我现在正试图在我的实验中使用GMM。但我有以下问题。我对这个错误很困惑。
import tensorflow as tf
class GMMDataLoader:
def __init__(self, points, batch_size):
self.points = points
self.batch_size = batch_size
num_points = points.shape[0]
self.num_points = num_points
dim = points.shape[1]
self.count = 0
#self.x = tf.constant(self.points)
print ('Loaded in a total of %d points, the dimension is %d'%(num_points, dim))
def next_batch(self, batch_size=128):
self.count += 1
count = self.count + 1
print ('batch [%d]'%count)
num_points = self.num_points
x = tf.constant(self.points)
indices = tf.random_uniform(tf.constant([batch_size]),
minval=0, maxval=num_points-1,
dtype=tf.int32,
seed=10)
return tf.gather(x, indices), None
import numpy as np
x = np.random.random((10000, 2048)).astype('float32')
loader = GMMDataLoader(x, 128)
gmm_model = tf.contrib.factorization.GMM(initial_clusters='random', num_clusters=100, random_seed=666)
gmm_model.fit(input_fn=loader.next_batch)
当我运行此代码时，出现以下错误：
Loaded in a total of 10000 points, the dimension is 2048
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp19vzg37k
WARNING:tensorflow:From /u/usr/usr/anaconda2/envs/py35/lib/python3.5/site-packages/tensorflow/contrib/factorization/python/ops/gmm_ops.py:59: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /u/usr/usr/anaconda2/envs/py35/lib/python3.5/site-packages/tensorflow/contrib/factorization/python/ops/gmm_ops.py:353: calling reduce_logsumexp (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /u/usr/usr/anaconda2/envs/py35/lib/python3.5/site-packages/tensorflow/contrib/factorization/python/ops/gmm_ops.py:377: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /u/usr/usr/anaconda2/envs/py35/lib/python3.5/site-packages/tensorflow/contrib/factorization/python/ops/gmm.py:170: get_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.get_global_step
2018-01-21 13:25:54.515678: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-01-21 13:25:55.440734: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1062] Found device 0 with properties:
name: Tesla P100-SXM2-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.4805
pciBusID: 0000:89:00.0
totalMemory: 15.89GiB freeMemory: 15.60GiB
2018-01-21 13:25:56.339431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1062] Found device 1 with properties:
name: Tesla P100-SXM2-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.4805
pciBusID: 0000:8a:00.0
totalMemory: 15.89GiB freeMemory: 15.60GiB
2018-01-21 13:25:56.339489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1077] Device peer to peer matrix
2018-01-21 13:25:56.339527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1083] DMA: 0 1
2018-01-21 13:25:56.339536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1093] 0: Y Y
2018-01-21 13:25:56.339543: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1093] 1: Y Y
2018-01-21 13:25:56.339564: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1152] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Tesla P100-SXM2-16GB, pci bus id: 0000:89:00.0, compute capability: 6.0)
2018-01-21 13:25:56.339574: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1152] Creating TensorFlow device (/device:GPU:1) -> (device: 1, name: Tesla P100-SXM2-16GB, pci bus id: 0000:8a:00.0, compute capability: 6.0)
2018-01-21 13:28:44.093288: I tensorflow/core/kernels/cuda_solvers.cc:159] Creating CudaSolver handles for stream 0x564187890370
Segmentation fault
任何人都知道如何修复它？
答案 0 :(得分：0)
这是由于数据样本和高斯之间的距离计算中的内存分配过多。
已提交修复程序，很快就会提供。您可以通过在此处对其进行注释来禁用Tensorflow图的那一部分：