第四节:Tensorboard可视化(一)¶
1.搭建图纸¶
input层开始¶
# 将xs和ys包含进来，形成一个大的图层，图层名字叫做inputs
with tf.name_scope('inputs'):
# 为xs指定名称x_input
xs = tf.placeholder(tf.float32, [None, 1],name='x_input')
# 为ys指定名称y_input
ys = tf.placeholder(tf.float32, [None, 1],name='y_input')
layer层¶
# 定义添加神经层的函数
def add_layer(inputs,in_size,out_size,activation_function=None):
# 定义大框架名字为layer
with tf.name_scope('layes'):
# 框架里面的小部件Weights定义,同时也可以在weights中指定名称W(将会在Weights展开后显示)
with tf.name_scope('weights'):
Weights=tf.Variable(tf.random_uniform([in_size,out_size]),name='W')
# 框架里面的小部件biases定义
with tf.name_scope('biases'):
biases=tf.Variable(tf.zeros([1,out_size])+0.1)
# 框架里面的小部件Wx_plus_b定义
with tf.name_scope('Wx_plus_b'):
Wx_plus_b=tf.matmul(inputs,Weights)+biases
'''
activation_function 的话，可以暂时忽略。因为当选择
用 tensorflow 中的激励函数（activation function）的时候，
tensorflow会默认添加名称,这个可以在图形呈现后对比两个layer层进行查看
'''
if activation_function is None:
outputs=Wx_plus_b
else:
outputs=activation_function(Wx_plus_b)
return outputs
定义两层¶
# 定义隐藏层
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
# 定义输出层
prediction=add_layer(l1,10,1,activation_function=None)
绘制loss¶
# 计算预测值prediction与真实值的误差，对两者差的平方求和再取平均
with tf.name_scope('loss'):
loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),
reduction_indices=[1]))
绘制train¶
# 机器学习提升准确率
with tf.name_scope('train'):
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 0.1表示学习效率
收集框架并存储至logs/目录¶
sess=tf.Session()
writer=tf.summary.FileWriter("logs/",sess.graph)
PyCharm Terminal直接进入项目根目录，运行tensorboard --logdir=logs,复制相应的链接至谷歌浏览器你去即可！
第五节:Tensorboard可视化(二)¶
1.导包¶
import tensorflow as tf
import numpy as np
2.make up some data¶
x_data = np.linspace(-1, 1, 300, dtype=np.float32)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32)
y_data = np.square(x_data) - 0.5 + noise
3.将xs和ys包含进来，形成一个大的图层，图层名字叫做inputs¶
with tf.name_scope('inputs'):
# 为xs指定名称x_input
xs = tf.placeholder(tf.float32, [None, 1],name='x_input')
# 为ys指定名称y_input
ys = tf.placeholder(tf.float32, [None, 1],name='y_input')
4.在 layer 中为 Weights, biases 设置变化图表¶
# add_layer多加一个n_layer参数(表示第几层)
def add_layer(inputs ,
in_size,
out_size,n_layer,
activation_function=None):
## add one more layer and return the output of this layer
layer_name='layer%s'%n_layer
with tf.name_scope(layer_name):
# 对weights进行绘制图标
with tf.name_scope('weights'):
Weights= tf.Variable(tf.random_normal([in_size, out_size]),name='W')
tf.summary.histogram(layer_name + '/weights', Weights)
# 对biases进行绘制图标
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.add(tf.matmul(inputs,Weights), biases)
if activation_function is None:
outputs=Wx_plus_b
else:
outputs= activation_function(Wx_plus_b)
# 对outputs进行绘制图标
tf.summary.histogram(layer_name + '/outputs', outputs)
return outputs
5.修改隐藏层与输出层¶
# 由于我们对addlayer 添加了一个参数, 所以修改之前调用addlayer()函数的地方. 对此处进行修改:
# add hidden layer
l1= add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
# add output layer
prediction= add_layer(l1, 10, 1, n_layer=2, activation_function=None)
6.设置loss的变化图¶
# loss是在tesnorBorad 的event下面的, 这是由于我们使用的是tf.scalar_summary() 方法.
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(
tf.square(ys - prediction), reduction_indices=[1]))
tf.summary.scalar('loss', loss) # tensorflow ### = 0.12
7.给所有训练图合并¶
# 机器学习提升准确率
with tf.name_scope('train'):
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 0.1表示学习效率
# 初始化
sess= tf.Session()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/", sess.graph) #
sess.run(tf.global_variables_initializer())
8.训练数据¶
for i in range(1000):
sess.run(train_step, feed_dict={xs:x_data, ys:y_data})
if i%50 == 0:
rs = sess.run(merged,feed_dict={xs:x_data,ys:y_data})
writer.add_summary(rs, i)
9.问题¶
若在浏览器输入相应的链接，没有显示，试试关闭防火墙即可解决！