在使用tensorflow的object detection時，出現以下報錯
tensorflow Resource exhausted: OOM when allocating tensor with shape
可能的解決方法：
減小訓練的batch大小
tensorflow報錯 tensorflow Resource exhausted: OOM when allocating tensor with shape
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OP_REQUIRES failed at conv_ops.cc:386 : Resource exhausted: OOM when allocating tensor with shape..
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,1424,2128,64]
''tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[?]'' 錯誤分析
tensorflow ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[256,256,15,15] and type float on 報錯（顯存不夠報錯）
【tf.keras】Resource exhausted: OOM when allocating tensor with shape [9216,4096] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Resource exhausted: OOM when allocating tensor with shape[3,3,384,384] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0。。。。。
錯誤：基於tensorflow識別mnist數據集出現ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[10000,32,28,28] and type float on
顯存不夠----ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[4096]
OOM when allocating tensor of shape [] and type float [[node conv2d_224/kernel/Initializer/random_uniform/min (defined at ./intances/utils.py:19) ]]
Tensorflow:If you want to see a list of allocated tensors when OOM happens, add report_tensor_alloca