第二讲 神经网络优化--正则化

# -*- coding: utf-8 -*-
#导入所需模块
import tensorflow as tf
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd

#读入数据/标签，生成x_train, y_train
df = pd.read_csv("F:\\tensorflow tutorial\\中国大学MOOCTF笔记2.1共享给所有学习者\\class2\\dot.csv")
x_data = np.array(df[["x1", "x2"]])
y_data = np.array(df["y_c"])

x_train = x_data
y_train = y_data.reshape(-1, 1)

Y_c = [["red" if y else "blue"] for y in y_train]

#转换x的数据类型，否则后面矩阵相乘时会因数据类型问题报错
x_train = tf.cast(x_train, tf.float32)
y_train = tf.cast(y_train, tf.float32)

#from_tensor_slices函数切分传入的张量的第一个维度，生成对应的数据集，使输入特征和标签值一一对应
train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32)



#用tf.Variable()保证参数可训练
w1 = tf.Variable(tf.random.normal([2, 11]), dtype=tf.float32)
b1 = tf.Variable(tf.constant(0.01, shape=[32]))
print(b1)
print(b1.shape)

w2 = tf.Variable(tf.random.normal([11, 1], dtype=tf.float32))
b2 = tf.Variable(tf.constant(0.01, shape=[1]))

lr = 0.005 #学习率
epoch = 800 #循环轮数


#训练部分
for epoch in range(epoch):
  for step, (x_train, y_train) in enumerate(train_db):
    with tf.GradientTape() as tape: #记录梯度信息
      h1 = tf.matmul(x_train, w1) + b1 #记录神经网络乘加运算
      h1 = tf.nn.relu(h1)
      y = tf.matmul(h1, w2) + b2
      
      #采用均方误差损失函数mse = mean(sum(y-out)^2)
      loss_mse = tf.reduce_mean(tf.square(y_train - y))

　　　　"""
      #添加L2正则化
      loss_regularization = []
      #tf.nn.l2_loss(w) = sum(w ** 2) / 2
      loss_regularization.append(tf.nn.l2_loss(w1))
      loss_regularization.append(tf.nn.l2_loss(w2))
      #求和
      #例： x=tf.constant([1, 1, 1], [1, 1, 1])
      # tf.reduce_sum(x)
      # >>>6
      loss_regularization = tf.reduce_sum(loss_regularization)
      loss = loss_mse + 0.03 * loss_regularization  #REGULARIZER = 0.03
      """
      loss = loss_mse
    #计算loss对各个参数的梯度
    variables = [w1, b1, w2, b2]
    grads = tape.gradient(loss, variables)

    #实现梯度更新
    #w1 = w1 - lr * w1_grad
    w1.assign_sub(lr * grads[0])
    b1.assign_sub(lr * grads[1])
    w2.assign_sub(lr * grads[2])
    b2.assign_sub(lr * grads[3])

  #每200个epoch, 打印loss信息
  if epoch % 50 ==0:
    print("epoch:", epoch, "loss:", float(loss))


#预测部分
print("***********predict****************")
#xx在-3到3之间以步长为0.01， yy在-3到3之间以步长0.01，生成间隔数值点
xx, yy = np.mgrid[-3:3:.1, -3:3:.1]
#将xx, yy拉直，并合并配对为二维张量，生成二维坐标点
grid = np.c_[xx.ravel(), yy.ravel()]
grid = tf.cast(grid, tf.float32)
#将网格坐标点喂入神经网络，进行预测，probs为输出
probs = []
for x_predict in grid:
  #使用训练好的参数进行预测
  h1 = tf.matmul([x_predict], w1) + b1
  h1 = tf.nn.relu(h1)
  y = tf.matmul(h1, w2) + b2 #y为预测结果
  probs.append(y)

#取第0列给x1，取第1列给x2
x1 = x_data[:, 0]
x2 = x_data[:, 1]

#probs的shape调整成xx的样子
probs = np.array(probs).reshape(xx.shape)
plt.scatter(x1, x2, color=np.squeeze(Y_c))
#把坐标xx, yy和对应的值probs放入contour函数，给probs值为0.5的所有点上色，plt.show()后，显示的是红蓝点的分界线
plt.contour(xx, yy, probs, levels=[.5])
plt.show()

　　　　　　　　　　不含正则化　　　　　　　　　　　　　　　　　　　　　　　　含正则化