Tensorflow Deep MNIST:资源枯竭:分配具有形状的张量时为OOM [10000,32,28,28] [英] Tensorflow Deep MNIST: Resource exhausted: OOM when allocating tensor with shape[10000,32,28,28]
本文介绍了Tensorflow Deep MNIST:资源枯竭:分配具有形状的张量时为OOM [10000,32,28,28]的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
这是我正在运行的示例MNIST代码:
This is the sample MNIST code I am running:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.initialize_all_variables()
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
with tf.Session(config = config) as s:
sess.run(init)
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
我正在使用的GPU是:GeForce GTX 750 Ti
The GPU I am using is: GeForce GTX 750 Ti
错误:
...
...
...
step 19900, training accuracy 1
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (256): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (512): Total Chunks: 1, Chunks in use: 0 768B allocated for chunks. 1.20MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (1024): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (2048): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (4096): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (8192): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (16384): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (32768): Total Chunks: 1, Chunks in use: 0 36.8KiB allocated for chunks. 4.79MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (65536): Total Chunks: 1, Chunks in use: 0 78.5KiB allocated for chunks. 4.79MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (131072): Total Chunks: 1, Chunks in use: 0 200.0KiB allocated for chunks. 153.1KiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (262144): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (524288): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (1048576): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (2097152): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (4194304): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (8388608): Total Chunks: 1, Chunks in use: 0 11.86MiB allocated for chunks. 390.6KiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (16777216): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (33554432): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (67108864): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (134217728): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:639] Bin (268435456): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:656] Bin for 957.03MiB was 256.00MiB, Chunk State:
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a40000 of size 1280
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a40500 of size 1280
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a40a00 of size 31488
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a48500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a48600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a48700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a48800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a48900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a48a00 of size 4096
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a49a00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a49b00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a49c00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a49d00 of size 3328
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a4aa00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a4ab00 of size 204800
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a7cb00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a7cc00 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026bcc00 of size 4096
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026bdc00 of size 40960
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026c7c00 of size 31488
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cf700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cf800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cf900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cfa00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cfb00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cfc00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cfd00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cfe00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026cff00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026d0000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026d0100 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026d0500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026d0600 of size 3328
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026d1300 of size 40960
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026db300 of size 80128
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x602702600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x602734700 of size 204800
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x603342700 of size 4096
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x603343700 of size 3328
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334d700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334d800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334d900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334da00 of size 3328
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334e700 of size 3328
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334f400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334f500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334f600 of size 204800
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x603381600 of size 204800
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6033b3600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6033b3700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6033b3800 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x603ff3800 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c33800 of size 4096
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c34800 of size 4096
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c35800 of size 40960
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c3f800 of size 40960
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c49800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c49900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c49a00 of size 13053184
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6058bc700 of size 31360000
I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6076a4b00 of size 1801385216
I tensorflow/core/common_runtime/bfc_allocator.cc:683] Free at 0x6026d0200 of size 768
I tensorflow/core/common_runtime/bfc_allocator.cc:683] Free at 0x6026eec00 of size 80384
I tensorflow/core/common_runtime/bfc_allocator.cc:683] Free at 0x602702700 of size 204800
I tensorflow/core/common_runtime/bfc_allocator.cc:683] Free at 0x602766700 of size 12435456
I tensorflow/core/common_runtime/bfc_allocator.cc:683] Free at 0x603344400 of size 37632
I tensorflow/core/common_runtime/bfc_allocator.cc:689] Summary of in-use Chunks by size:
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 32 Chunks of size 256 totalling 8.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 2 Chunks of size 1280 totalling 2.5KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 5 Chunks of size 3328 totalling 16.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 5 Chunks of size 4096 totalling 20.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 2 Chunks of size 31488 totalling 61.5KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 4 Chunks of size 40960 totalling 160.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 1 Chunks of size 80128 totalling 78.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 4 Chunks of size 204800 totalling 800.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 3 Chunks of size 12845056 totalling 36.75MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 1 Chunks of size 13053184 totalling 12.45MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 1 Chunks of size 31360000 totalling 29.91MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:692] 1 Chunks of size 1801385216 totalling 1.68GiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] Sum Total of in-use chunks: 1.76GiB
I tensorflow/core/common_runtime/bfc_allocator.cc:698] Stats:
Limit: 1898266624
InUse: 1885507584
MaxInUse: 1885907712
NumAllocs: 2387902
MaxAllocSize: 1801385216
W tensorflow/core/common_runtime/bfc_allocator.cc:270] **********************************************************xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
W tensorflow/core/common_runtime/bfc_allocator.cc:271] Ran out of memory trying to allocate 957.03MiB. See logs for memory state.
W tensorflow/core/framework/op_kernel.cc:968] Resource exhausted: OOM when allocating tensor with shape[10000,32,28,28]
Traceback (most recent call last):
File "trainer_deepMnist.py", line 109, in <module>
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 559, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3648, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 710, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 908, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 958, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 978, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.ResourceExhaustedError: OOM when allocating tensor with shape[10000,32,28,28]
[[Node: Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Variable_2/read)]]
Caused by op u'Conv2D', defined at:
File "trainer_deepMnist.py", line 61, in <module>
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
File "trainer_deepMnist.py", line 46, in conv2d
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 394, in conv2d
data_format=data_format, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 703, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2320, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1239, in __init__
self._traceback = _extract_stack()
我阅读了一些github问题(此处，
I read some github issues (here, here) related to the same problem but could not understand how I should change my code to solve this problem.
推荐答案
这是我解决此问题的方法:该错误表示在准确性评估期间GPU内存不足.因此，它需要一个较小的数据集，可以通过批量使用数据来实现.因此，与其在整个测试数据集上运行代码，不如本文所述，它需要分批运行:
Here is how I solved this problem: the error means that the GPU runs out of memory during accuracy evaluation. Hence it needs a smaller sized dataset, which can be achieved by using data in batches. So, instead of running the code on the whole test dataset it needs to be run in batches as mentioned in this post: How to read data in batches when using TensorFlow
因此，用于测试数据集的准确性评估，而不是此位置:
Hence, for accuracy evaluation on test dataset, instead of this loc :
print("test accuracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
可以使用:
for i in xrange(10):
testSet = mnist.test.next_batch(50)
print("test accuracy %g"%accuracy.eval(feed_dict={ x: testSet[0], y_: testSet[1], keep_prob: 1.0}))
当我为training运行1000 epochs并将batch_size = 50的10 batches用于accuracy evaluation时，我得到以下结果:
When i ran 1000 epochs for training and used 10 batches of batch_size = 50 for accuracy evaluation, I got the following results:
step 0, training accuracy 0.04
step 100, training accuracy 0.88
step 200, training accuracy 0.9
step 300, training accuracy 0.88
step 400, training accuracy 0.94
step 500, training accuracy 0.96
step 600, training accuracy 0.94
step 700, training accuracy 0.96
step 800, training accuracy 0.9
step 900, training accuracy 1
test accuracy 1
test accuracy 0.92
test accuracy 1
test accuracy 1
test accuracy 0.94
test accuracy 0.96
test accuracy 0.92
test accuracy 0.96
test accuracy 0.92
test accuracy 0.94
这篇关于Tensorflow Deep MNIST:资源枯竭:分配具有形状的张量时为OOM [10000,32,28,28]的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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