tensorflow构建一个简单神经网络

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# 添加层（输入层，隐藏层，输出层）
# inputs，输入数据
# insize，输入神经数，out_size,输出神经数
# activation_function，激活函数，None就是不作处理
def add_layer(inputs, in_size, out_size, activation_function=None):

    # (1,10) 正态分布随机数
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    # 点积
    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

# np.newaxis, 增加一维,[300, 1]
x_data = np.linspace(-1, 1, 300, dtype=np.float32)[:, np.newaxis]
# 躁点
noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32)
y_data = np.square(x_data) - 0.5 + noise

# 占位符
# [None，1]， 任意行，1列
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

# 隐藏层
# 激活函数使用Relu，非线性化函数
# 为什么使用激活函数参考
# https://www.cnblogs.com/silence-tommy/p/7113405.html
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# 输出层
prediction = add_layer(l1, 10, 1, activation_function=None)
# 损失函数
# reduct_sum, axis=【1】，将整行的所有列相加
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), axis=[1]))

# 优化，梯度下降，减少损失函数的值
# 得到使损失函数最低的W，也就是最优解
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    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}))

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
plt.show()