我使用TensorFlow的while_loop算法处理大矩阵，但最近发现出现了奇怪的问题：每次运行结果都不同，有时甚至出现了nan值。我花了一些时间来缩小问题的范围，现在有了以下最小示例：我取一个大小为15000x15000的由1填充的大矩阵K，然后对一个由1填充的向量u计算K⁵u。经过一次迭代后，我期望得到一个由15000填充的向量作为结果。但实际情况并非如此。
import numpy as np
import tensorflow as tf
n = 15000
np_kernel_mat = np.ones((n, n), dtype=np.float32)
kernel_mat = tf.constant(np_kernel_mat)
# for debugging
def compare_kernel(kernel_matrix):
print("AverageDifference:" + str(np.average(np.abs(np_kernel_mat - kernel_matrix))))
print("AmountDifferent:" + str(np.count_nonzero(np.abs(np_kernel_mat - kernel_matrix))))
return True
# body of the loop
def iterate(i, u):
# for debugging
with tf.control_dependencies(tf.py_func(compare_kernel, [kernel_mat], [tf.bool])):
u = tf.identity(u)
# multiply
u = tf.matmul(kernel_mat, u)
# check result and kernel
u = tf.Print(u, [tf.count_nonzero(tf.abs(kernel_mat-np_kernel_mat))], "AmountDifferentKernel: ")
u = tf.Print(u, [tf.count_nonzero(tf.abs(u-float(n)))], "AmountDifferentRes: ")
i = i + 1
return i, u
def cond(i, u):
return tf.less(i, 5)
u0 = tf.fill((n, 1), 1.0, name='u0')
iu_0 = (tf.constant(0), u0)
iu_final = tf.while_loop(cond, iterate, iu_0, back_prop=False, parallel_iterations=1)
u_res = iu_final[1]
with tf.Session() as sess:
kernel_mat_eval, u_res_eval = sess.run([kernel_mat, u_res])
print(np.array_equal(kernel_mat_eval, np_kernel_mat))
现在运行此命令会得到以下输出：
I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX TITAN X major: 5 minor: 2 memoryClockRate(GHz): 1.076
pciBusID: 0000:00:0f.0
totalMemory: 11.93GiB freeMemory: 11.81GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
I tensorflow/core/common_runtime/gpu/gpu_device.cc:929] 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0: N
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11435 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:00:0f.0, compute capability: 5.2)
minimal_example.py:25: RuntimeWarning: invalid value encountered in subtr[8/281]
print("AverageDifference:" + str(np.average(np.abs(np_kernel_mat - kernel_matr
ix))))
/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:70: RuntimeWarning
: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims)
AverageDifference:nan
minimal_example.py:26: RuntimeWarning: invalid value encountered in subtract
print("AmountDifferent:" + str(np.count_nonzero(np.abs(np_kernel_mat - kernel_
matrix))))
AmountDifferent:4096
AmountDifferentKernel: [0]
AmountDifferentRes, DifferenceRes: [4][inf]
AverageDifference:nan
AmountDifferent:4096
AmountDifferentKernel: [0]
AmountDifferentRes, DifferenceRes: [15000][nan]
AverageDifference:nan
AmountDifferent:4096
AmountDifferentKernel: [0]
AmountDifferentRes, DifferenceRes: [15000][nan]
AverageDifference:nan
...
15000，但这并不能解释为什么差异为NaN。在CPU上，一切都正常（差异大约为2e08）。现在我的问题是：为什么Print操作的输出与py_func打印的输出不同？为什么矩阵的评估再次等于原始矩阵？为什么我在不同的运行中得到不同的结果？有人能够复现这个问题吗？我正在运行Ubuntu 16.04，TensorFlow 1.8，numpy 1.14，python3.6。GPU是GeForceGTX 1080。NVRM version: NVIDIA UNIX x86_64 Kernel Module 390.48 Thu Mar 22 00:42:57 PDT 2018
GCC
version: gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.9)
float64可以大大减少这种误差。请注意，您在最后一次迭代中计算的数字约为7.6×10²⁰，因此1.3×10¹⁵的误差是“相对较小的”（float32通常精确到大约7个小数位，但我想这种误差会随着迭代而积累）。 - jdehesa