TensorFlow学习笔记----TensorBoard_1

一个曲线拟合的小例子说明要使用TensorBoard，需要对程序添加那些额外的东西。程序：

import tensorflow as tf

import numpy as np

# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3

x_data = np.random.rand(1000,1).astype(np.float32)

y_data = tf.sin(x_data)*tf.cos(x_data)+tf.random_uniform([1000,1], -0.1, 0.1)

#graph

X = tf.placeholder(tf.float32,[None,1],name = 'X-input')

Y = tf.placeholder(tf.float32,[None,1],name = 'Y-input')

W1 = tf.Variable(tf.random_uniform([1,5], -1.0, 1.0),name = 'weight1')

W2 = tf.Variable(tf.random_uniform([5,2], -1.0, 1.0),name = 'weight2')

W3 = tf.Variable(tf.random_uniform([2,1], -1.0, 1.0),name = 'weight3')

b1 = tf.Variable(tf.zeros([5]), name = 'bias1')

b2 = tf.Variable(tf.zeros([2]), name = 'bias2')

b3 = tf.Variable(tf.zeros([1]), name = 'bias3')

with tf.name_scope('layer2') as scope:
L2 = tf.sigmoid(tf.matmul(X,W1)+b1)

with tf.name_scope('layer3') as scope:
L3 = tf.sigmoid(tf.matmul(L2,W2)+b2)

with tf.name_scope('layer4') as scope:
hypothesis = tf.sigmoid(tf.matmul(L3,W3)+b3)

with tf.name_scope('cost') as scope:
cost = -tf.reduce_mean(Y*tf.log(hypothesis))
cost_summery = tf.scalar_summary("cost",cost)

with tf.name_scope('train') as scope:
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(cost)

#the summery

w1_hist = tf.histogram_summary("weight1",W1)

w2_hist = tf.histogram_summary("weight2",W2)

b1_hist = tf.histogram_summary("bisa1",b1)

b2_hist = tf.histogram_summary("bisa2",b2)

y_hist = tf.histogram_summary("y",Y)

init = tf.initialize_all_variables()

#run

with tf.Session() as sess:

sess.run(init)
#the workers who translate data to TensorBoard
merged = tf.merge_all_summaries() #collect the tf.xxxxx_summary
writer = tf.train.SummaryWriter('keep',sess.graph) 

        # maybe many writers to show different curvs in the same figure
for step in range(20000):
summary, _ = sess.run([merged, train], feed_dict={X:x_data,Y:y_data.eval()})
writer.add_summary(summary, step)
if step%10 ==0:
print('step %s' % (step))