服务器之家

服务器之家 > 正文

tensorflow建立一个简单的神经网络的方法

时间:2021-01-14 00:41     来源/作者:Mr丶Caleb

本笔记目的是通过tensorflow实现一个两层的神经网络。目的是实现一个二次函数的拟合。

如何添加一层网络

代码如下:

?
1
2
3
4
5
6
7
8
9
10
def add_layer(inputs, in_size, out_size, activation_function=None):
  # add one more layer and return the output of this layer
  Weights = tf.Variable(tf.random_normal([in_size, out_size]))
  biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
  Wx_plus_b = tf.matmul(inputs, Weights) + biases
  if activation_function is None:
    outputs = Wx_plus_b
  else:
    outputs = activation_function(Wx_plus_b)
  return outputs

注意该函数中是xW+b,而不是Wx+b。所以要注意乘法的顺序。x应该定义为[类别数量, 数据数量], W定义为[数据类别,类别数量]。

创建一些数据

?
1
2
3
4
# Make up some real data
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

numpy的linspace函数能够产生等差数列。start,stop决定等差数列的起止值。endpoint参数指定包不包括终点值。

?
1
2
3
numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)[source]
Return evenly spaced numbers over a specified interval.
Returns num evenly spaced samples, calculated over the interval [start, stop].

tensorflow建立一个简单的神经网络的方法

noise函数为添加噪声所用,这样二次函数的点不会与二次函数曲线完全重合。

numpy的newaxis可以新增一个维度而不需要重新创建相应的shape在赋值,非常方便,如上面的例子中就将x_data从一维变成了二维。

添加占位符,用作输入

?
1
2
3
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

添加隐藏层和输出层

?
1
2
3
4
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)

计算误差,并用梯度下降使得误差最小

?
1
2
3
# the error between prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

完整代码如下:

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
def add_layer(inputs, in_size, out_size, activation_function=None):
  # add one more layer and return the output of this layer
  Weights = tf.Variable(tf.random_normal([in_size, out_size]))
  biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
  Wx_plus_b = tf.matmul(inputs, Weights) + biases
  if activation_function is None:
    outputs = Wx_plus_b
  else:
    outputs = activation_function(Wx_plus_b)
  return outputs
 
# Make up some real data
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
 
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)
 
# the error between prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
           reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
 
# important step
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
 
# plot the real data
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()
 
for i in range(1000):
  # training
  sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
  if i % 50 == 0:
    # to visualize the result and improvement
    try:
      ax.lines.remove(lines[0])
    except Exception:
      pass
    prediction_value = sess.run(prediction, feed_dict={xs: x_data})
    # plot the prediction
    lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
    plt.pause(0.1)

运行结果:

tensorflow建立一个简单的神经网络的方法

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持服务器之家。

原文链接:http://blog.csdn.net/qq_30159351/article/details/52639291

相关文章

热门资讯

2020微信伤感网名听哭了 让对方看到心疼的伤感网名大全
2020微信伤感网名听哭了 让对方看到心疼的伤感网名大全 2019-12-26
Intellij idea2020永久破解,亲测可用!!!
Intellij idea2020永久破解,亲测可用!!! 2020-07-29
背刺什么意思 网络词语背刺是什么梗
背刺什么意思 网络词语背刺是什么梗 2020-05-22
歪歪漫画vip账号共享2020_yy漫画免费账号密码共享
歪歪漫画vip账号共享2020_yy漫画免费账号密码共享 2020-04-07
苹果12mini价格表官网报价 iPhone12mini全版本价格汇总
苹果12mini价格表官网报价 iPhone12mini全版本价格汇总 2020-11-13
返回顶部