ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[682112,156]
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: [
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- 9. InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs
- 10. InternalError (see above for traceback): Blas GEMM launch failed已解決