ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[682112,156]
运行出错结果如下： 出错原因主要和自己的算法有关，在图像识别的程序中，我的filters的数量分配如下： Filters的数量越多，同一张图片经过这些filters提取特征后占用显卡内存数量越大，经过这两层众多数量的filter卷积后，占用的内存极大。另外： 图片经过这些filters提取特征后缓存在显卡内存中，经过flaten展平，在经过一层包含1024个神经元的神经网络学习，每一批（batch
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- 1. ResourceExhaustedError: OOM when allocating tensor with shape[10,256,400,528]
- 2. [显存被占满，程序无法运行问题]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 报错：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
- 10. InternalError (see above for traceback): Blas GEMM launch failed已解决