'
REGION = '<my region>'


import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION


get_ipython().run_cell_magic('bash', '', 'if ! gsutil ls | grep -q gs://${BUCKET}/; then\n  gsutil mb -l ${REGION} gs://${BUCKET}\nfi')


get_ipython().run_line_magic('bash', '')
#gsutil ls gs://${BUCKET}/logs2/preproc_tft/*-00000*
gsutil ls gs://${BUCKET}/logs2/preproc/*-00000*


CSV_COLUMNS ='end_time,device,device_os,device_os_version,latency,megacycles,cost,Status,device_brand,device_family,browser_version,app,ua_parse,key'.split(',')
LABEL_COLUMN = 'Status'
KEY_COLUMN = 'key'

DEFAULTS = [['null'], ['null'],['null'],['null'], [0.0],[0.0],[0.0], ['null'], ['null'],['null'],['null'],['null'],['null'],['null'],['nokey']]

TRAIN_STEPS = 1000
EVAL_STEPS = None
BATCH_SIZE = 512
NEMBEDS = 3
NNSIZE = [64, 16, 4]

def read_dataset(filename, mode, batch_size=512):
    def _input_fn():
        def decode_csv(value_column):
            columns = tf.decode_csv(value_column, record_defaults=DEFAULTS)
            features = dict(zip(CSV_COLUMNS, columns))
            label = features.pop(LABEL_COLUMN)
            return features, label

        # Create list of files that match pattern
        file_list = tf.gfile.Glob(filename)

        # Create dataset from file list
        filenames = tf.data.Dataset.from_tensor_slices(tf.constant(file_list, dtype=tf.string))
        dataset = filenames.flat_map(lambda fn: tf.data.TextLineDataset(fn).skip(1))
        dataset = dataset.map(decode_csv)
        #dataset = (tf.data.TextLineDataset(file_list)  # Read text file
        #           .map(decode_csv))  # Transform each elem by applying decode_csv fn

        if mode == tf.estimator.ModeKeys.TRAIN:
            num_epochs = None  # indefinitely
            dataset = dataset.shuffle(buffer_size=10 * batch_size)
        else:
            num_epochs = 1  # end-of-input after this

        dataset = dataset.repeat(num_epochs).batch(batch_size)
        return dataset.make_one_shot_iterator().get_next()

    return _input_fn


# Define feature columns
def get_wide_deep():
  # Define column types
  feature_columns = []
  end_time = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('end_time', 1000), 10)
  feature_columns.append(end_time)
  device = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device', 1000), 10)
  feature_columns.append(device)
  device_os = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_os', 1000), 10)
  feature_columns.append(device_os)
  device_os_version = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_os_version', 1000), 10)
  feature_columns.append(device_os_version)
  latency = tf.feature_column.bucketized_column(
      tf.feature_column.numeric_column('latency'), 
      boundaries=[.000000, .000010, .000100, .001000, .010000, .100000])
  feature_columns.append(latency)
  megacycles = tf.feature_column.bucketized_column(
      tf.feature_column.numeric_column('megacycles'), 
      boundaries=[0, 50, 100, 200, 300])
  feature_columns.append(megacycles)
  cost = tf.feature_column.bucketized_column(
      tf.feature_column.numeric_column('cost'), 
      boundaries=[0.000001e-08, 1.000000e-08, 5.000000e-08, 10.000000e-08, 15.000000e-08 ])
  feature_columns.append(cost)
  device_brand = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_brand', 1000), 10)
  feature_columns.append(device_brand)
  device_family = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_family', 1000), 10)
  feature_columns.append(device_family)
  browser_version = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('browser_version', 1000), 10)
  feature_columns.append(browser_version)
  app = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('app', 1000), 10)
  feature_columns.append(app)
  ua_parse = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('ua_parse', 1000), 10)
  feature_columns.append(ua_parse)

  # Sparse columns are wide, have a linear relationship with the output
  wide = [end_time,
          device,
          device_os,
          device_os_version,
          latency,
          megacycles,
          cost,
          device_brand,
          device_family,
          browser_version,
          app,
          ua_parse]

  # Feature cross all the wide columns and embed into a lower dimension
  #crossed = tf.feature_column.crossed_column(wide, hash_bucket_size=20000)
  #embed = tf.feature_column.embedding_column(crossed, 3)

  # Continuous columns are deep, have a complex relationship with the output
  deep = [latency,
          megacycles,
          cost]
          #embed]
  return wide, deep


# Create serving input function to be able to serve predictions later using provided inputs
def serving_input_fn():
    feature_placeholders = {
        'end_time': tf.placeholder(tf.string, [None]),
        'device': tf.placeholder(tf.string, [None]),
        'device_os': tf.placeholder(tf.string, [None]),
        'device_os_version': tf.placeholder(tf.string, [None]),
        'latency': tf.placeholder(tf.float32, [None]),
        'megacycles': tf.placeholder(tf.float32, [None]),
        'cost': tf.placeholder(tf.float32, [None]),
        'device_brand': tf.placeholder(tf.string, [None]),
        'device_family': tf.placeholder(tf.string, [None]),
        'browser_version': tf.placeholder(tf.string, [None]),
        'app': tf.placeholder(tf.string, [None]),
        'ua_parse': tf.placeholder(tf.string, [None]),

    }
    features = {
        key: tf.expand_dims(tensor, -1)
        for key, tensor in feature_placeholders.items()
    }
    return tf.estimator.export.ServingInputReceiver(features, feature_placeholders)


# create metric for hyperparameter tuning
def my_rmse(labels, predictions):
    pred_values = predictions['predictions']
    return {'rmse': tf.metrics.root_mean_squared_error(labels, pred_values)}


# forward to key-column to export
def forward_key_to_export(estimator):
    estimator = tf.contrib.estimator.forward_features(estimator, KEY_COLUMN)
    # return estimator

    ## This shouldn't be necessary (I've filed CL/187793590 to update extenders.py with this code)
    config = estimator.config
    def model_fn2(features, labels, mode):
      estimatorSpec = estimator._call_model_fn(features, labels, mode, config=config)
      if estimatorSpec.export_outputs:
        for ekey in ['predict', 'serving_default']:
          if (ekey in estimatorSpec.export_outputs and
              isinstance(estimatorSpec.export_outputs[ekey],
                         tf.estimator.export.PredictOutput)):
               estimatorSpec.export_outputs[ekey] =                  tf.estimator.export.PredictOutput(estimatorSpec.predictions)
      return estimatorSpec
    return tf.estimator.Estimator(model_fn=model_fn2, config=config)
    ##


# Create estimator to train and evaluate
def train_and_evaluate(output_dir):
  wide, deep = get_wide_deep()
  estimator = tf.estimator.DNNLinearCombinedRegressor(
                       model_dir = output_dir,
                       linear_feature_columns = wide,
                       dnn_feature_columns = deep,
                       dnn_hidden_units = [64, 32])
  train_spec = tf.estimator.TrainSpec(
                       input_fn = read_dataset('gs://nosh_ml_models/logs2/preproc/train.*', mode = tf.estimator.ModeKeys.TRAIN),
                       max_steps = TRAIN_STEPS)
  exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)
  eval_spec = tf.estimator.EvalSpec(
                       input_fn = read_dataset('gs://nosh_ml_models/logs2/preproc/eval.*', mode = tf.estimator.ModeKeys.EVAL),
                       steps = None,
                       start_delay_secs = 60, # start evaluating after N seconds
                       throttle_secs = 300,  # evaluate every N seconds
                       exporters = exporter)
  tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)


# Run the model
shutil.rmtree('logs_trained', ignore_errors = True) # start fresh each time
train_and_evaluate('logs_trained')

现在我收到这个错误：

    <ipython-input-8-17576dd9a3da> in <lambda>(fn)
     12         # Create dataset from file list
     13         filenames = tf.data.Dataset.from_tensor_slices(file_list)
---> 14         dataset = filenames.flat_map(lambda fn: tf.data.TextLineDataset(fn).skip(1))
     15         dataset = dataset.map(decode_csv)
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/data/ops/readers.pyc in __init__(self, filenames, compression_type, buffer_size)
         46     super(TextLineDataset, self).__init__()
         47     self._filenames = ops.convert_to_tensor(
    ---> 48         filenames, dtype=dtypes.string, name="filenames")
         49     self._compression_type = convert.optional_param_to_tensor(
         50         "compression_type",

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in convert_to_tensor(value, dtype, name, preferred_dtype)
        930       name=name,
        931       preferred_dtype=preferred_dtype,
    --> 932       as_ref=False)
        933 
        934 

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
       1020 
       1021     if ret is None:
    -> 1022       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
       1023 
       1024     if ret is NotImplemented:

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in _TensorTensorConversionFunction(t, dtype, name, as_ref)
        864     raise ValueError(
        865         "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %
    --> 866         (dtype.name, t.dtype.name, str(t)))
        867   return t
        868 

    ValueError: Tensor conversion requested dtype string for Tensor with dtype float32: 'Tensor("arg0:0", shape=(), dtype=float32)'

INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f5127451fd0>, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'logs_trained', '_save_summary_steps': 100}
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 300 secs (eval_spec.throttle_secs) or training is finished.
INFO:tensorflow:Create CheckpointSaverHook.

UnimplementedErrorTraceback (most recent call last)
<ipython-input-13-9982390b7e4a> in <module>()
      1 # Run the model
      2 shutil.rmtree('logs_trained', ignore_errors = True) # start fresh each time
----> 3 train_and_evaluate('logs_trained')

<ipython-input-12-b456e07a6c7d> in train_and_evaluate(output_dir)
     17                        throttle_secs = 300,  # evaluate every N seconds
     18                        exporters = exporter)
---> 19   tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.pyc in train_and_evaluate(estimator, train_spec, eval_spec)
    430       config.task_type != run_config_lib.TaskType.EVALUATOR):
    431     logging.info('Running training and evaluation locally (non-distributed).')
--> 432     executor.run_local()
    433     return
    434 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.pyc in run_local(self)
    609           input_fn=self._train_spec.input_fn,
    610           max_steps=self._train_spec.max_steps,
--> 611           hooks=train_hooks)
    612 
    613       # Final export signal: For any eval result with global_step >= train

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    312 
    313     saving_listeners = _check_listeners_type(saving_listeners)
--> 314     loss = self._train_model(input_fn, hooks, saving_listeners)
    315     logging.info('Loss for final step: %s.', loss)
    316     return self

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model(self, input_fn, hooks, saving_listeners)
    813         loss = None
    814         while not mon_sess.should_stop():
--> 815           _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
    816       return loss
    817 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    537                           feed_dict=feed_dict,
    538                           options=options,
--> 539                           run_metadata=run_metadata)
    540 
    541   def run_step_fn(self, step_fn):

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
   1011                               feed_dict=feed_dict,
   1012                               options=options,
-> 1013                               run_metadata=run_metadata)
   1014       except _PREEMPTION_ERRORS as e:
   1015         logging.info('An error was raised. This may be due to a preemption in '

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
   1102         raise six.reraise(*original_exc_info)
   1103       else:
-> 1104         raise six.reraise(*original_exc_info)
   1105 
   1106 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
   1087   def run(self, *args, **kwargs):
   1088     try:
-> 1089       return self._sess.run(*args, **kwargs)
   1090     except _PREEMPTION_ERRORS:
   1091       raise

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
   1159                                   feed_dict=feed_dict,
   1160                                   options=options,
-> 1161                                   run_metadata=run_metadata)
   1162 
   1163     for hook in self._hooks:

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
    939 
    940   def run(self, *args, **kwargs):
--> 941     return self._sess.run(*args, **kwargs)
    942 
    943   def run_step_fn(self, step_fn, raw_session, run_with_hooks):

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    893     try:
    894       result = self._run(None, fetches, feed_dict, options_ptr,
--> 895                          run_metadata_ptr)
    896       if run_metadata:
    897         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1126     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1127       results = self._do_run(handle, final_targets, final_fetches,
-> 1128                              feed_dict_tensor, options, run_metadata)
   1129     else:
   1130       results = []

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1342     if handle is None:
   1343       return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1344                            options, run_metadata)
   1345     else:
   1346       return self._do_call(_prun_fn, self._session, handle, feeds, fetches)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
   1361         except KeyError:
   1362           pass
-> 1363       raise type(e)(node_def, op, message)
   1364 
   1365   def _extend_graph(self):

UnimplementedError: Cast string to float is not supported
     [[Node: head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](head/labels)]]

Caused by op u'head/ToFloat', defined at:
  File "/usr/local/envs/py2env/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/local/envs/py2env/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/ioloop.py", line 887, in start
    handler_func(fd_obj, events)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2882, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-13-9982390b7e4a>", line 3, in <module>
    train_and_evaluate('logs_trained')
  File "<ipython-input-12-b456e07a6c7d>", line 19, in train_and_evaluate
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.py", line 432, in train_and_evaluate
    executor.run_local()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.py", line 611, in run_local
    hooks=train_hooks)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 314, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 743, in _train_model
    features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 725, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 528, in _model_fn
    config=config)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 216, in _dnn_linear_combined_model_fn
    logits=logits)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/head.py", line 1078, in create_estimator_spec
    features=features, mode=mode, logits=logits, labels=labels)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/head.py", line 1026, in create_loss
    labels = math_ops.to_float(labels)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 807, in to_float
    return cast(x, dtypes.float32, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 758, in cast
    return gen_math_ops.cast(x, base_type, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 919, in cast
    "Cast", x=x, DstT=DstT, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
    op_def=op_def)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

UnimplementedError (see above for traceback): Cast string to float is not supported
     [[Node: head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](head/labels)]]

现在我得到了这个错误：

    <ipython-input-8-17576dd9a3da> in <lambda>(fn)
     12         # Create dataset from file list
     13         filenames = tf.data.Dataset.from_tensor_slices(file_list)
---> 14         dataset = filenames.flat_map(lambda fn: tf.data.TextLineDataset(fn).skip(1))
     15         dataset = dataset.map(decode_csv)
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/data/ops/readers.pyc in __init__(self, filenames, compression_type, buffer_size)
         46     super(TextLineDataset, self).__init__()
         47     self._filenames = ops.convert_to_tensor(
    ---> 48         filenames, dtype=dtypes.string, name="filenames")
         49     self._compression_type = convert.optional_param_to_tensor(
         50         "compression_type",

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in convert_to_tensor(value, dtype, name, preferred_dtype)
        930       name=name,
        931       preferred_dtype=preferred_dtype,
    --> 932       as_ref=False)
        933 
        934 

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
       1020 
       1021     if ret is None:
    -> 1022       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
       1023 
       1024     if ret is NotImplemented:

    /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in _TensorTensorConversionFunction(t, dtype, name, as_ref)
        864     raise ValueError(
        865         "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %
    --> 866         (dtype.name, t.dtype.name, str(t)))
        867   return t
        868 

    ValueError: Tensor conversion requested dtype string for Tensor with dtype float32: 'Tensor("arg0:0", shape=(), dtype=float32)'

INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f5127451fd0>, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'logs_trained', '_save_summary_steps': 100}
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 300 secs (eval_spec.throttle_secs) or training is finished.
INFO:tensorflow:Create CheckpointSaverHook.

UnimplementedErrorTraceback (most recent call last)
<ipython-input-13-9982390b7e4a> in <module>()
      1 # Run the model
      2 shutil.rmtree('logs_trained', ignore_errors = True) # start fresh each time
----> 3 train_and_evaluate('logs_trained')

<ipython-input-12-b456e07a6c7d> in train_and_evaluate(output_dir)
     17                        throttle_secs = 300,  # evaluate every N seconds
     18                        exporters = exporter)
---> 19   tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.pyc in train_and_evaluate(estimator, train_spec, eval_spec)
    430       config.task_type != run_config_lib.TaskType.EVALUATOR):
    431     logging.info('Running training and evaluation locally (non-distributed).')
--> 432     executor.run_local()
    433     return
    434 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.pyc in run_local(self)
    609           input_fn=self._train_spec.input_fn,
    610           max_steps=self._train_spec.max_steps,
--> 611           hooks=train_hooks)
    612 
    613       # Final export signal: For any eval result with global_step >= train

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    312 
    313     saving_listeners = _check_listeners_type(saving_listeners)
--> 314     loss = self._train_model(input_fn, hooks, saving_listeners)
    315     logging.info('Loss for final step: %s.', loss)
    316     return self

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model(self, input_fn, hooks, saving_listeners)
    813         loss = None
    814         while not mon_sess.should_stop():
--> 815           _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
    816       return loss
    817 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    537                           feed_dict=feed_dict,
    538                           options=options,
--> 539                           run_metadata=run_metadata)
    540 
    541   def run_step_fn(self, step_fn):

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
   1011                               feed_dict=feed_dict,
   1012                               options=options,
-> 1013                               run_metadata=run_metadata)
   1014       except _PREEMPTION_ERRORS as e:
   1015         logging.info('An error was raised. This may be due to a preemption in '

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
   1102         raise six.reraise(*original_exc_info)
   1103       else:
-> 1104         raise six.reraise(*original_exc_info)
   1105 
   1106 

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
   1087   def run(self, *args, **kwargs):
   1088     try:
-> 1089       return self._sess.run(*args, **kwargs)
   1090     except _PREEMPTION_ERRORS:
   1091       raise

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata)
   1159                                   feed_dict=feed_dict,
   1160                                   options=options,
-> 1161                                   run_metadata=run_metadata)
   1162 
   1163     for hook in self._hooks:

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs)
    939 
    940   def run(self, *args, **kwargs):
--> 941     return self._sess.run(*args, **kwargs)
    942 
    943   def run_step_fn(self, step_fn, raw_session, run_with_hooks):

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    893     try:
    894       result = self._run(None, fetches, feed_dict, options_ptr,
--> 895                          run_metadata_ptr)
    896       if run_metadata:
    897         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1126     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1127       results = self._do_run(handle, final_targets, final_fetches,
-> 1128                              feed_dict_tensor, options, run_metadata)
   1129     else:
   1130       results = []

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1342     if handle is None:
   1343       return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1344                            options, run_metadata)
   1345     else:
   1346       return self._do_call(_prun_fn, self._session, handle, feeds, fetches)

/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
   1361         except KeyError:
   1362           pass
-> 1363       raise type(e)(node_def, op, message)
   1364 
   1365   def _extend_graph(self):

UnimplementedError: Cast string to float is not supported
     [[Node: head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](head/labels)]]

Caused by op u'head/ToFloat', defined at:
  File "/usr/local/envs/py2env/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/local/envs/py2env/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/ioloop.py", line 887, in start
    handler_func(fd_obj, events)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2882, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-13-9982390b7e4a>", line 3, in <module>
    train_and_evaluate('logs_trained')
  File "<ipython-input-12-b456e07a6c7d>", line 19, in train_and_evaluate
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.py", line 432, in train_and_evaluate
    executor.run_local()
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/training.py", line 611, in run_local
    hooks=train_hooks)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 314, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 743, in _train_model
    features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 725, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 528, in _model_fn
    config=config)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 216, in _dnn_linear_combined_model_fn
    logits=logits)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/head.py", line 1078, in create_estimator_spec
    features=features, mode=mode, logits=logits, labels=labels)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/head.py", line 1026, in create_loss
    labels = math_ops.to_float(labels)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 807, in to_float
    return cast(x, dtypes.float32, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 758, in cast
    return gen_math_ops.cast(x, base_type, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 919, in cast
    "Cast", x=x, DstT=DstT, name=name)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
    op_def=op_def)
  File "/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

UnimplementedError (see above for traceback): Cast string to float is not supported
     [[Node: head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](head/labels)]]

我如何读取具有下图格式的csv？ 您可以使用跳过数据集的元素。 但是，tf.data.TextLineDatasetfile_list会遇到一个小问题，因为它只会跳过第一个文件的第一行。幸运的是，您可以使用循环遍历文件名并跳过每个文件的第一行，如下所示：

# Start by making a dataset of filenames.
filenames = tf.data.Dataset.from_tensor_slices(
    tf.constant(file_list, dtype=tf.string))

# For each filename, create a TextLineDataset and skip the first line.
# The resulting dataset contains all the non-header lines of all files in
# `file_list`.
dataset = filenames.flat_map(lambda fn: tf.data.TextLineDataset(fn).skip(1))

# Then continue to preprocess the data as needed.
dataset = dataset.map(decode_csv)

顺便说一句，TensorFlow 1.8目前是一个候选版本，它引入了一个用于读取CSV数据的实用程序，名为，它可能有助于简化CSV相关代码。

您可以使用它跳过数据集的一个元素。 但是，tf.data.TextLineDatasetfile_list会遇到一个小问题，因为它只会跳过第一个文件的第一行。幸运的是，您可以使用循环遍历文件名并跳过每个文件的第一行，如下所示：

# Start by making a dataset of filenames.
filenames = tf.data.Dataset.from_tensor_slices(
    tf.constant(file_list, dtype=tf.string))

# For each filename, create a TextLineDataset and skip the first line.
# The resulting dataset contains all the non-header lines of all files in
# `file_list`.
dataset = filenames.flat_map(lambda fn: tf.data.TextLineDataset(fn).skip(1))

# Then continue to preprocess the data as needed.
dataset = dataset.map(decode_csv)

---

顺便说一句，TensorFlow 1.8目前是一个候选版本，它引入了一个用于读取CSV数据的实用程序，名为，这可能有助于简化CSV相关代码。

感谢您的回复。我添加了您的代码，现在收到了以下错误：Tensor conversion请求使用dtype float32的Tensor的dtype字符串：“Tensorarg0:0，shape=，dtype=float32”我可以向您发送回溯或编辑此问题。csv既有string值也有float32值，tf不处理此转换吗？添加了修改后的代码并回溯到原始问题。是否可以尝试打印文件列表和文件名的内容。输出类型？从错误消息来看，似乎出于某种未知原因，file_list变量是一个由float32值组成的list/1-D数组。但是，这没有意义，因为tf.gfile.Glob的输出应该始终是字符串列表。file_list prints[]和filenames.output_types prints tf.float32问题似乎是传递给tf.gfile.Glob的文件名与任何文件都不匹配。空列表被隐式转换为tf.float32张量，这将导致不明确的类型错误。我修改了答案中的代码，将其转换为字符串并避免了类型错误。您能检查文件名指定是否正确吗？谢谢您的回复。我添加了您的代码，现在收到了以下错误：Tensor conversion请求使用dtype float32的Tensor的dtype字符串：“Tensorarg0:0，shape=，dtype=float32”我可以向您发送回溯或编辑此问题。csv既有string值也有float32值，tf不处理此转换吗？添加了修改后的代码并回溯到原始问题。是否可以尝试打印文件列表和文件名的内容。输出类型？从错误消息来看，似乎出于某种未知原因，file_list变量是一个由float32值组成的list/1-D数组。但是，这没有意义，因为tf.gfile.Glob的输出应该始终是字符串列表。file_list prints[]和filenames.output_types prints tf.float32问题似乎是传递给tf.gfile.Glob的文件名与任何文件都不匹配。空列表被隐式转换为tf.float32张量，这将导致不明确的类型错误。我修改了答案中的代码，将其转换为字符串并避免了类型错误。您能检查文件名指定是否正确吗？