所有的Tensorflow输出都是nan

Question

import tensorflow as tf 

# Model parameters 
A = tf.Variable([.3], dtype=tf.float32) 
W = tf.Variable([.3], dtype=tf.float32) 
b = tf.Variable([-.3], dtype=tf.float32) 
# Model input and output 
x = tf.placeholder(tf.float32) 
q_model = A * (x**2) + W * x + b 
y = tf.placeholder(tf.float32) 

# loss 
loss = tf.reduce_sum(tf.square(q_model - y)) # sum of the squares 
# optimizer 
optimizer = tf.train.GradientDescentOptimizer(0.01) 
train = optimizer.minimize(loss) 

# training data 
x_train = [0, 1, 2, 3, 4] 
y_train = [0, 1, 4, 9, 16] 
# training loop 
init = tf.global_variables_initializer() 
sess = tf.Session() 
sess.run(init) # reset values to wrong 
for i in range(1000): 
    sess.run(train, {x: x_train, y: y_train}) 

# evaluate training accuracy 
curr_A, curr_W, curr_b, curr_loss = sess.run([A, W, b, loss], {x: x_train, y: y_train}) 
print("A: %s W: %s b: %s loss: %s"%(curr_A, curr_W, curr_b, curr_loss)) 

在他们的网站上，tf给出了执行线性回归的模型代码。但是，我想玩弄一下，看看我是否也可以做到二次回归。为此，我添加了一个tf.Variable A，将其放入模型中，然后修改输出以告诉我它的值是多少。

下面是结果：

A: [ nan] W: [ nan] b: [ nan] loss: nan 

什么你们都以为是这里的问题？它在椅子和键盘之间吗？

Answer 1

如果您为每次迭代打印A,W和b的值，则会看到它们是交替的（即正值和负值相互跟随）。这通常是由于学习率很高。在你的榜样，你应该能够通过减少学习率约0.001来解决此问题：

optimizer = tf.train.GradientDescentOptimizer(0.001) 

有了这个学习率，我实现了减少损失，而A倾向于1和W和b趋于如预期的那样为零。

A: [ 0.7536] W: [ 0.42800003] b: [-0.26100001] loss: 7.86113 
A: [ 0.8581112] W: [ 0.45682004] b: [-0.252166] loss: 0.584708 
A: [ 0.88233441] W: [ 0.46283191] b: [-0.25026742] loss: 0.199126 
... 
A: [ 0.96852171] W: [ 0.1454313] b: [-0.11387932] loss: 0.0183883 
A: [ 0.96855479] W: [ 0.14527865] b: [-0.11376046] loss: 0.0183499 
A: [ 0.96858788] W: [ 0.14512616] b: [-0.11364172] loss: 0.0183113 
A: [ 0.9686209] W: [ 0.14497384] b: [-0.1135231] loss: 0.0182731