我正在学习Tensorflow。以下是我的代码。代码使用一些特征构建了一个线性回归模型，并尝试预测MPG（燃料消耗）。
代码的第一部分（数据集准备）为训练准备数据集。第二部分（开始Tensorflow）尝试构建和训练一个线性回归模型。
我遇到的问题是当我调用线性回归模型的训练函数时，抛出了一个错误…
我不知道如何修复这个错误。我也不知道为什么“不可哈希的列表”会影响训练。
请提供一些见解。谢谢。
from __future__ import absolute_import, division, print_functionimport pathlibimport pandas as pdimport seaborn as snsimport tensorflow as tffrom tensorflow import keras# tf.enable_eager_execution() # turn eager model on; this should only be called ONCE!print(tf.__version__)#-----------------------------------------------------## Dataset preparation# read dataset and previewdataset_path = keras.utils.get_file("auto-mpg.data", "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight', 'Acceleration', 'Model Year', 'Origin'] raw_dataset = pd.read_csv(dataset_path, names=column_names, na_values = "?", comment='\t', sep=" ", skipinitialspace=True)dataset = raw_dataset.copy()# erase NaN rowsdataset = dataset.dropna()# Origin column is not magnitude meaningful, don't use this as feature!origin = dataset.pop('Origin')# Separate train & test datasetdataset_train = dataset.sample(frac=0.8, random_state = 0)dataset_test = dataset.drop(dataset_train.index)#-----------------------------------------------------## Begin Tensorflow # build input fndef train_input_fn(df, label_name): """ Argus: df: pandas dataframe label_name: name of label column return: A function: <function tensorflow.python.estimator.inputs.pandas_io.input_fn> """ return tf.estimator.inputs.pandas_input_fn( x = df, y = df[label_name], batch_size = 32, num_epochs = 5, shuffle = True, queue_capacity = 1000, num_threads = 1 )# define modelfeature_names = ['Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year'] feature_cols_tensor = [tf.feature_column.numeric_column(feature_names)] # turn the string list into tensor objectlinear_regressor = tf.estimator.LinearRegressor(feature_columns = feature_cols_tensor)linear_regressor.train( train_input_fn(dataset_train, 'MPG'), steps = 100)以下是错误信息
INFO:tensorflow:Calling model_fn.TypeErrorTraceback (most recent call last)<ipython-input-14-c1814cca00b6> in <module>()----> 1 linear_regressor.train(train_input_fn(dataset_train_norm, 'MPG'), steps = 100)/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) 361 362 saving_listeners = _check_listeners_type(saving_listeners)--> 363 loss = self._train_model(input_fn, hooks, saving_listeners) 364 logging.info('Loss for final step: %s.', loss) 365 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) 841 return self._train_model_distributed(input_fn, hooks, saving_listeners) 842 else:--> 843 return self._train_model_default(input_fn, hooks, saving_listeners) 844 845 def _train_model_default(self, input_fn, hooks, saving_listeners):/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model_default(self, input_fn, hooks, saving_listeners) 854 worker_hooks.extend(input_hooks) 855 estimator_spec = self._call_model_fn(--> 856 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config) 857 return self._train_with_estimator_spec(estimator_spec, worker_hooks, 858 hooks, global_step_tensor,/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _call_model_fn(self, features, labels, mode, config) 829 830 logging.info('Calling model_fn.')--> 831 model_fn_results = self._model_fn(features=features, **kwargs) 832 logging.info('Done calling model_fn.') 833 /usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/linear.pyc in _model_fn(features, labels, mode, config) 430 optimizer=optimizer, 431 partitioner=partitioner,--> 432 config=config) 433 434 super(LinearRegressor, self).__init__(/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/linear.pyc 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 return head.create_estimator_spec(/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/canned/linear.pyc 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:/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/feature_column/feature_column.pyc in linear_model(features, feature_columns, units, sparse_combiner, weight_collections, trainable, cols_to_vars) 409 nor `_CategoricalColumn`. 410 """--> 411 feature_columns = _clean_feature_columns(feature_columns) 412 for column in feature_columns: 413 if not isinstance(column, (_DenseColumn, _CategoricalColumn)):/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/feature_column/feature_column.pyc in _clean_feature_columns(feature_columns) 2231 name_to_column = dict() 2232 for column in feature_columns:-> 2233 if column.name in name_to_column: 2234 raise ValueError('Duplicate feature column name found for columns: {} ' 2235 'and {}. This usually means that these columns refer to 'TypeError: unhashable type: 'list'回答：
问题在于你向Tensorflow特征列函数提供了列表，但它需要一个单一的字符串来表示列名。
将以下代码行替换为：
feature_cols_tensor = [tf.feature_column.numeric_column(feature_names)]替换为：
feature_cols_tensor = list(map(tf.feature_column.numeric_column, feature_names))你的列名Model Year似乎是个拼写错误：在你的原始数据中，我只找到了没有空格的ModelYear。
