TensorFlow机器学习writer = tf.summary.FileWriter('logs/', sess.graph)图形化失败

有个问题啊,我看了大神的TensorFlow机器学习,有些代码是不能用的了,有更新了方法名称之类的.我的这一段代码没有办法得到想要的效果.不知为何?
朋友请帮我看看吧.

import tensorflow as tf
import numpy as np

def add_layer(input,in_size,out_size,activation_function=None):
    with tf.name_scope('layer'):
        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)
    with tf.name_scope('Wx_plus_b'):
        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

print("111")


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

with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

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]))

with tf.name_scope('train_step'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()

sess = tf.Session()
writer = tf.summary.FileWriter('logs/', sess.graph)
sess.run(init)


for i in range(5000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i % 20 == 0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

#代码图形化失败
# 执行代码后,在logs下也生成了文件,但不是morvan文件,而是得到名为events.out.tfevents.1537191976.Administrator-PC的文件.
# terminal下执行tensorboard --logdir='logs/',将得到的url拷贝到浏览器中,也没有看到图形.
#失败,待处理.