我的数据如下所示:Loan Decision Data
我正在尝试根据前4个特征(Gen,Age,Psco,SocCon)预测贷款决定(0或1)Gen和SocCon是分类的,因此我使用了一键编码。
这里是代码:
import numpy as np import pandas as pd from sklearn.model_selection import train_test_split import tensorflow as tf loan_data = pd.read_csv('data/LoanDecision.csv') loanNN = pd.get_dummies(loan_data, columns=['Gen','SocCon']) X_dataNN = loanNN[['Gen_1', 'Gen_2', 'AGE', 'Psco', 'SocCon_1', 'SocCon_2', 'SocCon_3']] y_dataNN = loanNN[['Decision']] X_trainNN, X_testNN, y_trainNN, y_testNN = train_test_split(X_dataNN, y_dataNN, random_state=1011) x = tf.placeholder(tf.float32,[None,7]) t = tf.placeholder(tf.float32,[None,1]) w1 = tf.Variable(tf.random_normal([7,7]), name = "weight1") w0_1 = tf.Variable(tf.random_normal([7]), name = "bias1") f1 = tf.matmul(x,w1)+w0_1 p1 = tf.nn.softmax(f1) W = tf.Variable(tf.random_normal([7,1]), name = "weights") b = tf.Variable(tf.random_normal([1]), name = "bias") f = tf.matmul(p1,W)+b p = tf.sigmoid(f) loss = tf.reduce_sum(tf.square(t-p)) #train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss) train_step = tf.train.AdamOptimizer(0.01).minimize(loss) #predict = tf.equal(tf.cast(p > 0.5,tf.float32), t) predict = tf.equal(tf.round(p), tf.round(t)) accuracy = tf.reduce_mean(tf.cast(predict,tf.float32)) sess = tf.Session() sess.run(tf.global_variables_initializer()) i=0 for _ in range(10000): i+=1 sess.run(train_step, feed_dict = {x: X_dataNN, t: y_dataNN}) if i % 1000 == 0: loss_val, acc_val = sess.run([loss, accuracy], feed_dict = {x: X_dataNN, t: y_dataNN}) print('Step: %d, Loss: %f, Accuracy: %f '%(i, loss_val, acc_val)) sess.close()
我不确定出什么问题,但是我一直得到相同的损失值,准确度没有改变。
Step: 1000, Loss: 473.172150, Accuracy: 0.625062 Step: 2000, Loss: 473.172150, Accuracy: 0.625062 Step: 3000, Loss: 473.172150, Accuracy: 0.625062 Step: 4000, Loss: 473.172150, Accuracy: 0.625062 Step: 5000, Loss: 473.172150, Accuracy: 0.625062 Step: 6000, Loss: 473.172150, Accuracy: 0.625062 Step: 7000, Loss: 473.171844, Accuracy: 0.625062 Step: 8000, Loss: 473.171906, Accuracy: 0.625062 Step: 9000, Loss: 473.172821, Accuracy: 0.625062 Step: 10000, Loss: 473.170105, Accuracy: 0.625062
使用单层时是相同的。有任何想法吗?是损失函数,训练还是仅仅是不良数据?任何帮助表示赞赏。
我的数据看起来像这样:贷款决策数据我试图基于前4个特征(Gen,Age,Psco,SocCon)来预测贷款决策(0或1)Gen和SocCon是分类的,因此我使用了-热...
通常来说,很少怪数据(除非您有理由怀疑它?),而且我不确定您对培训的含义,它看起来很合理。您的损失正在缓慢地移动,所以我想您需要学习学习率/损失功能