failed to allocate **M (** bytes) from device: CUDA_ERROR_OUT_OF_MEMORY，错误原因及解决方案

错误原因：GPU资源占用太大

config = tf.ConfigProto(allow_soft_placement=True) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7) config.gpu_options.allow_growth = True

找到如下红的的这句话，在这之前加上如上三行代码，在session前约束占用空间。能够使得tensorflow占用资源降至70%，当然也可以改为50%，而不是整个服务器GPU

with tf.Session() as sess: 将其改为 with tf.Session(config=config) as sess:

最终改为如下结构就可以啦

config = tf.ConfigProto(allow_soft_placement=True) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7) config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: