Python UnimplementedError:不支持将字符串强制转换为浮点[[节点：linear/head/ToFloat]

作为学校作业的任务，我们必须能够预测出旅行者类型的最佳旅行。我遵循的是tensorFlow。经过数小时的工作，我终于找到了一条死胡同。以下是答案：

由于出现了其他错误，我以几种方式重写了代码。这是最新的迭代

我首先打开csv文件以清理它并格式化它。有些列我们不需要。如果有一种方法可以只对数据进行建模和训练，而不必手动删除不需要的列，我会接受建议。在删除我不需要的内容后，我会将数据框保存到不同的文件中，不带标题和索引，然后在中重新打开我的print语句是用于调试的。它们允许我查看函数的输入和输出，从而让我知道类型是否正确

这就是让我挂断电话的原因。根据照片，我所有的列都是正确的数据类型。它们与我的功能列相匹配。然后我又一次这样做了一天的大部分时间，所以我可能会失明

根据我尝试运行此文件和我列出的某个SO帖子得到的输出，我输入了一个不正确类型的输入列。但我无法确定是哪一个。这是我们第一次尝试tensorflow，因此我无法自行解决这一问题

下面是代码

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

import pandas as pd
import numpy as np
import tensorflow as tf

_CSV_COLUMNS = ['Score', 'Period_of_stay',
            'Traveler_type', 'Pool',
            'Gym',
            'Tennis_court', 'Spa',
            'Casino',
            'Free_internet',
            'Hotel_name',
            'Hotel_stars',
            'Review_month']

_TRAIN_FILE = './cleanedUPTrain.csv'

_TEST_FILE = './cleanedUPTest.csv'


train = pd.read_csv('./vegas2.csv', index_col=False,
        usecols=_CSV_COLUMNS,
        dtype={'Score': np.float64, 'Period_of_stay':np.str,
            'Traveler_type':np.str, 'Pool':np.str,
            'Gym':np.str,
            'Tennis_court':np.str, 'Spa':np.str,
            'Casino':np.str,
            'Free_internet':np.str,
            'Hotel_name':np.str,
            'Review_month':np.str,'Hotel_stars': np.float64})

train.to_csv(_TRAIN_FILE, header=False, index=False)
print(train.dtypes)
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")

test = pd.read_csv('./vegas.csv', index_col=False,
        usecols=_CSV_COLUMNS,
        dtype={'Score': np.float64, 'Period_of_stay':np.str,
            'Traveler_type':np.str, 'Pool':np.str,
            'Gym':np.str,
            'Tennis_court':np.str, 'Spa':np.str,
            'Casino':np.str,
            'Free_internet':np.str,
            'Hotel_name':np.str,
            'Review_month':np.str,'Hotel_stars': np.float64})

test.to_csv(_TEST_FILE, header=False, index=False)
print(test.dtypes)
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")


score = tf.feature_column.numeric_column(
        'Score', [1, 2, 3, 4, 5]
        )

stay_period = tf.feature_column.categorical_column_with_vocabulary_list(
        'Period_of_stay', ['Dec-Feb', 'Mar-May', 'Jun-Aug', 'Sep-Nov']
        )
traveler_type = tf.feature_column.categorical_column_with_vocabulary_list(
        'Traveler_type', ['Friends', 'Business', 'Families', 'Couples', 'Solo'])

pool = tf.feature_column.categorical_column_with_vocabulary_list(
        'Pool', ['YES', 'NO'])

gym = tf.feature_column.categorical_column_with_vocabulary_list(
        'Gym', ['YES', 'NO'])

tennis_court = tf.feature_column.categorical_column_with_vocabulary_list(
        'Tennis_court', ['YES', 'NO'])

spa = tf.feature_column.categorical_column_with_vocabulary_list(
        'Spa', ['YES', 'NO'])

casino = tf.feature_column.categorical_column_with_vocabulary_list(
        'Casino', ['YES', 'NO'])

free_internet = tf.feature_column.categorical_column_with_vocabulary_list(
        'Free_internet', ['YES', 'NO'])

hotel_name = tf.feature_column.categorical_column_with_vocabulary_list(
        'Hotel_name',
        ['Circus Circus Hotel & Casino Las Vegas', 'Excalibur Hotel & Casino', 'Tuscany Las Vegas Suites & Casino', 'Hilton Grand Vacations at the Flamingo', 'Monte Carlo Resort&Casino', 'Treasure Island- TI Hotel & Casino', 'Tropicana Las Vegas - A Double Tree by Hilton Hotel', 'Paris Las Vegas', 'The Westin las Vegas Hotel Casino & Spa', 'Caesars Palace', 'The Cosmopolitan Las Vegas', 'The Palazzo Resort Hotel Casino', 'Wynn Las Vegas', 'Trump International Hotel Las Vegas', 'Encore at wynn Las Vegas', 'The Venetian Las Vegas Hotel', 'Bellagio Las Vegas',"Marriott's Grand Chateau", 'Wyndham Grand Desert', 'The Cromwell']
        )
hotel_stars = tf.feature_column.numeric_column(
        'Hotel_stars', [1, 2, 3, 4, 5])

review_month = tf.feature_column.categorical_column_with_vocabulary_list(
        'Review_month',
        ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
        )


base_columns = [score, stay_period, pool,
        gym, tennis_court, spa, casino,
        free_internet, hotel_name, hotel_stars, review_month]



def input_fn(data_file, num_of_epochs, shuffle, batch_size):
    assert tf.gfile.Exists(data_file), ('file not found!!!')

    def parse_csv(value):
        print('Parsing file')
        record_defaults = [[1], ['Dec-Feb'], ['Solo'], ['YES'], ['YES'], ['YES'], ['YES'],
                ['YES'], ['YES'], ['Circus Circus Hotel & Casino Las Vegas'], [1], ['January']]
        columns = tf.decode_csv(value, record_defaults=record_defaults)
        features = dict(zip(_CSV_COLUMNS, columns))
        labels = features.pop('Traveler_type')
        print(labels)
        print("###################################")
        print(columns)
        print("###################################")
        print(features)
        print("###################################")
        return features, labels

    dataset = tf.data.TextLineDataset(data_file)

    if shuffle:
        dataset = dataset.shuffle(shuffle)

    dataset = dataset.map(parse_csv, num_parallel_calls=5)

    dataset = dataset.repeat(num_of_epochs)
    dataset = dataset.batch(batch_size)
    return dataset




#CREATE THE MODEL
classifier = tf.estimator.LinearClassifier(
        model_dir = '~/lab3AI/',
        feature_columns=base_columns
        )

#train the model
classifier.train(input_fn=lambda:input_fn(_TRAIN_FILE,50, 50, 20), steps=10)

results = classifier.evaluate(input_fn=lambda: input_fn(_TEST_FILE, 1, False, 20))



inputs = tf.feature_column.input_layer(temp, base_columns)

var_init = tf.global_variables_initializer()

table_init = tf.tables_initializer()
sess = tf.Session()
sess.run((var_init,table_init))
print(sess.run(inputs))

错误日志

Score             float64
Period_of_stay     object
Traveler_type      object
Pool               object
Gym                object
Tennis_court       object
Spa                object
Casino             object
Free_internet      object
Hotel_name         object
Hotel_stars       float64
Review_month       object
dtype: object
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Score             float64
Period_of_stay     object
Traveler_type      object
Pool               object
Gym                object
Tennis_court       object
Spa                object
Casino             object
Free_internet      object
Hotel_name         object
Hotel_stars       float64
Review_month       object
dtype: object
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Parsing file
Tensor("DecodeCSV:2", shape=(), dtype=string) #labels
###################################columns
[<tf.Tensor 'DecodeCSV:0' shape=() dtype=int32>, <tf.Tensor 'DecodeCSV:1' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:2' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:3' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:4' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:5' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:6' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:7' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:8' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:9' shape=() dtype=string>, <tf.Tensor 'DecodeCSV:10' shape=() dtype=int32>, <tf.Tensor 'DecodeCSV:11' shape=() dtype=string>]
###################################features
{'Hotel_name': <tf.Tensor 'DecodeCSV:9' shape=() dtype=string>, 'Free_internet': <tf.Tensor 'DecodeCSV:8' shape=() dtype=string>, 'Casino': <tf.Tensor 'DecodeCSV:7' shape=() dtype=string>, 'Hotel_stars': <tf.Tensor 'DecodeCSV:10' shape=() dtype=int32>, 'Tennis_court': <tf.Tensor 'DecodeCSV:5' shape=() dtype=string>, 'Score': <tf.Tensor 'DecodeCSV:0' shape=() dtype=int32>, 'Period_of_stay': <tf.Tensor 'DecodeCSV:1' shape=() dtype=string>, 'Spa': <tf.Tensor 'DecodeCSV:6' shape=() dtype=string>, 'Pool': <tf.Tensor 'DecodeCSV:3' shape=() dtype=string>, 'Review_month': <tf.Tensor 'DecodeCSV:11' shape=() dtype=string>, 'Gym': <tf.Tensor 'DecodeCSV:4' shape=() dtype=string>}
###################################
2018-03-13 00:50:53.694401: W tensorflow/core/framework/op_kernel.cc:1179] OP_REQUIRES failed at cast_op.cc:77 : Unimplemented: Cast string to float is not supported
2018-03-13 00:50:53.694466: E tensorflow/core/common_runtime/executor.cc:645] Executor failed to create kernel. Unimplemented: Cast string to float is not supported
     [[Node: linear/head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](linear/head/labels)]]
Traceback (most recent call last):
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1361, in _do_call
    return fn(*args)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1340, in _run_fn
    target_list, status, run_metadata)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 516, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.UnimplementedError: Cast string to float is not supported
     [[Node: linear/head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](linear/head/labels)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "testingColumns.py", line 149, in <module>
    classifier.train(input_fn=lambda:input_fn(_TRAIN_FILE,50, 50, 20), steps=10)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 352, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 891, in _train_model
    _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py", line 546, in run
    run_metadata=run_metadata)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py", line 1022, in run
    run_metadata=run_metadata)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py", line 1113, in run
    raise six.reraise(*original_exc_info)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/six.py", line 693, in reraise
    raise value
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py", line 1098, in run
    return self._sess.run(*args, **kwargs)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py", line 1170, in run
    run_metadata=run_metadata)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py", line 950, in run
    return self._sess.run(*args, **kwargs)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 905, in run
    run_metadata_ptr)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1137, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1355, in _do_run
    options, run_metadata)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1374, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnimplementedError: Cast string to float is not supported
     [[Node: linear/head/ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/device:CPU:0"](linear/head/labels)]]

Caused by op 'linear/head/ToFloat', defined at:
  File "testingColumns.py", line 149, in <module>
    classifier.train(input_fn=lambda:input_fn(_TRAIN_FILE,50, 50, 20), steps=10)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 352, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 812, in _train_model
    features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 793, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/canned/linear.py", line 316, in _model_fn
    config=config)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/canned/linear.py", line 170, in _linear_model_fn
    logits=logits)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/canned/head.py", line 1100, in create_estimator_spec
    features=features, mode=mode, logits=logits, labels=labels))
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/estimator/canned/head.py", line 1010, in create_loss
    labels = math_ops.to_float(labels)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py", line 830, in to_float
    return cast(x, dtypes.float32, name=name)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py", line 779, in cast
    return gen_math_ops.cast(x, base_type, name=name)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py", line 911, in cast
    "Cast", x=x, DstT=DstT, name=name)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3271, in create_op
    op_def=op_def)
  File "/home/guak/tensorFlow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1650, 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, _device="/job:localhost/replica:0/task:0/device:CPU:0"](linear/head/labels)]]

---

你找到问题的答案了吗？就连我也遇到了同样的问题？

5.0,Dec-Feb,Friends,NO,YES,NO,NO,YES,YES,Circus Circus Hotel & Casino Las Vegas,3.0,January
3.0,Dec-Feb,Business,NO,YES,NO,NO,YES,YES,Circus Circus Hotel & Casino Las Vegas,3.0,January
4.0,Dec-Feb,Families,NO,YES,NO,NO,YES,YES,Circus Circus Hotel & Casino Las Vegas,3.0,December
2.0,Dec-Feb,Couples,NO,YES,NO,NO,YES,YES,Circus Circus Hotel & Casino Las Vegas,3.0,December
4.0,Dec-Feb,Couples,YES,YES,NO,YES,YES,YES,Excalibur Hotel & Casino,3.0,January
4.0,Dec-Feb,Business,YES,YES,NO,YES,YES,YES,Excalibur Hotel & Casino,3.0,January
5.0,Dec-Feb,Couples,YES,YES,NO,YES,YES,YES,Excalibur Hotel & Casino,3.0,February
3.0,Dec-Feb,Business,YES,YES,NO,YES,YES,YES,Excalibur Hotel & Casino,3.0,February