Tensorflow Deep MNIST: Resource exhausted: OOM when allocating tensor with shape[10000,32,28,28]
今天测试卷积神经网络报了如题所示的错误，我跑的代码如下。
- 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}))
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}))然后错误大致如下
- 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
- 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
- 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()
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
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
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() 原因是GPU OOM，没法分配那么多显存来搞定accuracy evaluation，因此需要改成批处理。
TensorFlow给出的原因解释：
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.
解决方案：把最后那句print换成我这的三行，分批print，就没问题了。
- 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}))
for i in xrange(10): 若报错 可将xrange改为range
testSet = mnist.test.next_batch(50)
print("test accuracy %g"%accuracy.eval(feed_dict={ x: testSet[0], y_: testSet[1], keep_prob: 1.0}))tensorflow搬运工：
https://stackoverflow.com/questions/39076388/tensorflow-deep-mnist-resource-exhausted-oom-when-alloc