generateds.py 生成数据
import numpy as np
import matplotlib.pyplot as plt
seed = 2
def generateds():
rdm = np.random.RandomState(seed)
X = rdm.randn(300,2)
Y_ = [int(x0*x0+x1*x1<2)for (x0,x1) in X]
# 1红0 绿
Y_c = [['red' if y else 'blue']for y in Y_]
# 形状整理，-1表示跟随第二列计算，第二个表示多少列
X = np.vstack(X).reshape(-1,2)
Y_ = np.vstack(Y_).reshape(-1, 1)
return X,Y_,Y_c
# def draw(X,Y_c):
# x =[None]*300
# y =[None]*300
# i = 0
# for x1,y1 in X:
# x[i] = x1
# y[i] =y1
# i+=1
# print(Y_c)
# z = [None]*300
# for i in range(300):
# z[i]="".join(Y_c[i])
# print(z)
# plt.figure()
# plt.scatter(x,y,c=z)
# plt.show()
# X,Y_,Y_c = generateds()
# draw(X,Y_c)
forward.py 搭建网络，设计网络结构
import tensorflow as tf
def forward(x,regularizer):#输入x，正则化权重，采用relu激活函数
w1 =get_weight([2,11],regularizer) #权值/权重
b1 = get_bias([11]) #偏执值
y1 = tf.nn.relu(tf.matmul(x,w1)+b1)
w2 =get_weight([11,1],regularizer)
b2 = get_bias([1])
y = tf.matmul(y1,w2)+b2# 输出层不过激活
return y
def get_weight(shape,regularizer):
w = tf.Variable(tf.random_normal(shape),dtype=tf.float32) #付初值
tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
return w
def get_bias(shape):
b = tf.Variable(tf.constant(0.01,shape=shape))
return b
backward.py 反向传播，训练参数
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import generateds
import forward
#定义超参数
STEPS =40000
BATCH_SIZE = 30
LEARNING_RATE_BASE = 0.001
LEARNING_RATE_DECAT = 0.999 #滑动平均的超参数
REGULARIZER = 0.01
def backward():
x = tf.placeholder(tf.float32,shape=(None,2))
y_= tf.placeholder(tf.float32,shape=(None,1))
X,Y_,Y_c = generateds.generateds()
y = forward.forward(x,REGULARIZER)
#定义轮数计数器
global_step = tf.Variable(0,trainable=False)
#指数衰减学习率
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,
global_step,
300/BATCH_SIZE,
LEARNING_RATE_DECAT,
staircase=True
)
#定义损失函数 正则化的
loss_mse = tf.reduce_mean(tf.square(y-y_))
loss_total = loss_mse+tf.add_n(tf.get_collection('losses'))
#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:
sess.run(tf.global_variables_initializer())
for i in range(STEPS):
start = (i*BATCH_SIZE)%300
end = start+BATCH_SIZE
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))
#-3 - 3 之间生成网格坐标点
xx,yy = np.mgrid[-3:3:0.01,-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))
plt.contour(xx,yy,probs,levels=[.5]) #曲线
plt.show()
if __name__ =='__main__':
backward()
结果：
