Tensorflow Deep MNIST：枯渇したリソース：形状を持つテンソルを割り当てるときのOOM [10000,32,28,28]
これは、私が実行しているサンプルMNISTコードです。
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}))
使用しているGPUはGeForce GTX 750 Ti
エラー：
...
...
...
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
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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
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I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x601a49d00 of size 3328
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I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x6026c7c00 of size 31488
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I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x60334d700 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:674] Chunk at 0x604c49a00 of size 13053184
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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()
同じ問題に関連するgithubの問題（ here 、 here ）を読みましたが、この問題を解決するためにコードを変更する方法を理解できませんでした。
この問題の解決方法を次に示します。エラーとは、精度の評価中にGPUのメモリが不足することを意味します。したがって、より小さなサイズのデータセットが必要です。これは、バッチでデータを使用することで実現できます。したがって、この投稿で述べたように、テストデータセット全体でコードを実行する代わりに、バッチで実行する必要があります： TensorFlow
したがって、このlocの代わりに、テストデータセットの精度評価のために：
print("test accuracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
これを使用できます：
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}))
実行したとき1000 epochs for trainingおよび使用済み10 batches of batch_size = 50 ために accuracy evaluation、次の結果が得られました。
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
Abhijayの答えを補完して、テストミニバッチ全体の平均精度を簡単に取得できます。
accuracy_sum = tf.reduce_sum(tf.cast(correct_prediction, tf.float32))
good = 0
total = 0
for i in xrange(10):
testSet = mnist.test.next_batch(50)
good += accuracy_sum.eval(feed_dict={ x: testSet[0], y_: testSet[1], keep_prob: 1.0})
total += testSet[0].shape[0]
print("test accuracy %g"%(good/total))