问题：
Tensorflow错误"未实现的错误：不支持将字符串转换为浮动"-使用估计器的线性分类器模型
陶超
以下是已经采取的步骤：
- 为tensorflow创建了csv输入文件
错误发生在估计器运行读取输入数据时。
错误日志：
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': 'sample_dir', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001E370166828>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
Created Estimator
Defining Train Spec
Train Spec Defination Completed
Defining Exporter
Defining Eval Spec
Eval Spec Defination Completed
Running Estimator
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 10 secs (eval_spec.throttle_secs) or training is finished.
Created Dataset
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into sample_dir\model.ckpt.
---------------------------------------------------------------------------
UnimplementedError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1321 try:
-> 1322 return fn(*args)
1323 except errors.OpError as e:
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1306 return self._call_tf_sessionrun(
-> 1307 options, feed_dict, fetch_list, target_list, run_metadata)
1308
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1408 self._session, options, feed_dict, fetch_list, target_list,
-> 1409 run_metadata)
1410 else:
UnimplementedError: Cast string to float is not supported
[[Node: linear/head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _class=["loc:@linea...t/Switch_1"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](linear/head/labels/ExpandDims, ^linear/head/labels/assert_equal/Assert/Assert)]]
During handling of the above exception, another exception occurred:
UnimplementedError Traceback (most recent call last)
<ipython-input-229-7ea5d3d759fb> in <module>()
----> 1 train_and_evaluate(OUTDIR, num_train_steps=5)
<ipython-input-227-891dd877d57e> in train_and_evaluate(output_dir, num_train_steps)
26
27 print('Running Estimator')
---> 28 tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py in train_and_evaluate(estimator, train_spec, eval_spec)
445 '(with task id 0). Given task id {}'.format(config.task_id))
446
--> 447 return executor.run()
448
449
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py in run(self)
529 config.task_type != run_config_lib.TaskType.EVALUATOR):
530 logging.info('Running training and evaluation locally (non-distributed).')
--> 531 return self.run_local()
532
533 # Distributed case.
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py in run_local(self)
667 input_fn=self._train_spec.input_fn,
668 max_steps=self._train_spec.max_steps,
--> 669 hooks=train_hooks)
670
671 if not self._continuous_eval_listener.before_eval():
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
364
365 saving_listeners = _check_listeners_type(saving_listeners)
--> 366 loss = self._train_model(input_fn, hooks, saving_listeners)
367 logging.info('Loss for final step: %s.', loss)
368 return self
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1117 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1118 else:
-> 1119 return self._train_model_default(input_fn, hooks, saving_listeners)
1120
1121 def _train_model_default(self, input_fn, hooks, saving_listeners):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1133 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
1134 hooks, global_step_tensor,
-> 1135 saving_listeners)
1136
1137 def _train_model_distributed(self, input_fn, hooks, saving_listeners):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners)
1334 loss = None
1335 while not mon_sess.should_stop():
-> 1336 _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
1337 return loss
1338
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in run(self, fetches, feed_dict, options, run_metadata)
575 feed_dict=feed_dict,
576 options=options,
--> 577 run_metadata=run_metadata)
578
579 def run_step_fn(self, step_fn):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in run(self, fetches, feed_dict, options, run_metadata)
1051 feed_dict=feed_dict,
1052 options=options,
-> 1053 run_metadata=run_metadata)
1054 except _PREEMPTION_ERRORS as e:
1055 logging.info('An error was raised. This may be due to a preemption in '
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in run(self, *args, **kwargs)
1142 raise six.reraise(*original_exc_info)
1143 else:
-> 1144 raise six.reraise(*original_exc_info)
1145
1146
C:\ProgramData\Anaconda3\lib\site-packages\six.py in reraise(tp, value, tb)
691 if value.__traceback__ is not tb:
692 raise value.with_traceback(tb)
--> 693 raise value
694 finally:
695 value = None
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in run(self, *args, **kwargs)
1127 def run(self, *args, **kwargs):
1128 try:
-> 1129 return self._sess.run(*args, **kwargs)
1130 except _PREEMPTION_ERRORS:
1131 raise
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in run(self, fetches, feed_dict, options, run_metadata)
1199 feed_dict=feed_dict,
1200 options=options,
-> 1201 run_metadata=run_metadata)
1202
1203 for hook in self._hooks:
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in run(self, *args, **kwargs)
979
980 def run(self, *args, **kwargs):
--> 981 return self._sess.run(*args, **kwargs)
982
983 def run_step_fn(self, step_fn, raw_session, run_with_hooks):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
898 try:
899 result = self._run(None, fetches, feed_dict, options_ptr,
--> 900 run_metadata_ptr)
901 if run_metadata:
902 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1133 if final_fetches or final_targets or (handle and feed_dict_tensor):
1134 results = self._do_run(handle, final_targets, final_fetches,
-> 1135 feed_dict_tensor, options, run_metadata)
1136 else:
1137 results = []
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1314 if handle is None:
1315 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1316 run_metadata)
1317 else:
1318 return self._do_call(_prun_fn, handle, feeds, fetches)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1333 except KeyError:
1334 pass
-> 1335 raise type(e)(node_def, op, message)
1336
1337 def _extend_graph(self):
UnimplementedError: Cast string to float is not supported
[[Node: linear/head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _class=["loc:@linea...t/Switch_1"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](linear/head/labels/ExpandDims, ^linear/head/labels/assert_equal/Assert/Assert)]]
Caused by op 'linear/head/ToFloat', defined at:
File "C:\ProgramData\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\ProgramData\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\ProgramData\Anaconda3\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 486, in start
self.io_loop.start()
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 127, in start
self.asyncio_loop.run_forever()
File "C:\ProgramData\Anaconda3\lib\asyncio\base_events.py", line 422, in run_forever
self._run_once()
File "C:\ProgramData\Anaconda3\lib\asyncio\base_events.py", line 1432, in _run_once
handle._run()
File "C:\ProgramData\Anaconda3\lib\asyncio\events.py", line 145, in _run
self._callback(*self._args)
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 117, in _handle_events
handler_func(fileobj, events)
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
return fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 450, in _handle_events
self._handle_recv()
File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 480, in _handle_recv
self._run_callback(callback, msg)
File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 432, in _run_callback
callback(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
return fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2662, in run_cell
raw_cell, store_history, silent, shell_futures)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2785, in _run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in run_ast_nodes
if self.run_code(code, result):
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-229-7ea5d3d759fb>", line 1, in <module>
train_and_evaluate(OUTDIR, num_train_steps=5)
File "<ipython-input-227-891dd877d57e>", line 28, in train_and_evaluate
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 447, in train_and_evaluate
return executor.run()
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 531, in run
return self.run_local()
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 669, in run_local
hooks=train_hooks)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 366, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1119, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1132, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1107, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\canned\linear.py", line 311, in _model_fn
config=config)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\canned\linear.py", line 164, in _linear_model_fn
logits=logits)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\canned\head.py", line 239, in create_estimator_spec
regularization_losses))
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\canned\head.py", line 1208, in _create_tpu_estimator_spec
features=features, mode=mode, logits=logits, labels=labels))
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\estimator\canned\head.py", line 1114, in create_loss
labels = math_ops.to_float(labels)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py", line 719, in to_float
return cast(x, dtypes.float32, name=name)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py", line 665, in cast
x = gen_math_ops.cast(x, base_type, name=name)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 1613, in cast
"Cast", x=x, DstT=DstT, name=name)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3414, in create_op
op_def=op_def)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1740, 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: linear/head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _class=["loc:@linea...t/Switch_1"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](linear/head/labels/ExpandDims, ^linear/head/labels/assert_equal/Assert/Assert)]]
Tensorflow代码：
# Import libraries
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
import shutil
# Read data
df = pd.read_csv('sample.csv')
# Separate label from dataset
X = df.drop(['label'], axis=1).values
y = df[['label']].values
# Split into train and test dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Convert to dataframe
X_train = pd.DataFrame(X_train)
X_test = pd.DataFrame(X_test)
y_train = pd.DataFrame(y_train)
y_test = pd.DataFrame(y_test)
# Concatenate for writing into csv
train = pd.concat([X_train, y_train], axis=1)
valid = pd.concat([X_valid, y_valid], axis=1)
# Write into csv file
train.to_csv('train.csv', header=False, index=False)
valid.to_csv('valid.csv', header=False, index=False)
# Specify structure for tensorflow input
CSV_COLUMNS = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8']
LABEL_COLUMN = 'label'
DEFAULTS = [['none'], ['none'], ['none'], ['none'], ['none'], ['0'], [0], [0]]
# Function for reading input file and creating dataset
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
dataset = tf.data.TextLineDataset(file_list).map(decode_csv)
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
# Input feature columns
INPUT_COLUMNS = [
tf.feature_column.categorical_column_with_vocabulary_list('col1', vocabulary_list=['1', '2', '3', '4']),
tf.feature_column.categorical_column_with_vocabulary_list('col2', vocabulary_list = [ '1', '2', '3', '4', '5', '6']),
tf.feature_column.categorical_column_with_vocabulary_list('col3', vocabulary_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9']),
tf.feature_column.categorical_column_with_vocabulary_list('col4', vocabulary_list = [ '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']),
tf.feature_column.categorical_column_with_vocabulary_list('col5', vocabulary_list = [ '0', '1', '2', '3', '4', '5']),
tf.feature_column.categorical_column_with_vocabulary_list('col6', vocabulary_list=['0', '1']),
tf.feature_column.numeric_column('col7'),
tf.feature_column.numeric_column('col8')
]
def add_more_features(feats):
# for future reference
return(feats)
feature_cols = add_more_features(INPUT_COLUMNS)
# Serving function
def serving_input_fn():
feature_placeholders = {
'col1': tf.placeholder(tf.string, [None]),
'col2': tf.placeholder(tf.string, [None]),
'col3': tf.placeholder(tf.string, [None]),
'col4': tf.placeholder(tf.string, [None]),
'col5': tf.placeholder(tf.string, [None]),
'col6': tf.placeholder(tf.string, [None]),
'col7': tf.placeholder(tf.int64, [None]),
'col8': tf.placeholder(tf.int64, [None])
}
features = {
key: tf.expand_dims(tensor, -1)
for key, tensor in feature_placeholders.items()
}
return tf.estimator.export.ServingInputReceiver(features, feature_placeholders)
# Train and evaluate function
def train_and_evaluate(output_dir, num_train_steps):
estimator = tf.estimator.LinearClassifier(
model_dir=output_dir,
feature_columns=feature_cols)
train_spec = tf.estimator.TrainSpec(
input_fn = read_dataset('train.csv', mode = tf.estimator.ModeKeys.TRAIN),
max_steps=num_train_steps)
exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)
eval_spec = tf.estimator.EvalSpec(
input_fn = read_dataset('valid.csv', mode = tf.estimator.ModeKeys.EVAL),
steps = None,
start_delay_secs = 1,
throttle_secs = 10,
exporters = exporter)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
# Log level and cleanup
tf.logging.set_verbosity(tf.logging.INFO)
OUTDIR = 'sample_dir'
shutil.rmtree(OUTDIR, ignore_errors=True)
# Run training and evaluation
train_and_evaluate(OUTDIR, num_train_steps=1)
我一直在努力克服这个错误。非常感谢您的帮助。
共有1个答案
东方镜
调试此问题时，问题已得到解决，但我不确定实际解决问题的步骤是什么。
我在调试这个问题时尝试了以下事情：
- 参考stackoverflow线程：float64和pandas到_csv，将写入csv文件的浮动类型格式更改如下：
先前代码：
train.to_csv('train.csv', header=False, index=False)
valid.to_csv('valid.csv', header=False, index=False)
修改代码：
train.to_csv('train.csv', header=False, index=False, float_format='%.4f')
valid.to_csv('valid.csv', header=False, index=False, float_format='%.4f')
现在模型已经启动并运行。
类似资料：
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我正在谷歌 colab 环境中的 tensorflow 中训练一个 DNN，代码直到昨天都运行良好，但现在当我运行代码的估计器训练部分时，它给出了一个错误。 我不知道到底是什么原因，谷歌colab是否使用任何更新版本的tensorflow，其中某些函数与旧版本不兼容？因为我之前的代码没有问题，我没有改变它。似乎其他代码也存在此问题，例如，斯坦福大学的此示例代码之前运行没有任何错误，https://
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（）中的ValueError回溯（最近一次调用）---- ~\Anaconda3\lib\site packages\sklearn\preprocessing\data.py in fit_transform（self，X，y）2017”““2018年返回所选的fit_transform（X，self.\u fit_transform- ~\Anaconda3\lib\site-包\skLear
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问题内容： 我有一个使用转换的字符串，现在我正尝试将其转换回来，但是当UIPicker启动时，这给了我错误的一天 我尝试了硬编码，但结果仍然相同。UIDatePicker上的日期在1991年12月22日开始。 如果我使用hardcore ，则从1979年12月23日开始。 （我不知道是不是这种情况，但是我在UIPickerView中有，但它是为字符串使用的。。我不认为是因为保存时，它保存了正确的值
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我将Camel与Spring Boot一起使用。在基本上记录消息正文的服务路由实现过程中，我看到了如下所示的错误。 我的路线是： 我在日志正文行中出现错误。 我的问题是预期的行为？为什么Camel不只是调用对象的方法。如果这是预期的行为，那么我需要一个字符串转换器来处理每个新的复杂类型？
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问题内容： 显示值时出现错误： 在php中，来自数据库（$ thedate）的值为“ 2015-05-05 21：52：31.000” 我如何格式化它以便能够将它作为字符串显示在php页面上？当前，它显示错误“类DateTime的对象无法转换为字符串”。 问题答案： 您有一个对象，因此必须使用它来格式化输出，例如