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ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[682112,156]
時間 2020-12-24
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圖像分類
keras
gpu
cuda
tensorflow
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運行出錯結果如下: 出錯原因主要和自己的算法有關,在圖像識別的程序中,我的filters的數量分配如下: Filters的數量越多,同一張圖片經過這些filters提取特徵後佔用顯卡內存數量越大,經過這兩層衆多數量的filter卷積後,佔用的內存極大。另外: 圖片經過這些filters提取特徵後緩存在顯卡內存中,經過flaten展平,在經過一層包含1024個神經元的神經網絡學習,每一批(batch
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相關文章
1.
ResourceExhaustedError: OOM when allocating tensor with shape[10,256,400,528]
2.
[顯存被佔滿,程序無法運行問題]ResourceExhaustedError (see above for traceback): OOM when allocating tensor
3.
''tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with sh
4.
Error:OOM when allocating tensor with shape[......]
5.
【tf.keras】Resource exhausted: OOM when allocating tensor with shape [9216,4096] and type float on /j
6.
OOM when allocating tensor with shape[300,14,14,256] and type float on /job:localhost/replica:0/tas
7.
TensorFlow 報錯:InvalidArgumentError (see above for traceback): tags and values not the same shape: [
8.
Tensorflow:NotFoundError (see above for traceback): Key style_weight/Variable not found in checkpoin
9.
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs
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InternalError (see above for traceback): Blas GEMM launch failed已解決
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