我们生成一组带有高斯分布噪声的数据点集，总共300个点。搭建两层的神经网络，通过训练神经网络，使loss减小，达到一个理想的网络模型。

import tensorflow as tf
import numpy as np

def add_layer(inputs, in_size, out_size, activation_function=None):
    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

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

xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

prediction = add_layer(l1, 10, 1, activation_function=None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                     reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)


init = tf.global_variables_initializer()  # 替换成这样就好


sess = tf.Session()
sess.run(init)

for i in range(3000):
    # training
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})

    if i % 50 == 0:
        # to see the step improvement
        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))