Python 3.x 获得；Tensorflow%s不是有效的作用域名称错误；当我试图为kaggle竞赛创建一个模型时

这是我的完整代码和回溯。这是我的ml模型的起始代码。将会有很多的补充

import pandas as pd
import tensorflow as tf
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import metrics
from IPython import display
from tensorflow.python.data import Dataset
tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 5
pd.options.display.float_format = '{:.1f}'.format

housing_data = pd.read_csv("train.csv")
housing_data = housing_data.reindex(
np.random.permutation(housing_data.index))
housing_data = pd.get_dummies(
housing_data).dropna()

对特征进行预处理。这里还有更多的工作要做

def preprocess_features(housing_data):
    selected_features = housing_data
    selected_features = selected_features.drop(columns = "SalePrice")
    processed_features = selected_features.copy()
    return processed_features

def preprocess_target(housing_data):
    output_target = pd.DataFrame()
    output_target["SalePrice"] = (housing_data.SalePrice / 1000.0)
    return output_target

training_examples = preprocess_features(housing_data.head(900))
training_targets = preprocess_target(housing_data.head(900))

validation_examples = preprocess_features(housing_data.tail(221))
validation_targets = preprocess_target(housing_data.tail(221))

def construct_feature_columns(input_features):
    '''
    Returns the set of feature columns for tf.estimator classifiers and regressors
    '''
    return set([tf.feature_column.numeric_column(my_feature) for my_feature in input_features])


def my_input_fn(features, targets, batch_size = 1, shuffle = True, num_epochs = None):
    #convert the pandas dataframe into a numpy array

    features = {key:np.array(value) for key,value in dict(features).items()}

    #create the dataset
    ds = Dataset.from_tensor_slices((features,targets))
    ds = ds.batch(batch_size).repeat(num_epochs)

    #shuffle the data
    if shuffle:
        ds = ds.shuffle(1000)

    #return the features and targets tuple for next iteration
    features,labels= 
ds.make_one_shot_iterator().get_next()
    return features,labels

线性分类器

def train_linear_classifier_model(
    learning_rate,
    regularization_strength,
    steps,
    batch_size,
    training_examples,
    training_targets,
    validation_examples,
    validation_targets
):
    periods = 10
    steps_per_period = steps / periods

    my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)
    my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
    linear_classifier = tf.estimator.LinearClassifier(
      feature_columns=construct_feature_columns(training_examples),
      optimizer=my_optimizer
  )


    training_input_fn = lambda: my_input_fn(training_examples, 
                                          training_targets["SalePrice"], 
                                          batch_size=batch_size)
    predict_training_input_fn = lambda: my_input_fn(training_examples, 
                                                  training_targets["SalePrice"], 
                                                  num_epochs=1, 
                                                  shuffle=False)
    predict_validation_input_fn = lambda: my_input_fn(validation_examples, 
                                                    validation_targets["SalePrice"], 
                                                    num_epochs=1, 
                                                    shuffle=False)


    print("Training model...")
    print("LogLoss (on validation data):")
    training_log_losses = []
    validation_log_losses = []
    for period in range (0, periods):
        linear_classifier.train(
        input_fn=training_input_fn,
        steps=steps_per_period
    )
    # Take a break and compute predictions.
    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)
    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])

    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)
    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])

    # Compute training and validation loss.
    training_log_loss = metrics.log_loss(training_targets, training_probabilities)
    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)
    # Occasionally print the current loss.
    print("  period %02d : %0.2f" % (period, validation_log_loss))
    # Add the loss metrics from this period to our list.
    training_log_losses.append(training_log_loss)
    validation_log_losses.append(validation_log_loss)
    print("Model training finished.")

  # Output a graph of loss metrics over periods.
    plt.ylabel("LogLoss")
    plt.xlabel("Periods")
    plt.title("LogLoss vs. Periods")
    plt.tight_layout()
    plt.plot(training_log_losses, label="training")
    plt.plot(validation_log_losses, label="validation")
    plt.legend()

    return linear_classifier

linear_classifier = train_linear_classifier_model(
    learning_rate=0.1,
    regularization_strength=0.1,
    steps=300,
    batch_size=100,
    training_examples=training_examples,
    training_targets=training_targets,
    validation_examples=validation_examples,
    validation_targets = validation_targets)

这是我的回溯

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-75-f9d203769761> in <module>()
      7     training_targets=training_targets,
      8     validation_examples=validation_examples,
----> 9     validation_targets = validation_targets)

<ipython-input-74-e1dbd56d9615> in train_linear_classifier_model(learning_rate, regularization_strength, steps, batch_size, training_examples, training_targets, validation_examples, validation_targets)
     40         linear_classifier.train(
     41         input_fn=training_input_fn,
---> 42         steps=steps_per_period
     43     )
     44     # Take a break and compute predictions.

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    350 
    351     saving_listeners = _check_listeners_type(saving_listeners)
--> 352     loss = self._train_model(input_fn, hooks, saving_listeners)
    353     logging.info('Loss for final step: %s.', loss)
    354     return self

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
    810       worker_hooks.extend(input_hooks)
    811       estimator_spec = self._call_model_fn(
--> 812           features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
    813 
    814       if self._warm_start_settings:

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\estimator\estimator.py in _call_model_fn(self, features, labels, mode, config)
    791 
    792     logging.info('Calling model_fn.')
--> 793     model_fn_results = self._model_fn(features=features, **kwargs)
    794     logging.info('Done calling model_fn.')
    795 

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\estimator\canned\linear.py in _model_fn(features, labels, mode, config)
    314           optimizer=optimizer,
    315           partitioner=partitioner,
--> 316           config=config)
    317 
    318     super(LinearClassifier, self).__init__(

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\estimator\canned\linear.py in _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, partitioner, config)
    155     logit_fn = _linear_logit_fn_builder(
    156         units=head.logits_dimension, feature_columns=feature_columns)
--> 157     logits = logit_fn(features=features)
    158 
    159     def _train_op_fn(loss):

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\estimator\canned\linear.py in linear_logit_fn(features)
     96         feature_columns=feature_columns,
     97         units=units,
---> 98         cols_to_vars=cols_to_vars)
     99     bias = cols_to_vars.pop('bias')
    100     if units > 1:

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\feature_column\feature_column.py in linear_model(features, feature_columns, units, sparse_combiner, weight_collections, trainable, cols_to_vars)
    422     for column in sorted(feature_columns, key=lambda x: x.name):
    423       with variable_scope.variable_scope(
--> 424           None, default_name=column._var_scope_name):  # pylint: disable=protected-access
    425         ordered_columns.append(column)
    426         weighted_sum = _create_weighted_sum(

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in __enter__(self)
   1901 
   1902     try:
-> 1903       return self._enter_scope_uncached()
   1904     except:
   1905       if self._graph_context_manager is not None:

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\ops\variable_scope.py in _enter_scope_uncached(self)
   2001           self._default_name)
   2002       try:
-> 2003         current_name_scope_name = current_name_scope.__enter__()
   2004       except:
   2005         current_name_scope.__exit__(*sys.exc_info())

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\framework\ops.py in __enter__(self)
   5619       try:
   5620         self._name_scope = g.name_scope(self._name)
-> 5621         return self._name_scope.__enter__()
   5622       except:
   5623         self._g_manager.__exit__(*sys.exc_info())

c:\users\user\appdata\local\programs\python\python35\lib\contextlib.py in __enter__(self)
     57     def __enter__(self):
     58         try:
---> 59             return next(self.gen)
     60         except StopIteration:
     61             raise RuntimeError("generator didn't yield") from None

c:\users\user\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\framework\ops.py in name_scope(self, name)
   3942         # (viz. '-', '\', '/', and '_').
   3943         if not _VALID_SCOPE_NAME_REGEX.match(name):
-> 3944           raise ValueError("'%s' is not a valid scope name" % name)
   3945       else:
   3946         # Scopes created in the root must match the more restrictive

ValueError: 'Exterior1st_Wd Sdng' is not a valid scope name

---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在（）
7培训目标=培训目标，
8个验证示例=验证示例，
---->9验证目标=验证目标）
在训练线性分类器模型中（学习率、正则化强度、步骤、批量大小、训练示例、训练目标、验证示例、验证目标）
40线性_系列(
41输入\u fn=培训\u输入\u fn，
--->42步=每个周期的步数
43     )
44#休息一下，计算预测。
c:\users\user\appdata\local\programs\python\35\lib\site packages\tensorflow\python\estimator\estimator.py in-train（self、input\fn、hook、steps、max\u steps、saving\u监听器）
350
351保存侦听器=\u检查侦听器\u类型（保存侦听器）
-->352损失=自我训练模型（输入、挂钩、保存侦听器）
353 logging.info（'最后一步丢失：%s'，丢失）
354回归自我
c:\users\user\appdata\local\programs\python\35\lib\site packages\tensorflow\python\estimator\estimator.py in\u train\u模型（self、input\fn、hook、saving\u监听器）
810工人挂钩。扩展（输入挂钩）
811估计器\u规格=自身。\u调用\u模型\u fn(
-->812特性、标签、型号（fn_lib.ModeKeys.TRAIN、self.config）
813
814如果自热启动设置：
c:\users\user\appdata\local\programs\python\35\lib\site packages\tensorflow\python\estimator\estimator.py in\u call\u model\u fn（自身、功能、标签、模式、配置）
791
792 logging.info（'Calling model_fn'））
-->793模型结果=自我。\模型（特征=特征，**kwargs）
794 logging.info（'Done calling model_fn'））
795
c:\users\user\appdata\local\programs\python35\lib\site packages\tensorflow\python\estimator\canted\linear.py in\u model\u fn（功能、标签、模式、配置）
314优化器=优化器，
315分割器=分割器，
-->316配置=配置）
317
318超级（线性分类器，自）__(
c:\users\user\appdata\local\programs\python\35\lib\site packages\tensorflow\python\estimator\canted\linear.py in\u linear\u model\u fn（特性、标签、模式、标题、特性列、优化器、分区器、配置）
155逻辑函数=线性逻辑函数(
156个单位=head.logits\u尺寸，特征列=特征列）
-->157 logits=logit\u fn（特征=特征）
158
159 def列操作fn（损失）：
c:\users\user\appdata\local\programs\python35\lib\site packages\tensorflow\python\estimator\canted\linear.py in linear\u logit\u fn（功能）
96个要素列=要素列，
97单位=单位，
--->98列至列=列至列）
99 bias=cols_to_vars.pop（'bias'））
100如果单位>1：
线性模型中的c:\users\user\appdata\local\programs\python\35\lib\site packages\tensorflow\python\feature\u column\feature\u column.py（特征、特征列、单位、稀疏组合器、权重集合、可训练、cols\u到变量）
422对于已排序的列（特征_列，key=lambda x:x.name）：
423带变量范围。变量范围(
-->424无，默认值\名称=列。\变量\范围\名称）：\ pylint:disable=受保护的访问
425有序列。追加（列）
426加权和=\u创建\u加权和(
c:\users\user\appdata\local\programs\python35\lib\site packages\tensorflow\python\ops\variable\u scope.py in\uuuuuuu enter\uuuuuu（self）
1901
1902尝试：
->1903返回自我。\输入\范围\未缓存（）
1904年除外：
1905如果self.\u graph\u context\u manager不是None：
c:\users\user\appdata\local\programs\python35\lib\site packages\tensorflow\python\ops\variable\u scope.py in\u enter\u scope\u uncached（self）
2001年自我（默认名称）
2002年尝试：
->2003当前\名称\范围\名称=当前\名称\范围。\输入\范围（）
2004年除外：
2005当前\u名称\u范围。\u退出\u（*sys.exc\u info（））
c:\users\user\appdata\local\programs\python35\lib\site packages\tensorflow\python\framework\ops.py in\uuuuuuuu enter\uuuuuu（self）
5619请尝试：
5620 self.\u name\u scope=g.name\u scope（self.\u name）
->5621返回self.\u name\u scope.\u输入
5622除了：
5623自我管理器退出（*sys.exc\u info（））
c:\users\user\appdata\local\programs\python35\lib\contextlib.py in\uuuuuu enter\uuuuu（self）
57定义输入（自我）：
58尝试：
--->59返回下一个（self.gen）
60除停止迭代外：
61从“无”引发运行时错误（“生成器未生成”）
c:\users\user\appdata\local\programs\python35\lib\site packages\tensorflow\python\framework\ops.py在name\u范围内（self，name）
3942#（即'-'、'\'、'/'和''.'）。
3943如果无效\u范围\u名称\u正则表达式匹配（名称）：
->3944提升值错误（“%s”不是有效的作用域名称“%name”）
3945其他：
3946#在根目录中创建的作用域必须与限制性更强的
ValueError:“Exterior1st_Wd Sdng”不是有效的作用域名称

我无法理解术语“Exterior1st_Wd Sdng”的含义，因为我没有任何这样命名的变量。

---

提前谢谢

我不确定这是否与您的错误有关，但我从未见过功能列作为一个集合，通常它们是一个列表。 我认为

construct\u feature\u columns

函数应该只

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