我设法在我的服务器上安装#DeepDream。
我有两个核心和2GB Ram。但需要1分钟才能处理尺寸为100kbp的图像。
有什么建议吗？
答案 0 :(得分：0)
除非您可以转移到更好的工作站/获取GPU，否则您将需要调整图像大小。
img = PIL.Image.open('sky1024px.jpg')
img = np.float32(img.resize( [int(0.5 * s) for s in img.size] ))
答案 1 :(得分：0)
Taking 1 minute to process a 100kb image is a sensible turnaround time for #deepdream, and we accept that these renders have an incredibly long baking time. Often, experimental research software will run too slow, hungry for a future of faster computers. That said, there are a couple ways that come to mind about making your setup execute faster.
Thread! Increase thread count with a command line argument. Here's one way to enable multi-threading in Caffe How to enable multithreading with Caffe?
GPU! Install CUDA and switch from CPU rendering to GPU rendering. If your server doesn't have a special GPU, try getting a GPU instance on amazon ec2. https://github.com/BVLC/caffe/wiki/Install-Caffe-on-EC2-from-scratch-(Ubuntu,-CUDA-7,-cuDNN)
答案 2 :(得分：0)
根据经验，深度学习对计算和内存资源都很困难。 2GB RAM Core Duo机器不是深度学习的好选择。请记住，许多开创这一领域的人使用GTX Titan卡进行了大量研究，因为在训练深度学习网络时，甚至在至强服务器上的CPU计算也非常缓慢。