我正在学习Tensorflow。以下是我的代码。该代码正在使用某些功能构建线性回归模型，并试图预测MPG（燃料费用）。
代码的第一部分（数据集准备）为训练准备了数据集。第二部分（开始Tensorflow）尝试构建和训练线性回归器。
我遇到的问题是当我在线性回归器上调用train函数时，抛出了一个错误...
我不知道如何解决此错误。而且我不知道为什么“无法散列表”会影响培训。
请对此提供一些见解。谢谢。
from __future__ import absolute_import, division, print_function
import pathlib
import pandas as pd
import seaborn as sns
import tensorflow as tf
from 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 preview
dataset_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 rows
dataset = dataset.dropna()
# Origin column is not magnitude meaningful, don't use this as feature!
origin = dataset.pop('Origin')
# Separate train & test dataset
dataset_train = dataset.sample(frac=0.8, random_state = 0)
dataset_test = dataset.drop(dataset_train.index)
#-----------------------------------------------------
## Begin Tensorflow
# build input fn
def 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 model
feature_names = ['Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year']
feature_cols_tensor = [tf.feature_column.numeric_column(feature_names)] # turn the string list into tensor object
linear_regressor = tf.estimator.LinearRegressor(feature_columns = feature_cols_tensor)
linear_regressor.train(
train_input_fn(dataset_train, 'MPG'),
steps = 100
)
以下是错误消息
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'
答案 0 :(得分：1)
问题是您为张量流特征列函数提供了一个列表，但是它使用单个字符串表示列的名称。
替换行：
feature_cols_tensor = [tf.feature_column.numeric_column(feature_names)]
使用：
feature_cols_tensor = list(map(tf.feature_column.numeric_column, feature_names))
您的列Model Year似乎是一个错字：我只发现ModelYear的原始数据中没有空格。