Tensorflow tf.sigmoid（）返回一個數組而不是一個標量

我想創建一個只有一個類的tensorflow中的簡單邏輯迴歸模型。但是，由於某些原因，tf.sigmoid函數返回的是數組類型而不是標量。Tensorflow tf.sigmoid（）返回一個數組而不是一個標量

的類型從成本函數返回值是一個np.ndarray（）

形狀（3390,2）

我想不通爲什麼tf.sigmoid函數將返回數組類型而不是標量... 任何幫助表示讚賞。

這裏是我的代碼：

#!/usr/bin/env python3 

import tensorflow as tf 
import numpy as np 
import pandas as pd 
from sklearn import preprocessing 
from sklearn import model_selection 
import matplotlib.pyplot as plt 
import seaborn as sns 
import sys 

sns.set(style='white') 
sns.set(style='whitegrid',color_codes=True) 



bank_data = pd.read_csv('data/bank.csv',header=0,delimiter = ';') 
bank_data = bank_data.dropna() 

bank_data.drop(bank_data.columns[[0,3,8,9,10,11,12,13]],axis=1,inplace=True) 
data_set = pd.get_dummies(bank_data,columns = ['job','marital','default','housing','loan','poutcome']) 
data_set.drop(data_set.columns[[14,27]],axis=1,inplace=True) 
data_set_y = data_set['y'] 
data_set_y = data_set_y.replace(('yes','no'),(1.0,0.0)) 

data_set_X = data_set.drop(['y'],axis=1) 
num_samples = data_set.shape[0] 
num_features = data_set_X.shape[1] 
num_labels = 1 


X = tf.placeholder('float',[None,num_features]) 
y = tf.placeholder('float',[None,num_labels]) 

W = tf.Variable(tf.zeros([num_features,2]),dtype=tf.float32) 
b = tf.Variable(tf.zeros([1]),dtype=tf.float32) 

train_X,test_X,train_y,test_y = model_selection.train_test_split(data_set_X,data_set_y,random_state=0) 
train_y = np.reshape(train_y,(-1,1)) 

prediction = tf.add(tf.matmul(X,W),b) 
cost = tf.sigmoid(prediction) 
optimizer = tf.train.GradientDescentOptimizer(0.001).minimize(cost) 
num_epochs = 1000 


print ('Shape of train_y is: ',train_y.shape) 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    for epoch in range(num_epochs): 
     _,l = sess.run([optimizer,cost],feed_dict = {X: train_X, y: train_y}) 
     if epoch % 50 == 0: 
      print (type(l)) 
      print (l.shape) 
      print (l)

來源

2017-10-21 redmage123