Tensorflow DNNClassifier.fit錯誤：類型錯誤：「元組」對象不是可調用

我試圖分析數據集60列（FEATURES = 59列（整數和浮點數）的組合，LABEL =列名爲TARGET 0 /值爲1）Tensorflow DNNClassifier.fit錯誤：類型錯誤：「元組」對象不是可調用

我得到以下錯誤，而試圖擬合模型：

TypeError: 'tuple' object is not callable

以下是所使用的代碼：

from __future__ import absolute_import 
from __future__ import division 
from __future__ import print_function 

import os 
import urllib 

import itertools 

import pandas as pd 
import numpy as np 
import tensorflow as tf 

tf.logging.set_verbosity(tf.logging.INFO) 

COLUMNS = ["DEMAdmNo","target","PEV_30","PEV_365","lhlos","LTSD","LNOSD","comorbidity_index","AdmD_3","AdmD_7","AdmD_8","AdmD_9","AdmD_11","AdmD_13","AdmD_14","AdmD_15","AdmD_16","AdmD_18","AdmD_20","AdmD_21","AdmD_22","AdmD_23","AdmD_26","AdmD_27","AdmD_28","AdmD_30","AdmD_31","AdmD_32","AdmD_33","AdmD_36","DisP_1","DisP_2","DisP_4","DisP_5","DisP_6","DisP_7","DisP_11","DisP_12","DisP_13","DisP_14","DisP_16","Disc_8","Disc_10","Disc_11","Disc_12","Disc_14","Disc_15","Disc_17","Disc_21","Disc_22","Disc_23","Disc_24","Disc_25","Disc_26","Disc_27","Disc_28","Disc_29"] 

FEATURES = ["DEMAdmNo","PEV_30","PEV_365","lhlos","LTSD","LNOSD","comorbidity_index","AdmD_3","AdmD_7","AdmD_8","AdmD_9","AdmD_11","AdmD_13","AdmD_14","AdmD_15","AdmD_16","AdmD_18","AdmD_20","AdmD_21","AdmD_22","AdmD_23","AdmD_26","AdmD_27","AdmD_28","AdmD_30","AdmD_31","AdmD_32","AdmD_33","AdmD_36","DisP_1","DisP_2","DisP_4","DisP_5","DisP_6","DisP_7","DisP_11","DisP_12","DisP_13","DisP_14","DisP_16","Disc_8","Disc_10","Disc_11","Disc_12","Disc_14","Disc_15","Disc_17","Disc_21","Disc_22","Disc_23","Disc_24","Disc_25","Disc_26","Disc_27","Disc_28","Disc_29"] 

LABEL = "target" 

# Load datasets 
training_set = pd.read_csv("Performance_train_jun5.csv", skipinitialspace=True,skiprows=1, names=COLUMNS) 
test_set = pd.read_csv("Performance_test_jun5.csv", skipinitialspace=True,skiprows=1, names=COLUMNS) 

def my_input_fn(data_set): 
    feature_cols = {k: tf.constant(data_set[k].values) 
        for k in FEATURES} 
    labels = tf.constant(data_set[LABEL].values) 
    return feature_cols, labels 

classifier = 
    tf.contrib.learn.DNNClassifier(feature_columns=my_input_fn(training_set), 
               hidden_units=[10, 20, 10], 
               n_classes=2, 
               model_dir="/tmp/h_model") 

     INFO:tensorflow:Using default config. 
INFO:tensorflow:Using config: {'_evaluation_master': '', '_task_type': None, '_num_ps_replicas': 0, '_keep_checkpoint_every_n_hours': 10000, '_master': '', '_num_worker_replicas': 0, '_save_checkpoints_steps': None, '_model_dir': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000000000DA51908>, '_keep_checkpoint_max': 5, '_save_checkpoints_secs': 600, '_tf_config': gpu_options { 
    per_process_gpu_memory_fraction: 1 
} 
, '_tf_random_seed': None, '_task_id': 0, '_environment': 'local', '_save_summary_steps': 100, '_is_chief': True} 


classifier.fit(input_fn=my_input_fn(training_set), steps=2000) 

TypeError: 'tuple' object is not callable

關於上面的代碼，我有以下問題：

1）由於我的功能是int和float數據類型的混合，它們會在將它們轉換爲張量時引發問題嗎？

2）我的理解是，在classifier.fit的my_input_fn呼叫應閱讀功能和目標數據。我在這裏錯過了什麼嗎？第一

來源

2017-06-15 py_begin

第一件事，錯誤：TypeError: 'tuple' object is not callable它只是一個標準的Python錯誤，由於錯誤的訪問的元組。例如：

創建一個元組：my_tuple = ('elem1','elem2')

訪問它在右方式：
在[0]：my_tuple[0]
缺貨[0]：'elem1'

訪問它在一個壞方式：
[1]中：my_tuple(0)（注括號）
出[1]：TypeError: 'tuple' object is not callable

因此，該錯誤只告訴你，你要訪問到元組值與括號代替平方braquets是要走的路。

質詢

1）是的，這會導致一個問題，如果不能很好的預處理。你的問題可能會使int特性浮動？也許你可以嘗試這種方法。

2）我沒有檢查函數是否正確，但是，它應該這樣做。事情是函數返回一個元組，你必須訪問以正確的方式（以上暴露）

注的價值是：如果你想要更多的幫助，發佈您的CSV文件（「Performance_train_jun5。CSV「）的地方將有很大的幫助

來源

2017-06-15 08:27:53 Alber8295

我曾試圖將int特性強制浮動，它似乎工作。我將進一步研究預處理。感謝您的輸入！ –

根據Tensorflow的文檔，參數‘input_fn’必須接受一個函數對象（即input_fn = my_input_fn），而不是一個函數調用的返回值。這是你的TypeError與'fit'的原因。查看以下鏈接，瞭解如何使用帶有input_fn參數參數的函數。 https://www.tensorflow.org/get_started/input_fn

來源

2017-06-23 14:32:06

Tensorflow DNNClassifier.fit錯誤：類型錯誤：「元組」對象不是可調用

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