tensorflow运行python崩溃

时间：2017-04-12 03:15:47

标签： python tensorflow

tensorflow：tensorflow-gpu 0.12

Anaconda：anaconda4.2.9（python3.5）

GPU：Nvidia 940M（笔记本电脑）（2GB）

操作系统：win7-64bit sp1

Cuda：8.0

cudnn：5.0

IDE：pycharm

在GPU（CNN）下进行Mnist测试，但是当涉及到我自己的项目时，python崩溃。我调试我的代码并找到函数“**session.run()**”导致这个问题。错误是：

E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:586] Could not identify NUMA node of /job:localhost/replica:0/task:0/gpu:0, defaulting to 0.  Your kernel may not have been built with NUMA support.
E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_event.cc:49] Error polling for event status: failed to query event: CUDA_ERROR_LAUNCH_FAILED
F c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_event_mgr.cc:198] Unexpected Event status: 1
E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_dnn.cc:385] could not create cudnn handle: **CUDNN_STATUS_INTERNAL_ERROR**
E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] could not destroy cudnn handle: **CUDNN_STATUS_BAD_PARAM**
F c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\kernels\conv_ops.cc:532] **Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)**

由于Mnist运行正常，因此我的GPU驱动程序cuda和cudnn没有任何缺陷。我真的不知道这个问题是怎么来的。

这是我的代码：

import cv2
import os
import tensorflow as tf
import data_trans as dt


with tf.variable_scope('weights'):
    weights={
        # 60*60*3->60*60*32->30*30*32
        'conv1':tf.get_variable('conv1',[5,5,3,32],initializer=tf.contrib.layers.xavier_initializer_conv2d()),
        # 30*30*32->30*30*64->15*15*64
        'conv2':tf.get_variable('conv2',[5,5,32,64],initializer=tf.contrib.layers.xavier_initializer_conv2d()),
        # 15*15*64->12*12*128->6*6*128
        'conv3':tf.get_variable('conv3',[4,4,64,128],initializer=tf.contrib.layers.xavier_initializer_conv2d()),
         # 6*6*128->256
        'fc1':tf.get_variable('fc1',[6*6*128,256],initializer=tf.contrib.layers.xavier_initializer()),
        # 256->2
        'fc2':tf.get_variable('fc2',[256,2],initializer=tf.contrib.layers.xavier_initializer())
}
with tf.variable_scope('biases'):
    biases = {
        'conv1':tf.get_variable('conv1',[32,],initializer=tf.constant_initializer(value=0.0,dtype=tf.float32)),
        'conv2':tf.get_variable('conv2',[64,],initializer=tf.constant_initializer(value=0.0,dtype=tf.float32)),
        'conv3':tf.get_variable('conv3',[128,],initializer=tf.constant_initializer(value=0.0,dtype=tf.float32)),
        'fc1':tf.get_variable('fc1',[256,],initializer=tf.constant_initializer(value=0.0,dtype=tf.float32)),
        'fc2':tf.get_variable('fc2',[2,],initializer=tf.constant_initializer(value=0.0,dtype=tf.float32))
    }

def inference(images):
    images = (tf.cast(images,tf.float32)/255)

    conv1 = tf.nn.bias_add(tf.nn.conv2d(images,weights['conv1'],strides=[1,1,1,1],padding='SAME'),biases['conv1'])
    relu1 = tf.nn.relu(conv1)
    pool1 = tf.nn.max_pool(relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

    conv2 = tf.nn.bias_add(tf.nn.conv2d(pool1,weights['conv2'],strides=[1,1,1,1],padding='SAME'),biases['conv2'])
    relu1 = tf.nn.relu(conv2)
    pool2 = tf.nn.max_pool(relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

    conv3 = tf.nn.bias_add(tf.nn.conv2d(pool2,weights['conv3'],strides=[1,1,1,1],padding='VALID'),biases['conv3'])
    relu3 = tf.nn.relu(conv3)
    pool3 = tf.nn.max_pool(relu3,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID')

    flatten = tf.reshape(pool3,[-1,weights['fc1'].get_shape().as_list()[0]])
    drop = tf.nn.dropout(flatten,0.5)
    fc1 = tf.matmul(drop,weights['fc1']) + biases['fc1']
    fc_relu1 = tf.nn.relu(fc1)
    fc2 = tf.matmul(fc_relu1,weights['fc2']) + biases['fc2']
    return fc2


def train():
    dt.encode_to_tfrecords('../train_data/train.txt','../train_data','data.tfrecords',(60,60))        
    image,label = dt.decode_from_tfrecords('../train_data/data.tfrecords')
    batch_image,batch_label = dt.get_batch(image,label,batch_size=10,crop_size=60)

    inf = inference(batch_image)
    predicts = tf.nn.softmax(inf)
    cross_entropy = -tf.reduce_mean(batch_label * tf.log(predicts))
    train_step = tf.train.GradientDescentOptimizer(1e-2).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(predicts, 1), tf.argmax(batch_label, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    sess = tf.InteractiveSession()
    sess.run(tf.global_variables_initializer())
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    #if os.path.exists(os.path.join('model','model.ckpt')) is True:
    #    tf.train.Saver(max_to_keep=None).restore(sess,os.path.join('model','model.ckpt'))
    for epcho in range(8):
        print(sess.run(accuracy))
    print('here!')
    coord.request_stop()
    coord.join(threads)

train()

data_trans.py包含三个用于将图像转换为tfrecords的函数：

import cv2
import tensorflow as tf


def encode_to_tfrecords(label_file,data_root,new_name='data.tfrecords',resize=None):
    writer = tf.python_io.TFRecordWriter(data_root + '/' + new_name)
    num_example = 0
    with open(label_file,'r') as f:
        for l in f.readlines():
            l = l.split()
            path = data_root+'/'+l[0]
            image = cv2.imread(path)
            if resize is not None:
                image = cv2.resize(image,resize)
            height,width,nchannel = image.shape
            label = int(l[1])

            example = tf.train.Example(features=tf.train.Features(feature={
                'height':tf.train.Feature(int64_list=tf.train.Int64List(value=[height])),
                'width':tf.train.Feature(int64_list=tf.train.Int64List(value=[width])),
                'nchannel':tf.train.Feature(int64_list=tf.train.Int64List(value=[nchannel])),
                'image':tf.train.Feature(bytes_list=tf.train.BytesList(value=[image.tobytes()])),
                'label':tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))
                }))
            serialized = example.SerializeToString()
            writer.write(serialized)
            num_example += 1
    print(label_file,'Sample_Num:',num_example)
    writer.close()


#encode_to_tfrecords('../train_data/train.txt','../train_data')

def decode_from_tfrecords(filename,num_epoch=None):
    filename_queue = tf.train.string_input_producer([filename],num_epoch)
    reader = tf.TFRecordReader()
    _,serialized = reader.read(filename_queue)
    example = tf.parse_single_example(serialized,features={
        'height':tf.FixedLenFeature([],tf.int64),
        'width':tf.FixedLenFeature([],tf.int64),
        'nchannel':tf.FixedLenFeature([],tf.int64),
        'image':tf.FixedLenFeature([],tf.string),
        'label':tf.FixedLenFeature([],tf.int64)
        })
    label = tf.cast(example['label'],tf.int32)
    image = tf.decode_raw(example['image'],tf.uint8)
    image = tf.reshape(image,tf.stack([
        tf.cast(example['height'],tf.int32),
        tf.cast(example['width'],tf.int32),
        tf.cast(example['nchannel'],tf.int32)
        ]))
    return image, label

#encode_to_tfrecords("../train_data/train.txt","../train_data",'data.tfrecords')
#image,label=decode_from_tfrecords('../train_data/data.tfrecords')
#print image[0]


def get_batch(image,label,batch_size,crop_size):
    distorted_image = tf.random_crop(image,[crop_size, crop_size, 3])
    distorted_image = tf.image.random_flip_up_down(distorted_image)
    images,label_batch = tf.train.shuffle_batch([distorted_image,label],batch_size=batch_size,capacity=130,min_after_dequeue=100)
    return images,tf.one_hot(tf.reshape(label_batch,[batch_size]), 2)

1 个答案:

答案 0 :(得分：0)

全部谢谢。我已经解决了这个问题。它似乎是windows（7/10）中的tensorflow-gpu中的一个错误.function＆＃34; tf.one_hot（）＆＃34;在win7（也许是tensorflow-gpu0.12＆amp; win7）下无法正确执行，我们必须显式设置由cpu执行的此函数，如：

tf.device('/cpu:0'): 
    tf.one_hot()
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