Python神经网络编程-根据身份证号判断性别

import tensorflow as tf import random tf.compat.v1.disable_eager_execution()# for v2 random.seed() x = tf.compat.v1.placeholder(tf.float32) yTrain = tf.compat.v1.placeholder(tf.float32) #x = tf.placeholder(tf.float32) #yTrain = tf.placeholder(tf.float32) w = tf.Variable(tf.random.normal([4], mean=0.5, stddev=0.1), dtype=tf.float32) #w形态为【4】，一个4维向量 #w = tf.Variable(tf.random_normal([4], mean=0.5, stddev=0.1), dtype=tf.float32) #w形态为【4】，一个4维向量 b = tf.Variable(0, dtype=tf.float32) n1 = w * x + b y = tf.nn.sigmoid(tf.reduce_sum(n1)) #tf.reduce_sum()和sigmoid()合并到输出节点y的操作中 loss = tf.abs(y - yTrain) optimizer = tf.compat.v1.train.RMSPropOptimizer(0.01) #optimizer = tf.train.RMSPropOptimizer(0.01) train = optimizer.minimize(loss) sess = tf.compat.v1.Session() sess.run(tf.compat.v1.global_variables_initializer()) #sess = tf.Session() #sess.run(tf.global_variables_initializer()) lossSum = 0.0 #训练中的误差总和 for i in range(5000): xDataRandom = [int(random.random() * 10), int(random.random() * 10), int(random.random() * 10), int(random.random() * 10)] if xDataRandom[2] % 2 == 0: yTrainDataRandom = 0 else: yTrainDataRandom = 1 result = sess.run([train, x, yTrain, y, loss], feed_dict={x: xDataRandom, yTrain: yTrainDataRandom}) lossSum = lossSum + float(result[len(result) - 1]) print("i: %d, loss: %10.10f, avgLoss: %10.10f" % (i, float(result[len(result) - 1]), lossSum / (i + 1)))