import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def get_data(x,w,b,d):
c,r = x.shape
y = (w * x * x + b*x + d)+ (0.1*(2*np.random.rand(c,r)-1))
return(y)
xs = np.arange(0,3,0.01).reshape(-1,1)
ys = get_data(xs,1,-2,3)
plt.title("curve")
plt.plot(xs,ys)
plt.show()
生成的数据图像为:
x = tf.placeholder(tf.float32,[None,1])
y_ = tf.placeholder(tf.float32,[None,1])
w1 = tf.get_variable("w1",initializer=tf.random_normal([1,16]))
w2 = tf.get_variable("w2",initializer=tf.random_normal([16,1]))
b1 = tf.get_variable("b1",initializer=tf.zeros([1,16]))
b2 = tf.get_variable("b2",initializer=tf.zeros([1,1]))
l1 = tf.matmul(x,w1)+b1
l1 = tf.nn.elu(l1)
y = tf.matmul(l1,w2)+b2
loss = tf.reduce_mean(tf.square(y-y_))
opt = tf.train.GradientDescentOptimizer(0.02).minimize(loss)
with tf.Session() as sess:
srun = sess.run
init = tf.global_variables_initializer()
srun(init)
for e in range(4001):
loss_val,_ = srun([loss,opt],{x:xs,y_:ys})
if(e%200 ==0):
print("%d steps loss is %f"%(e,loss_val))
ys_pre = srun(y,{x:xs})
plt.title("curve")
plt.plot(xs,ys)
plt.plot(xs,ys_pre)
plt.legend("ys","ys_pre")
plt.show()
0 steps loss is 49.556065
200 steps loss is 0.352589
400 steps loss is 0.108551
600 steps loss is 0.042510
800 steps loss is 0.030316
1000 steps loss is 0.024551
1200 steps loss is 0.020459
1400 steps loss is 0.017488
1600 steps loss is 0.015306
1800 steps loss is 0.013710
2000 steps loss is 0.012510
2200 steps loss is 0.011569
2400 steps loss is 0.010802
2600 steps loss is 0.010148
2800 steps loss is 0.009566
3000 steps loss is 0.009039
3200 steps loss is 0.008552
3400 steps loss is 0.008097
3600 steps loss is 0.007674
3800 steps loss is 0.007285
4000 steps loss is 0.006934
运行结果图
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def get_data(x,w,b,d):
c,r = x.shape
y = (w * x * x + b*x + d)+ (0.1*(2*np.random.rand(c,r)-1))
return(y)
xs = np.arange(0,3,0.01).reshape(-1,1)
ys = get_data(xs,1,-2,3)
"""plt.title("curve")
plt.plot(xs,ys)
plt.show()"""
x = tf.placeholder(tf.float32,[None,1])
y_ = tf.placeholder(tf.float32,[None,1])
w1 = tf.get_variable("w1",initializer=tf.random_normal([1,16]))
w2 = tf.get_variable("w2",initializer=tf.random_normal([16,1]))
b1 = tf.get_variable("b1",initializer=tf.zeros([1,16]))
b2 = tf.get_variable("b2",initializer=tf.zeros([1,1]))
l1 = tf.matmul(x,w1)+b1
l1 = tf.nn.elu(l1)
y = tf.matmul(l1,w2)+b2
loss = tf.reduce_mean(tf.square(y-y_))
opt = tf.train.GradientDescentOptimizer(0.02).minimize(loss)
with tf.Session() as sess:
srun = sess.run
init = tf.global_variables_initializer()
srun(init)
for e in range(4001):
loss_val,_ = srun([loss,opt],{x:xs,y_:ys})
if(e%200 ==0):
print("%d steps loss is %f"%(e,loss_val))
ys_pre = srun(y,{x:xs})
plt.title("curve")
plt.plot(xs,ys)
plt.plot(xs,ys_pre)
plt.legend("ys","ys_pre")
plt.show()