テンソルフロー：割り当てようとしてメモリが不足しました
Tensor Flowバージョン0.7.1、64ビットGPU対応、pipをインストールし、Ubuntu14.04を搭載したPCで実行しています。私の問題は、私の計算に基づくと、GPUに十分なスペースがあるはずなのに、ネットワークを構築するときにTensorFlowのメモリが不足していることです。
以下は、Tensor FlowMNISTチュートリアルに基づいた私のコードの最小限の例です。ネットワークは2層の完全に接続されたネットワークであり、隠れ層のノード数は変数nによって定義されます。トレーニングミニバッチのサイズは1です。これが私のコードです。
n = 23000
mnist = read_data_sets('MINST_Data', one_hot=True)
session = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [None, 784])
W1 = tf.Variable(tf.truncated_normal([784, n], stddev=0.1))
b1 = tf.Variable(tf.constant(0.1, shape=[n]))
nn1 = tf.matmul(x, W1) + b1
W2 = tf.Variable(tf.truncated_normal([n, 10], stddev=0.1))
b2 = tf.Variable(tf.constant(0.1, shape=[10]))
nn2 = tf.matmul(nn1, W2) + b2
y = tf.nn.softmax(nn2)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(1)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
ここで、n <= 22000の場合、ネットワークは正常に実行されます。ただし、n >= 23000の場合、次のエラーが発生します。
W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:211] Ran out of memory trying to allocate 877.38MiB. See logs for memory state
W tensorflow/core/kernels/cwise_ops_common.cc:56] Resource exhausted: OOM when allocating tensor with shape[10000,23000]
しかし、私の計算によれば、メモリに問題はないはずです。ネットワーク内のパラメータの数は次のとおりです。
First layer weights: 784 * n
First layer biases: n
Second layer weights: 10 * n
Second layer biases: 10
Total: 795n + 10
したがって、n = 23000を使用し、float32データを使用すると、ネットワークに必要な合計メモリは73.1MBになります。
現在、私のグラフィックカードはNVIDIA GeForce GTX 780 Tiで、3072MBのメモリを搭載しています。グラフィックカードを見つけた後、TensorFlowは以下を出力します。
Total memory: 3.00GiB
Free memory: 2.32GiB
したがって、約2.32 GBのメモリが使用可能であるはずです。これは、上記で計算された73.1MBよりはるかに大きいです。ミニバッチサイズは1なので、これによる影響は最小限です。なぜこのエラーが発生するのですか？
また、NVida GeForce GTX 880MGPUを搭載したラップトップでもこれを試しました。ここで、TensorFlowはFree memory: 7.60GiBを読み取ります。上記と同じコードを実行すると、n = 700,000付近でメモリエラーが発生します。これは、2.2GBに相当します。これはもう少し理にかなっており、私のPCコードが壊れた時点よりもかなり高くなっています。しかし、なぜそれが7.6GBのマークに近づかないのかは私にはまだ不可解です。
n = 23000を使用してPCで上記のコードを実行しているときの、TensorFlowからの完全な出力は次のとおりです。
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GTX 780 Ti
major: 3 minor: 5 memoryClockRate (GHz) 1.0455
pciBusID 0000:01:00.0
Total memory: 3.00GiB
Free memory: 2.32GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:717] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 780 Ti, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 2.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 4.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 8.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 16.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 32.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 64.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 128.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 256.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 512.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 2.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 4.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 8.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 16.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 32.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 64.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 128.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 256.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 512.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.00GiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 2.00GiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 4.00GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:717] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 780 Ti, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:73] Allocating 2.03GiB bytes.
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:83] GPU 0 memory begins at 0xb04720000 extends to 0xb86295000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] Bin (32768): 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/gpu/gpu_bfc_allocator.cc:431] Bin (65536): 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/gpu/gpu_bfc_allocator.cc:431] Bin (131072): 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] Bin (524288): Total Chunks: 2, Chunks in use: 0 819.0KiB 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] Bin (8388608): 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] Bin (134217728): Total Chunks: 1, Chunks in use: 0 68.79MiB allocated for chunks. 29.91MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:431] 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/gpu/gpu_bfc_allocator.cc:431] Bin (536870912): 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/gpu/gpu_bfc_allocator.cc:431] Bin (1073741824): 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/gpu/gpu_bfc_allocator.cc:431] Bin (2147483648): 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/gpu/gpu_bfc_allocator.cc:431] Bin (4294967296): 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/gpu/gpu_bfc_allocator.cc:450] Bin for 877.38MiB was 1.00GiB, Chunk State:
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d239400 of size 80128
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d1d7600 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d24cd00 of size 438528
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d1d7500 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb1a3e3200 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb1a302800 of size 920064
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb15d58800 of size 920064
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb08cf7500 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb04736b00 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d2b7f00 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb15e39200 of size 72128000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb08c16b00 of size 920064
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb15c61500 of size 92160
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb04736d00 of size 72128000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d2b8100 of size 72128000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb15c4ad00 of size 92160
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb04736a00 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d2b7e00 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d1d7900 of size 400128
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb04720200 of size 92160
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb04736c00 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb08cf7600 of size 72128000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb1a3e3300 of size 1810570496
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d1c0c00 of size 92160
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb08c00300 of size 92160
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d2b8000 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d1d7800 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb04720100 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d1d7700 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb04720000 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb0d1d7400 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb11781700 of size 72128000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb15c77d00 of size 256
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:465] Chunk at 0xb15c77e00 of size 920064
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:468] Summary of in-use Chunks by size:
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 16 Chunks of size 256 totalling 4.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 1 Chunks of size 80128 totalling 78.2KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 5 Chunks of size 92160 totalling 450.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 1 Chunks of size 400128 totalling 390.8KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 1 Chunks of size 438528 totalling 428.2KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 4 Chunks of size 920064 totalling 3.51MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 5 Chunks of size 72128000 totalling 343.93MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:471] 1 Chunks of size 1810570496 totalling 1.69GiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:475] Sum Total of in-use chunks: 2.03GiB
W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:211] Ran out of memory trying to allocate 877.38MiB. See logs for memory state
W tensorflow/core/kernels/cwise_ops_common.cc:56] Resource exhausted: OOM when allocating tensor with shape[10000,23000]
W tensorflow/core/common_runtime/executor.cc:1102] 0x50f40e0 Compute status: Resource exhausted: OOM when allocating tensor with shape[10000,23000]
[[Node: add = Add[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](MatMul, Variable_1/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x3234d30 Compute status: Resource exhausted: OOM when allocating tensor with shape[10000,23000]
[[Node: add = Add[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](MatMul, Variable_1/read)]]
[[Node: range_1/_13 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="Edge_97_range_1", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x3234d30 Compute status: Resource exhausted: OOM when allocating tensor with shape[10000,23000]
[[Node: add = Add[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](MatMul, Variable_1/read)]]
[[Node: Cast/_11 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="Edge_96_Cast", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Traceback (most recent call last):
File "/home/jrowlay/Projects/Tensor_Flow_Tutorial/MNIST_CNN_Simple/memory_test.py", line 232, in <module>
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 315, in run
return self._run(None, fetches, feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 511, in _run
feed_dict_string)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 564, in _do_run
target_list)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 586, in _do_call
e.code)
tensorflow.python.framework.errors.ResourceExhaustedError: OOM when allocating tensor with shape[10000,23000]
[[Node: add = Add[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](MatMul, Variable_1/read)]]
[[Node: range_1/_13 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="Edge_97_range_1", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op u'add', defined at:
File "/home/jrowlay/Projects/Tensor_Flow_Tutorial/MNIST_CNN_Simple/memory_test.py", line 215, in <module>
nn1 = tf.matmul(x, W1) + b1
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 468, in binary_op_wrapper
return func(x, y, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 44, in add
return _op_def_lib.apply_op("Add", x=x, y=y, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2040, 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 1087, in __init__
self._traceback = _extract_stack()
エラーから判断すると、tensorflow OOMは_[10000, 23000]_サイズのテンソルを割り当てようとしました。 10,000がたまたまMNISTテストセットの例の数であることを考えると、テストセット全体を一度に評価しようとする評価コードがあると仮定します。アクティベーションのためだけに10000 * (784 + n + 10) ~= 1GBが必要ですが、それだけではOOMするのに十分ではありません。しかし、説明が難しい何らかの理由で割り当てられた1.7GBのテンソルもあります。
ラップトップの場合、計算にいくつかの変数がありません。 Adam 各変数の1番目と2番目の瞬間を追跡します したがって、2.2GBは3倍になって6.6GBになります。メモリにあり、そのOOMを説明するグラデーションにオーバーヘッドを追加します。
申し訳ありませんが、これではあなたの質問に完全には答えられません。コメントとして追加したと思いますが、まだ評判がありません。
ご参考まで。 MacbookProでも同じエラーが発生しました。他のいくつかのアプリケーションを閉じた後、その問題はなくなりました。しかし、私はまだ次のような他のエラーが発生しました：
Blockquote W tensorflow/core/common_runtime/bfc_allocator.cc：217]214.51MiBを割り当てようとしてメモリが不足しました。呼び出し元は、これは障害ではないことを示していますが、より多くのメモリが使用可能であれば、パフォーマンスが向上する可能性があることを意味している可能性があります。
つまり、これは実際のメモリ不足の問題です。
同じエラーが発生しました。jupyternotebookからプログラムを再起動するだけで、正常に実行されます。まだ理由がわかりません。単一のsession = tf.InteractiveSession()を実行しても、同じエラーが表示されます。それが役に立てば幸い。