TensorFlow基础8——结果可视化

 

import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt #引入图形化包

def add_layer(input,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(input,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]
noise = np.random.normal(0,0.05,x_data.shape)#噪音
y_data = np.square(x_data)-0.5+noise

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
predition = add_layer(l1,10,1,activation_function=None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

#显示真实数据
fig = plt.figure()
ax = fig.add_subplot(1,1,1) #显示图比例？
ax.scatter(x_data,y_data)
plt.ion() #如果在脚本中使用ion()命令开启了交互模式，没有使用ioff()关闭的话，则图像会一闪而过，并不会常留。要想防止这种情况，需要在plt.show()之前加上ioff()命令。

plt.show()

for i in range(1001):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i % 50 == 0:
        #print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
        try:
            ax.lines.remove(lines[0]) #消去之前的线条
        except Exception:
            pass
        predition_value = sess.run(predition,feed_dict={xs:x_data})
        lines = ax.plot(x_data,predition_value,'r-',lw=5) #画线
        plt.pause(0.1) #暂停

 运行结果：

真实结果为动图，模拟训练的过程。