构造神经网络

注明：该代码用来训练一个神经网络，网络拟合y = x^2-0.5+noise，该神经网络的结构是输入层为一个神经元，隐藏层为十个神经元，输出层为一个神经元

1. 导入相关库

# 导入相关库
import tensorflow as tf  # 用来构造神经网络
import numpy as np  # 用来构造数据结构和处理数据模块

2. 定义一个层

# 定义一个层
def add_layer(inputs, in_size, out_size, activation_function=None):# 定义一个层，其中inputs为输入，in_size为上一层神经元数，out_size为该层神经元数# activation_function为激励函数Weights = tf.Variable(tf.random_normal([in_size, out_size]))# 初始权重随机生成比较好，in_size，out_size为该权重维度biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)# 偏置Wx_plus_b = tf.matmul(inputs, Weights) + biases# matmul为矩阵里的函数相乘if activation_function is None:outputs = Wx_plus_b  # 如果激活函数为空，则不激活，保持数据else:outputs = activation_function(Wx_plus_b)# 如果激活函数不为空，则激活，并且返回激活后的值return outputs  # 返回激活后的值

3. 构造数据集

# 构造一些样本，用来训练神经网络
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
# 值为（-1，1）之间的数，有300个
noise = np.random.normal(0, 0.05, x_data.shape)
x_data

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# 加入噪声会更贴近真实情况，噪声的值为（0，0.05）之间，结构为x_data一样
y_data = np.square(x_data) - 0.5 + noise
# y的结构
y_data

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4. 定义基本模型

# 定义placeholder用来输入数据到神经网络，其中1表只有一个特征，也就是维度为一维数据
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)# 代价函数，reduce_mean为求均值，reduce_sum为求和，reduction_indices为数据处理的维度
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))# 将代价函数传到梯度下降，学习速率为0.1，这里包含权重的训练，会更新权重
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

5. 变量初始化

# important step
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
# 变量初始化
if int((tf.__version__).split('.')[1]) < 12:init = tf.initialize_all_variables()
else:init = tf.global_variables_initializer()
sess = tf.Session()  # 打开TensorFlow
sess.run(init)  # 执行变量初始化

6. 开始训练

for i in range(1000):  # 梯度下降迭代一千次# trainingsess.run(train_step, feed_dict={xs: x_data, ys: y_data})# 执行梯度下降算法，并且将样本喂给损失函数if i % 50 == 0:# 每50次迭代输出代价函数的值print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))

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