tensorflow：添加隐藏层 搭建神经网络

# coding:UTF-8
import os
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # 忽略不必要的警告


# 添加隐藏层
def add_layer(inputs, in_size, out_size, activation_function=None):
    loc_w = tf.Variable(tf.random_normal([in_size, out_size]))
    loc_b = tf.Variable(tf.zeros([1, out_size]) + 0.1)  # 官方推荐biases初始值不为0
    loc_y = tf.matmul(inputs, loc_w) + loc_b
    if activation_function is None:
        loc_outputs = loc_y
    else:
        loc_outputs = activation_function(loc_y)
    return loc_outputs


# 输入数据
x_data = np.linspace(-1, 1, 250)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) + noise

# 数据可视化，输出原始数据
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)  # 参数为输出图框的编号
ax.scatter(x_data, y_data)
plt.ion()
plt.show()

# 创建结构 start
xs = tf.placeholder(tf.float32, [Non

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