tensorflow学习笔记(北京大学) tf4_8_backward.py 完全解析

#coding:utf-8 #tensorflow学习笔记(北京大学) tf4_8_backward.py 完全解析 #QQ群：476842922（欢迎加群讨论学习） #如有错误还望留言指正，谢谢 #0导入模块 ，生成模拟数据集 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import opt4_8_generateds import opt4_8_forward STEPS = 40000 BATCH_SIZE = 30 LEARNING_RATE_BASE = 0.001 LEARNING_RATE_DECAY = 0.999 REGULARIZER = 0.01#正则化 def backward():#反向传播 x = tf.placeholder(tf.float32, shape=(None, 2))#占位 y_ = tf.placeholder(tf.float32, shape=(None, 1))#占位 #X：300行2列的矩阵。Y_:坐标的平方和小于2，给Y赋值1，其余赋值0 X, Y_, Y_c = opt4_8_generateds.generateds() #前向传播 y = opt4_8_forward.forward(x, REGULARIZER) global_step = tf.Variable(0,trainable=False) #指数衰减学习率 learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE,#学习率 global_step,#计数 300/BATCH_SIZE, LEARNING_RATE_DECAY,#学习衰减lü staircase=True)#选择不同的衰减方式 #定义损失函数 loss_mse = tf.reduce_mean(tf.square(y-y_))#均方误差 loss_total = loss_mse + tf.add_n(tf.get_collection('losses'))#正则化 #定义反向传播方法：包含正则化 train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss_total) with tf.Session() as sess: init_op = tf.global_variables_initializer()#初始化 sess.run(init_op) for i in range(STEPS): start = (i*BATCH_SIZE) % 300 end = start + BATCH_SIZE#3000轮 sess.run(train_step, feed_dict={x: X[start:end], y_:Y_[start:end]}) if i % 2000 == 0: loss_v = sess.run(loss_total, feed_dict={x:X,y_:Y_}) print("After %d steps, loss is: %f" %(i, loss_v)) xx, yy = np.mgrid[-3:3:.01, -3:3:.01] grid = np.c_[xx.ravel(), yy.ravel()] probs = sess.run(y, feed_dict={x:grid}) probs = probs.reshape(xx.shape) plt.scatter(X[:,0], X[:,1], c=np.squeeze(Y_c)) #画点 plt.contour(xx, yy, probs, levels=[.5])#画线 plt.show()#显示图像 if __name__=='__main__': backward() #coding:utf-8 #tensorflow学习笔记(北京大学) tf4_8_backwar