Python Tensorflow-'；无法以字节形式获取元素'；错误

以下代码：

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
import pandas as pd



# DATA PREPARE
df = pd.read_csv('housing.csv')
df = df.dropna()
print(df.head)
print(df.describe())
print(df.info())



# NORMALIZATION
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaler.fit(df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
               'median_house_value']])
df_scaled_cols = scaler.transform(df[['housing_median_age', 'total_rooms', 'total_bedrooms',
                                      'population', 'households', 'median_income', 'median_house_value']])
df_scaled_cols = pd.DataFrame(data=df_scaled_cols, columns=['housing_median_age', 'total_rooms', 'total_bedrooms',
                                                            'population', 'households', 'median_income',
                                                            'median_house_value'])

df = pd.concat([df_scaled_cols, df['ocean_proximity']], axis=1)



# DATAFRAME INTO X AND Y -> TRAIN TEST SPLIT
x_data = df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
             'ocean_proximity']]
y_label = df['median_house_value']

X_train, X_test, y_train, y_test = train_test_split(x_data, y_label, test_size=0.3)



# FEATURE COLUMNS FROM DATA

m_age = tf.feature_column.numeric_column('housing_median_age')
rooms = tf.feature_column.numeric_column('total_rooms')
bedrooms = tf.feature_column.numeric_column('total_bedrooms')
population = tf.feature_column.numeric_column('population')
households = tf.feature_column.numeric_column('households')
income = tf.feature_column.numeric_column('median_income')
ocean = tf.feature_column.categorical_column_with_hash_bucket('ocean_proximity', hash_bucket_size=10)
embedded_ocean = tf.feature_column.embedding_column(ocean, dimension=4)

feat_cols = [m_age, rooms, bedrooms, population, households, income, embedded_ocean]



# 3 INPUT FUNCTIONS

train_input_func = tf.estimator.inputs.pandas_input_fn(x=X_train, y=y_train, batch_size=10, num_epochs=1000,
                                                       shuffle=True)
test_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test, y=y_test, batch_size=10, num_epochs=1, shuffle=False)
predict_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test, batch_size=10, num_epochs=1, shuffle=False)



# DNN_Reg MODEL

dnn_model = tf.estimator.DNNRegressor(hidden_units=[10,10,10], feature_columns=feat_cols)
dnn_model.train(input_fn=train_input_func, steps=1000)

导致错误：

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
import pandas as pd



# DATA PREPARE
df = pd.read_csv('housing.csv')
df = df.dropna()
print(df.head)
print(df.describe())
print(df.info())



# NORMALIZATION
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaler.fit(df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
               'median_house_value']])
df_scaled_cols = scaler.transform(df[['housing_median_age', 'total_rooms', 'total_bedrooms',
                                      'population', 'households', 'median_income', 'median_house_value']])
df_scaled_cols = pd.DataFrame(data=df_scaled_cols, columns=['housing_median_age', 'total_rooms', 'total_bedrooms',
                                                            'population', 'households', 'median_income',
                                                            'median_house_value'])

df = pd.concat([df_scaled_cols, df['ocean_proximity']], axis=1)



# DATAFRAME INTO X AND Y -> TRAIN TEST SPLIT
x_data = df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
             'ocean_proximity']]
y_label = df['median_house_value']

X_train, X_test, y_train, y_test = train_test_split(x_data, y_label, test_size=0.3)



# FEATURE COLUMNS FROM DATA

m_age = tf.feature_column.numeric_column('housing_median_age')
rooms = tf.feature_column.numeric_column('total_rooms')
bedrooms = tf.feature_column.numeric_column('total_bedrooms')
population = tf.feature_column.numeric_column('population')
households = tf.feature_column.numeric_column('households')
income = tf.feature_column.numeric_column('median_income')
ocean = tf.feature_column.categorical_column_with_hash_bucket('ocean_proximity', hash_bucket_size=10)
embedded_ocean = tf.feature_column.embedding_column(ocean, dimension=4)

feat_cols = [m_age, rooms, bedrooms, population, households, income, embedded_ocean]



# 3 INPUT FUNCTIONS

train_input_func = tf.estimator.inputs.pandas_input_fn(x=X_train, y=y_train, batch_size=10, num_epochs=1000,
                                                       shuffle=True)
test_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test, y=y_test, batch_size=10, num_epochs=1, shuffle=False)
predict_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test, batch_size=10, num_epochs=1, shuffle=False)



# DNN_Reg MODEL

dnn_model = tf.estimator.DNNRegressor(hidden_units=[10,10,10], feature_columns=feat_cols)
dnn_model.train(input_fn=train_input_func, steps=1000)

回溯（最近一次调用上次）：文件 “C:\Users\Admin\AppData\Local\Programs\Python36\lib\site packages\tensorflow\Python\client\session.py”， 第1278行，打电话 返回fn（*args）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\client\session.py”， 第1263行，在运行中 选项、提要、获取列表、目标列表、运行元数据）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\client\session.py”， 第1350行，输入呼叫会话运行 运行\u元数据）tensorflow.python.framework.errors\u impl.InternalError:无法获取 元素作为字节

在处理上述异常期间，发生了另一个异常：

回溯（最近一次调用上次）：文件 “C:/Users/Admin/Documents/pycharm项目/TF_回归_项目/Project.py”， 第69行，在 dnn\u model.train（input\u fn=train\u input\u func，steps=1000）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\estimator\estimator.py”， 376号线，火车上 loss=self.\u train\u model（输入、钩子、保存侦听器）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\estimator\estimator.py”， 1145号线，列车模型 返回self.\u train\u model\u默认（输入\u fn、挂钩、保存\u侦听器）文件 “C:\Users\Admin\AppData\Local\Programs\Python\36\lib\site packages\tensorflow\Python\estimator\estimator.py”， 第1173行，列车模式默认 保存_侦听器）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\estimator\estimator.py”， 1451号线，带估算器的列车 _，loss=mon_sess.run（[estimator_spec.train_op，estimator_spec.loss]）文件 “C:\Users\Admin\AppData\Local\Programs\Python36\lib\site packages\tensorflow\Python\training\monitored\u session.py”， 第695行，在退出中 self.\u close\u内部（异常类型）文件“C:\Users\Admin\AppData\Local\Programs\Python36\lib\site packages\tensorflow\Python\training\monitored\u session.py”， 第732行，内部关闭 self.\u sess.close（）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\training\monitored\u session.py”， 第980行，关闭 self.\u sess.close（）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\training\monitored\u session.py”， 第1124行，关闭 忽略文件“C:\Users\Admin\AppData\Local\Programs\Python36\lib\site packages\tensorflow\Python\training\coordinator.py”， 第389行，在join中 重新播放（*self.\u exc\u info\u to\u raise）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\six.py”， 第692行，重新播放 提升价值。使用回溯（tb）文件“C:\Users\Admin\AppData\Local\Programs\Python36\lib\site packages\tensorflow\Python\estimator\inputs\queues\feed\u queue\u runner.py”， 第94行，正在运行 运行（enqueue_op，feed_dict=feed_dict）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\client\session.py”， 第877行，运行中 运行元数据（ptr）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\client\session.py”， 1100号线，正在运行 feed\u dict\u tensor，options，run\u metadata）文件“C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site packages\tensorflow\Python\client\session.py”， 第1272行，正在运行 运行元数据）文件“C:\Users\Admin\AppData\Local\Programs\Python36\lib\site packages\tensorflow\Python\client\session.py”， 第1291行，在通话中 提升类型（e）（节点定义、操作、消息）tensorflow.python.framework.errors\u impl.InternalError:无法获取 元素作为字节

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内部有什么问题？

问题在于规范化

我没有使用sklearn方法，而是执行了以下操作：

df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
    'median_house_value']] = df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
               'median_house_value']].apply(lambda x: (x-x.min()) / (x.max()-x.min()))

总之，我做了和sklearn一样的事情，但是是手动的——用lambda