#可视化 tensorboard
import tensorflow as tf
import numpy as np
def add_layer(inputs,in_size,out_size,activation_function=None):
with tf.name_scope('layer'):
with tf.name_scope('Wieght'):
Weight = 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='biases')
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs,Weight),biases,name='Wx_plus_b')
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
#creat net
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]),name='loss')
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
#merged = tf.summary.merge_all() #将图形、训练过程等数据合并在一起
writer = tf.summary.FileWriter('logs/',tf.get_default_graph())
附:
tesorboard为tensorflow实现可视化,主要为tf.name_scope()函数的使用
tensorboard使用参看:
tensorflow–可视化tensorboard