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tensorboardUP.py
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tensorboardUP.py
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#!coding=UTF-8
import tensorflow as tf
import numpy as np
def add_layer(inputs, in_size, out_size, n_layer, acti_fun=None):
# rows = in_size, cols = out_size
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='WStupid')
# #################添加这样一样
tf.summary.histogram(layer_name+'/Weights', Weights)
# b一般不为0,所以需要加0.1
with tf.name_scope("biaes"):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='StupidB')
tf.summary.histogram(layer_name + '/biases', biases)
with tf.name_scope('y_hat'):
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if acti_fun is None:
# 未提供额外函数的时候,默认为线性函数
outputs = Wx_plus_b
else:
# 根据提供的激活函数处理结果
outputs = acti_fun(Wx_plus_b)
tf.summary.histogram(layer_name + '/outputs', outputs)
return outputs
# np.newaxis把vector变为300*1的mat, 注意更改生成的数据的float类型
x_data = np.linspace(-1,1,300, dtype=np.float32)[:, np.newaxis]
# 设置一个noise的值,参数分别未均值,方差,shape
noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32)
y_data = np.square(x_data) - 0.5 + noise
# None means无论输入多少sample都ok.因为输入只有一个特征,所以这里是1
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')
# 隐藏层有10个神经元,所以out_size=10, 浅层确实多种激活函数都可以使用
layer1 = add_layer(xs, 1, 10, n_layer=1, acti_fun=tf.nn.relu)
prediction = add_layer(layer1,10, 1, n_layer=2, acti_fun=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)
# 内部是learning rate,后面是train的目的,目的是最小化loss
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session()
# tf.train.SummaryWriter soon be deprecated, use following
# 把各种summary打包合并放到session.graph上面
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter('logs/', sess.graph)
# init = tf.initialize_all_variables() 这个方式可以废弃了
init = tf.global_variables_initializer() # 定义初始化全局所有变量的节点
sess.run(init) # 执行初始化
# 查看summary必须要有train data,并运行整个过程
for i in range(1000):
sess.run(train_step, feed_dict={xs:x_data, ys:y_data})
if i % 50 == 0:
# summary 只有在run之后才有效
result = sess.run(merged, feed_dict={xs:x_data, ys:y_data})
writer.add_summary(result, i)