出现这个的原因是gpu显存不够导致的，一般是我们设置了程序根据需求增长导致的
这里，我们设置 allow_growth=False 就可以控制显存使用的增长，从而控制显存的使用，而不会程序运行一半报错。
这种情况是建立在我们的显存比较小的情况下使用的策略，如果说我们显存够大则不用担心这种问题。
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显存不够----ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[4096]
错误：基于tensorflow识别mnist数据集出现ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[10000,32,28,28] and type float on
tensorflow报错 tensorflow 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[?]'' 错误分析
OOM when allocating tensor of shape [] and type float [[node conv2d_224/kernel/Initializer/random_uniform/min (defined at ./intances/utils.py:19) ]]
【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。。。。。
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Y' with dtype float
OP_REQUIRES failed at conv_ops.cc:386 : Resource exhausted: OOM when allocating tensor with shape..