小编luo*_*hao的帖子

我想指定运行我的进程的gpu.我将其设置如下:

import tensorflow as tf
with tf.device('/gpu:0'):
    a = tf.constant(3.0)
with tf.Session() as sess:
    while True:
        print sess.run(a)

但是它仍然在我的两个gpus中分配内存.

|    0      7479    C   python                         5437MiB 
|    1      7479    C   python                         5437MiB 

当我开始训练一些神经网络时,它遇到了CUDA_ERROR_OUT_OF_MEMORY但是训练可以继续进行而没有错误.因为我想使用gpu内存,所以我设置了gpu_options.allow_growth = True.日志如下:

I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] 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:951] Found device 0 with properties: …

出于某种原因,我想使用一些先前版本的tensorflow('tensorflow - ** - .whl',而不是github上的源代码),我在哪里可以下载以前的版本,如何知道相应cuda version的兼容性.

(春天mvc)首先我不知道下面的写作是否正确.如果它是对的,那么我不明白@autowired是如何在这里工作的.如果它是错的,那么当我有更多时我该怎么做比一个类实现一个接口.

public interface UserDao{
    public User findUserByUserName(String username);
}

public class UserDaoImpl1 implements UserDao{

    @Override
    public User findUserByUserName(String username){
            .......
    }
}

public class UserDaoImpl2 implements UserDao{
    @Override
    public User findUserByUserName(String username){
            .......
    }
}

@Service
public class UserServiceImpl implements UserService{

    @Autowired
    private UserDao userDao;//how does atuowired work here?

    @Override
    public User loginCheck(User user){
                ......
        }
}

    [xx_xx@xxxx ~]$ python multiply.py
Traceback (most recent call last):
  File "multiply.py", line 2, in <module>
    import tensorflow as tf
  File "/home/luohao/.usr/bin/python2.7.10/lib/python2.7/site-packages/tensorflow/__init__.py", line 4, in <module>
    from tensorflow.python import *
  File "/home/luohao/.usr/bin/python2.7.10/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 22, in <module>
    from tensorflow.python.client.client_lib import *
  File "/home/luohao/.usr/bin/python2.7.10/lib/python2.7/site-packages/tensorflow/python/client/client_lib.py", line 35, in <module>
    from tensorflow.python.client.session import InteractiveSession
  File "/home/luohao/.usr/bin/python2.7.10/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 11, in <module>
    from tensorflow.python import pywrap_tensorflow as tf_session
  File "/home/luohao/.usr/bin/python2.7.10/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 28, in <module>
    _pywrap_tensorflow = swig_import_helper()
  File "/home/luohao/.usr/bin/python2.7.10/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 24, in swig_import_helper
    _mod = imp.load_module('_pywrap_tensorflow', fp, …

在我的神经网络中,我创建了一些tf.Variable对象如下:

weights = {
    'wc1_0': tf.Variable(tf.random_normal([5, 5, 3, 64])),
    'wc1_1': tf.Variable(tf.random_normal([5, 5, 3, 64]))
}
biases = {
    'bc1_0': tf.Variable(tf.constant(0.0, shape=[64])),
    'bc1_1': tf.Variable(tf.constant(0.0, shape=[64]))
}

我将如何保存变量weights和biases不保存其它变量的具体数量的迭代之后？

在重复阅读多次后,我仍然在FCN中的"移位和缝合"技巧上挣扎.有人可以给出一些直觉的解释.