tensorflow:画neuralnetworks的流程图

import tensorflow as tf
with tf.name_scope('此处为命名')：
     

def add_layer(inputs,in_size,out_size,activation_function=None):
    with tf.name_scope('layers'):
        with tf.name_scope('weights'):
             Weights = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
             
        with tf.name_scope('biases'):
            biases=tf.Variable(tf.zeros([1,out_size])+0.1,name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b=tf.add(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

with tf.name_scope('inputs'):
    xs=tf.placeholder(tf.float32,[None,1],name='x_input')   #None 无论多少例子都可以
    ys=tf.placeholder(tf.float32,[None,1],name='y_input')


# create NN
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)

with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),
                   reduction_indices=[1]))  #维度为1
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.initialize_all_variables()
sess=tf.Session()
writer=tf.summary.FileWriter("E:\\python\\tensorflow",sess.graph)
sess.run(init)


#visiualization 




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}))
    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.savefig("1.png")
        plt.show()   
writer.close()  
sess.close()