Tensorflow Deep MNIST: Ressource épuisée: MOO lors de l'allocation du tenseur avec la forme [10000,32,28,28]
Voici l'exemple de code MNIST que j'utilise:
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}))
Le GPU que j'utilise est: GeForce GTX 750 Ti
Erreur:
...
...
...
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.
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I tensorflow/core/common_runtime/bfc_allocator.cc:656] Bin for 957.03MiB was 256.00MiB, Chunk State:
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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
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I tensorflow/core/common_runtime/bfc_allocator.cc:692] 5 Chunks of size 4096 totalling 20.0KiB
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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()
J'ai lu certains problèmes de github ( ici , ici ) liés au même problème mais je ne pouvais pas comprendre comment je devais changer mon code pour résoudre ce problème.
Voici comment j'ai résolu ce problème: l'erreur signifie que le GPU manque de mémoire lors de l'évaluation de la précision. Par conséquent, il a besoin d'un ensemble de données de plus petite taille, ce qui peut être réalisé en utilisant des données par lots. Ainsi, au lieu d'exécuter le code sur l'ensemble de données de test, il doit être exécuté par lots, comme mentionné dans cet article: Comment lire les données par lots lors de l'utilisation de TensorFlow
Par conséquent, pour l'évaluation de la précision sur l'ensemble de données de test, au lieu de ce loc:
print("test accuracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
cela peut être utilisé:
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}))
Quand j'ai couru 1000 epochs pour training et utilisé 10 batches de batch_size = 50 pour accuracy evaluation, J'ai obtenu les résultats suivants:
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
En complément de la réponse d'Abhijay, vous pouvez facilement obtenir la précision moyenne à travers les minibatches de test
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))