tensorflow基本教程6：可视化结果

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#如何建立神经网络的结构：层
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
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]
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)-0.5+noise
##用placeholder作为占位时，必须定义类型，因为没有默认类型
xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])
#定义隐藏层
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]))
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init=tf.global_variables_initializer()
with tf.Session()as sess:
    sess.run(init)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.scatter(x_data,y_data)
    plt.ion()#不暂停
    plt.show()
    for i in range(1000):
        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}))
            
            prediction_value=sess.run(prediction,feed_dict={xs:x_data,ys:y_data})
#            print(prediction_value)
            plt.plot(x_data,prediction_value,'r',lw=5)
#            ax.lines.remove(lines[0])
            plt.pause(0.1)