tensorflow学习七之tensorborader2

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
x_data = np.linspace(-1,1,300)[:,np.newaxis]
print(x_data.shape)
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data) -0.5 + noise

def add_layer(input,in_size,out_size,n_layer,activation_function=None):
    layer_name = 'layer%s' % n_layer
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size,out_size]),name='w')
            tf.summary.histogram(layer_name +'/weights',Weights)
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name='b')
            tf.summary.histogram(layer_name + '/biases', biases)
        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)
        tf.summary.histogram(layer_name + '/outputs',outputs)
        return outputs
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,n_layer = 1,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1,n_layer=2,activation_function=None)
with tf.name_scope("loss"):
    loss =tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
    tf.summary.scalar('loss',loss)
with tf.name_scope("train"):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
merged = tf.summary.merge_all()#打包放在一起
writer = tf.summary.FileWriter("logs/",sess.graph)
sess.run(init)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion()#可以连续plot
plt.show()
for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i% 50  == 0  :
       result = sess.run(merged,feed_dict={xs:x_data,ys:y_data})
       writer.add_summary(result,i)