AttributeError: 模块“tensorflow_estimator.python.estimator.api._v1.estimator”没有属性“inpus”

如何解决AttributeError: 模块“tensorflow_estimator.python.estimator.api._v1.estimator”没有属性“inpus”

我正在尝试使用线性分类器进行预测，这里列出了估计器的构建和训练：

model = tf.estimator.LinearClassifier(
  n_classes = 2,model_dir = "ongoing",feature_columns = categorical_features + continuous_features
(
FEATURES = ['Age','Gender','ICD9Code']
LABEL = 'Condition'

def get_input_fn(data_set,num_epochs,n_batch,shuffle):
    input = tf.compat.v1.estimator.inputs.pandas_input_fn(
       x = pd.DataFrame({k: data_set[k].values for k in FEATURES}),y = pd.Series(data_set[LABEL].values),batch_size = n_batch,num_epochs = num_epochs,shuffle = shuffle
     )
     return input
model.train(
  input_fn = get_input_fn(csv_data,num_epochs = None,n_batch = 10461,shuffle = False
  ),steps = 1000
)
predict_data = pd.read_csv('feature_condition.csv',usecols = ['PatientGuid','Age','ICD9Code'],nrows = 5)
predict_input_fn = tf.estimator.inpus.numpy_input_fn(
                      x = {"x": predict_data},y = None,batch_size = 5,shuffle = False,num_threads = 5
                   )
predict_results = model.predict(predict_input_fn)
print(predict_results)

出现错误：

AttributeError: module 'tensorflow_estimator.python.estimator.api._v1.estimator' has no attribute 'inpus'

我的 tensorflow 版本是 2.4.1

你能帮我解决这个问题吗？谢谢！

更新：我已经更正了拼写错误，错误也已修复，但我在此处列出了一个警告：

The name tf.estimator.inputs.numpy_input_fn is deprecated. Please use tf.compat.v1.estimator.inputs.numpy_input_fn instead.

在我使用建议的函数后，我在这里列出了相同的警告：

The name tf.estimator.inputs.numpy_input_fn is deprecated. Please use tf.compat.v1.estimator.inputs.numpy_input_fn instead

这真的让我很困惑，你能帮忙解决吗？谢谢！

我在谷歌驱动器中上传了我的完整代码，这是这里的链接： https://drive.google.com/file/d/1R6bRcv8Afjx4cPLBZaBpuCcDg71fNN3Y/view?usp=sharing

解决方法

如果您可以将 tf.estimator.inpus.numpy_input_fn 更改为 tf.estimator.inputs.numpy_input_fn，您的问题可以得到解决。这是打字错误。

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import json
import os
import numpy as np
import pandas as pd
from pandas.core.frame import DataFrame
from tensorflow.train import SequenceExample,FeatureLists
from tensorflow import feature_column
from tensorflow.keras import layers


csv_file = 'feature_condition.csv'
csv_data = pd.read_csv(csv_file,low_memory = False)
csv_df = pd.DataFrame(csv_data)

test_file = 'test.csv'
test_data = pd.read_csv(test_file,low_memory = False)
test_df = pd.DataFrame(test_data)



CONTI_FEATURES = ['Age']
CATE_FEATURES = ['Gender','ICD9Code']

# create the feature column:
continuous_features = [tf.feature_column.numeric_column(k) for k in CONTI_FEATURES]

categorical_features = [tf.feature_column.categorical_column_with_hash_bucket(k,hash_bucket_size = 1000) for k in CATE_FEATURES]


model = tf.estimator.LinearClassifier(
  n_classes = 2,model_dir = "ongoing",feature_columns = categorical_features + continuous_features
)

FEATURES = ['Age','Gender','ICD9Code']
LABEL = 'Condition'

# input function:
def get_input_fn(data_set,num_epochs,n_batch,shuffle):
    input = tf.compat.v1.estimator.inputs.pandas_input_fn(
       x = pd.DataFrame({k: data_set[k].values for k in FEATURES}),y = pd.Series(data_set[LABEL].values),batch_size = n_batch,num_epochs = num_epochs,shuffle = shuffle
     )
    return input
# train the model
model.train(
  input_fn = get_input_fn(csv_data,num_epochs = None,n_batch = 10461,shuffle = False
  ),steps = 1000
)

# iterate every data in test dataset and make a prediction:
row_pre = 0
for i in test_data.loc[:,'PatientGuid']:
    dict = {'Age': test_data.loc[row_pre]['Age'],'Gender': test_data.loc[row_pre]['Gender'],'ICD9Code': test_data.loc[row_pre]['ICD9Code'],}
    df = pd.DataFrame(dict,index = [1,2,3])
    predict_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
    #predict_input_fn = tf.estimator.inputs.numpy_input_fn(

                          x = {k: df[k].values for k in FEATURES},y = None,batch_size = 1,num_epochs = 1,shuffle = False,num_threads = 1
                       )
    predict_results = model.predict(predict_input_fn)
    row_pre += 1

您可以忽略已弃用的警告。