我使用了 tensorflow 教程中的单变量示例，但在优化 Tensorflow 中的多元线性回归问题时遇到了问题。
我正在使用波特兰房价的数据集 here .
我是 Tensorflow 的新手，我确信这里有一些看起来很可怕的东西。
优化似乎根本不起作用。它很快就会爆炸到无穷大。感谢您的帮助。
import tensorflow as tf
import numpy as np
X = np.array( [[ 2.10400000e+03, 3.00000000e+00],
[ 1.60000000e+03, 3.00000000e+00],
[ 2.40000000e+03, 3.00000000e+00],
[ 1.41600000e+03, 2.00000000e+00],
[ 3.00000000e+03, 4.00000000e+00],
[ 1.98500000e+03, 4.00000000e+00],
[ 1.53400000e+03, 3.00000000e+00],
[ 1.42700000e+03, 3.00000000e+00],
[ 1.38000000e+03, 3.00000000e+00],
[ 1.49400000e+03, 3.00000000e+00],
[ 1.94000000e+03, 4.00000000e+00],
[ 2.00000000e+03, 3.00000000e+00],
[ 1.89000000e+03, 3.00000000e+00],
[ 4.47800000e+03, 5.00000000e+00],
[ 1.26800000e+03, 3.00000000e+00],
[ 2.30000000e+03, 4.00000000e+00],
[ 1.32000000e+03, 2.00000000e+00],
[ 1.23600000e+03, 3.00000000e+00],
[ 2.60900000e+03, 4.00000000e+00],
[ 3.03100000e+03, 4.00000000e+00],
[ 1.76700000e+03, 3.00000000e+00],
[ 1.88800000e+03, 2.00000000e+00],
[ 1.60400000e+03, 3.00000000e+00],
[ 1.96200000e+03, 4.00000000e+00],
[ 3.89000000e+03, 3.00000000e+00],
[ 1.10000000e+03, 3.00000000e+00],
[ 1.45800000e+03, 3.00000000e+00],
[ 2.52600000e+03, 3.00000000e+00],
[ 2.20000000e+03, 3.00000000e+00],
[ 2.63700000e+03, 3.00000000e+00],
[ 1.83900000e+03, 2.00000000e+00],
[ 1.00000000e+03, 1.00000000e+00],
[ 2.04000000e+03, 4.00000000e+00],
[ 3.13700000e+03, 3.00000000e+00],
[ 1.81100000e+03, 4.00000000e+00],
[ 1.43700000e+03, 3.00000000e+00],
[ 1.23900000e+03, 3.00000000e+00],
[ 2.13200000e+03, 4.00000000e+00],
[ 4.21500000e+03, 4.00000000e+00],
[ 2.16200000e+03, 4.00000000e+00],
[ 1.66400000e+03, 2.00000000e+00],
[ 2.23800000e+03, 3.00000000e+00],
[ 2.56700000e+03, 4.00000000e+00],
[ 1.20000000e+03, 3.00000000e+00],
[ 8.52000000e+02, 2.00000000e+00],
[ 1.85200000e+03, 4.00000000e+00],
[ 1.20300000e+03, 3.00000000e+00]]
).astype('float32')
y_data = np.array([[ 399900.],
[ 329900.],
[ 369000.],
[ 232000.],
[ 539900.],
[ 299900.],
[ 314900.],
[ 198999.],
[ 212000.],
[ 242500.],
[ 239999.],
[ 347000.],
[ 329999.],
[ 699900.],
[ 259900.],
[ 449900.],
[ 299900.],
[ 199900.],
[ 499998.],
[ 599000.],
[ 252900.],
[ 255000.],
[ 242900.],
[ 259900.],
[ 573900.],
[ 249900.],
[ 464500.],
[ 469000.],
[ 475000.],
[ 299900.],
[ 349900.],
[ 169900.],
[ 314900.],
[ 579900.],
[ 285900.],
[ 249900.],
[ 229900.],
[ 345000.],
[ 549000.],
[ 287000.],
[ 368500.],
[ 329900.],
[ 314000.],
[ 299000.],
[ 179900.],
[ 299900.],
[ 239500.]]
).astype('float32')
m = 47
W = tf.Variable(tf.zeros([2,1]))
b = tf.Variable(tf.zeros([1]))
b = tf.Print(b, [b], "Bias: ")
W = tf.Print(W, [W], "Weights: ")
y = tf.add( tf.matmul(X,W), b)
y = tf.Print(y, [y], "y: ")
loss = tf.reduce_sum(tf.square(y - y_data)) / (2 * m)
loss = tf.Print(loss, [loss], "loss: ")
optimizer = tf.train.GradientDescentOptimizer(.01)
train = optimizer.minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(10):
sess.run(train)
#if i % 20 == 0:
#print(sess.run(W), sess.run(b))
sess.close()
对于输出，我得到以下内容:
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [0 0]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [0]
I tensorflow/core/kernels/logging_ops.cc:79] y: [0 0 0...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [6.5591554e+10]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [38210460 56018.387]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [17020.631]
I tensorflow/core/kernels/logging_ops.cc:79] y: [8.0394994e+10 6.1136921e+10 9.1705295e+10...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [3.373289e+21]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [-3.8223224e+09]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [-8.8281616e+12 -1.2750791e+10]
I tensorflow/core/kernels/logging_ops.cc:79] y: [-1.8574494e+16 -1.4125102e+16 -2.118763e+16...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [1.8006666e+32]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [2.0396713e+18 2.9459613e+15]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [8.8311514e+14]
I tensorflow/core/kernels/logging_ops.cc:79] y: [4.2914781e+21 3.2634836e+21 4.8952214e+21...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [inf]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [-2.040362e+20]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [-4.7124867e+23 -6.8063922e+20]
I tensorflow/core/kernels/logging_ops.cc:79] y: [-9.9150947e+26 -7.5400019e+26 -1.1309991e+27...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [inf]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [4.7140825e+25]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [1.0887797e+29 1.5725587e+26]
I tensorflow/core/kernels/logging_ops.cc:79] y: [2.2907974e+32 1.7420524e+32 2.6130761e+32...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [inf]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [-1.0891484e+31]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [-2.515532e+34 -3.6332629e+31]
I tensorflow/core/kernels/logging_ops.cc:79] y: [-5.2926912e+37 -4.0248632e+37 -6.0372884e+37...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [inf]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [2.5163837e+36]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [inf 8.3943417e+36]
I tensorflow/core/kernels/logging_ops.cc:79] y: [inf inf inf...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [inf]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [-inf]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [-nan -inf]
I tensorflow/core/kernels/logging_ops.cc:79] y: [-nan -nan -nan...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [-nan]
I tensorflow/core/kernels/logging_ops.cc:79] Bias: [-nan]
I tensorflow/core/kernels/logging_ops.cc:79] Weights: [-nan -nan]
I tensorflow/core/kernels/logging_ops.cc:79] y: [-nan -nan -nan...]
I tensorflow/core/kernels/logging_ops.cc:79] loss: [-nan]
我是从源代码构建的，我正在使用 python3，在修复完成后允许这样做。我怀疑这与它有什么关系，但只是想确定一下。我确信这是缺乏用户知识。
最佳答案
你的学习率太高了，所以解决方案是来回跳，越跳越远。
通常，对于此类问题，规范化您的输入范围是一种很好的做法，例如，使它们具有 mean(0) 和 var(1)。
关于python - 无法在 Tensorflow 中优化多元线性回归，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/34167537/