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tensorboard.py
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tensorboard.py
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#!coding=UTF-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs, in_size, out_size, acti_fun=None):
# rows = in_size, cols = out_size
with tf.name_scope('layer'):
with tf.name_scope("weights"):
Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='WStupid')
# 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')
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)
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
# **************************************
# 如果上面4行原始数据不注释掉,生成的flow graph就不正确.
# **************************************
# 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, acti_fun=tf.nn.relu)
prediction = add_layer(layer1,10, 1, acti_fun=None)
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))
# 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
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
# for i in range(1000):
# sess.run(train_step, feed_dict={xs:x_data, ys:y_data})
# if i % 50 == 0:
# print(sess.run(loss, feed_dict={xs:x_data, ys:y_data}))