import numpy as np
import tensorflow as tf
# 获取训练、测试数据
def get_data(number):
list_x = []
list_label = []
for i in range(number):
x = np.random.randn(1)
# 构建数据满足 y = 2x + 10
label = 2 * x + np.random.randn(1)*0.01 + 10
list_x.append(x)
list_label.append(label)
return list_x, list_label
def inference(x):
weight = tf.get_variable("weight", [1])
bias = tf.get_variable("bias", [1])
y = x * weight + bias
return y
def main():
train_x = tf.placeholder(tf.float32)
train_label = tf.placeholder(tf.float32)
test_x = tf.placeholder(tf.float32)
test_label = tf.placeholder(tf.float32)
with tf.variable_scope("inference"):
train_y = inference(train_x)
tf.get_variable_scope().reuse_variables()
test_y = inference(test_x)
train_loss = tf.square(train_y - train_label)
test_loss = tf.square(test_y - test_label)
opt = tf.train.GradientDescentOptimizer(0.002)
train_op = opt.minimize(train_loss)
init = tf.global_variables_initializer()
train_data_x, train_data_label = get_data(1000)
test_data_x, test_data_label = get_data(1)
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
sess.run(train_op, feed_dict={train_x: train_data_x[i],
train_label: train_data_label[i]})
if i % 10 == 0:
test_loss_value = sess.run(test_loss, feed_dict={test_x:test_data_x[0],
test_label:test_data_label[0]})
print("step %d eval loss is %.3f" % (i, test_loss_value))
if __name__ == "__main__":
main()
