### create a NN
# 添加层
def add_layer(inputs,in_size,out_size,activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size,out_size]))
biases=tf.Variable(tf.zeros([1,out_size]))+0.1
Wx_plus_b=tf.matmul(inputs,Weights)+biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
x_data=np.linspace(-1,1,300)[:,np.newaxis] # 300个 -1~1 行矩阵变成列矩阵
# add noises
noise=np.random.normal(0,0.05,x_data.shape)
y=np.square(x_data)-0.5+noise
xs=tf.placeholder(tf.float32,[None,1]) #None 无论多少例子都可以
ys=tf.placeholder(tf.float32,[None,1])
# create NN
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)
loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),
reduction_indices=[1])) #维度为1
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init=tf.initialize_all_variables()
sess=tf.Session()
sess.run(init)
for i in range(1000):
sess.run(train_step,feed_dict={xs: x_data, ys: y})
if i % 100==0:
print("i is:",i,"error is:",sess.run(loss,feed_dict={xs:x_data,ys:y}))
【结果】：
i is: 0 error is: 0.42239714 i is: 100 error is: 0.006036017 i is: 200 error is: 0.0050696223 i is: 300 error is: 0.004576256 i is: 400 error is: 0.0042678043 i is: 500 error is: 0.004062524 i is: 600 error is: 0.0039021121 i is: 700 error is: 0.0037654035 i is: 800 error is: 0.00363621 i is: 900 error is: 0.0034959468
【结果可视化】：
if i==999:
fig=plt.figure()
ax=fig.add_subplot(111)
ax.scatter(x_data,y)
prediction_value=sess.run(prediction,feed_dict={xs:x_data,ys:y})
lines=ax.plot(x_data,prediction_value,'r-',lw=5) #|线宽为5
plt.show()
