作为学校作业的分配，我们必须能够预测旅行者类型的最佳旅行。我正在跟随一个tensorFlow tutorial。经过几个小时的工作，我终于走到了死胡同。以下是寻找答案： Python TensorFlow Cast string to float is not.... python-COnverting from pandas to Tensorflow..
由于出现了其他错误，我已经以多种方式重写了代码。这是最新的迭代。
我首先打开csv文件进行清理并格式化。我们不需要一些列。如果有一种方法只能模拟和训练数据而不必手动删除不需要的列，我愿意接受建议。删除我不需要的东西后，我将没有标题和索引的数据文件保存到另一个文件，并在input_fn中重新打开。我的print语句用于调试。它们允许我查看函数的内容或内容，从而让我知道类型是否正确。
这是什么让我挂断电话。根据打印，我的所有列都是正确的数据类型。它们与我的功能列匹配。然后我在这一天的大部分时间都在这里，所以我可能会失明。
根据我尝试运行此输出和我列出的其中一个SO帖子的输出，我输入的输入列之一的类型不正确。但我无法弄清楚哪一个。这是我们对tensorflow的第一次尝试，所以我无法自己解决这个问题。
以下是代码。
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)]]
忘了添加样本数据
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