import tensorflow as tf

from numpy.random import RandomState

batch_size = 8

# 设置占位符

x = tf.placeholder(tf.float32, shape=[None, 2], name='X-input')

y = tf.placeholder(tf.float32, shape=[None, 1], name='y-input')

# 定义单层神经网络向前传播过程,初始化权值和偏置

w1 = tf.Variable(tf.random_normal([2, 1], stddev=1, seed=1))

y_predict = tf.matmul(x, w1)

# 自定义损失函数

loss_less = 10

loss_more = 1

loss = tf.reduce_sum(tf.where(tf.greater(y, y_predict), (y - y_predict) * loss_more, (y_predict - y) * loss_less))

# 优化

trian_step = tf.train.AdamOptimizer(0.0001).minimize(loss)

# 随机生成模拟数据集

rdm = RandomState(1)

data_size = 128

X = rdm.rand(data_size, 2) # 128个样本

# 加入噪音

Y = [[x1 + x2 + rdm.rand() / 10.0 - 0.05] for (x1, x2) in X]

# 训练

with tf.Session() as sess:

init_op = tf.global_variables_initializer()

sess.run(init_op)

for i in range(5000):

start = (i * batch_size) % data_size

end = min(start + batch_size, data_size)

sess.run(trian_step, feed_dict={x: X[start:end], y: Y[start:end]})

print(sess.run(w1))