训练一个简单的Tensorflow神经网络模型

以下代码运行于Google Colaboratory：

import tensorflow as tf
from numpy.random import RandomState

batch_size = 8

# 定义神经网络参数
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))

# 定义输入
x = tf.placeholder(tf.float32, shape=(None, 2), name='x-input')
y_ = tf.placeholder(tf.float32, shape=(None, 1), name='y-input')

# 前向传播
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

# 定义损失函数和反向传播算法
cross_entropy = -tf.reduce_mean(
    y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

# 通过随机数生成一个模拟数据集
rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size, 2)

# 定义规则来给出样本标签
Y = [[int(x1+x2 < 1)] for (x1, x2) in X]

# 创建一个会话
with tf.Session() as sess:
  init_op = tf.global_variables_initializer()
  sess.run(init_op)
  print('w1:', sess.run(w1))
  print('w2:', sess.run(w2))
  
  # 设定训练的轮数
  STEPS = 5000
  for i in range(STEPS):
    
    start = (i * batch_size) % dataset_size
    end = min(start+batch_size, dataset_size)
    
    sess.run(train_step,
            feed_dict={x: X[start:end], y_: Y[start:end]})
    
    # 每隔一段时间计算在所有数据上的交叉熵
    if i % 1000 == 0:
      total_cross_entropy = sess.run(cross_entropy,
                                     feed_dict={x: X, y_: Y})
      print("After %d trainning step(s), cross entropy on all data id %g"
            % (i, total_cross_entropy))
      
  # 训练结束
  print('w1:', sess.run(w1))
  print('w2:', sess.run(w2))

输出结果如下:

w1: [[-0.8113182   1.4845988   0.06532937]
 [-2.4427042   0.0992484   0.5912243 ]]
w2: [[-0.8113182 ]
 [ 1.4845988 ]
 [ 0.06532937]]
After 0 trainning step(s), cross entropy on all data id 0.0674925
After 1000 trainning step(s), cross entropy on all data id 0.0163385
After 2000 trainning step(s), cross entropy on all data id 0.00907547
After 3000 trainning step(s), cross entropy on all data id 0.00714436
After 4000 trainning step(s), cross entropy on all data id 0.00578471
w1: [[-1.9618274  2.582354   1.6820378]
 [-3.4681718  1.0698233  2.11789  ]]
w2: [[-1.8247149]
 [ 2.6854665]
 [ 1.4181951]]