基于Theano的Alexnet_Python_Shell_下载.zip

# AlexNet Implementation with Theano Demonstration of training an AlexNet in Python with Theano. Please see this [technical report](http://arxiv.org/abs/1412.2302) for a high level description. [theano_multi_gpu](https://github.com/uoguelph-mlrg/theano_multi_gpu) provides a toy example on how to use 2 GPUs to train a MLP on the mnist data. If you use this in your research, we kindly ask that you cite the above report: ```bibtex @article{ding2014theano, title={Theano-based Large-Scale Visual Recognition with Multiple GPUs}, author={Ding, Weiguang and Wang, Ruoyan and Mao, Fei and Taylor, Graham}, journal={arXiv preprint arXiv:1412.2302}, year={2014} } ``` ## Dependencies * [numpy](http://www.numpy.org/) * [Theano](http://deeplearning.net/software/theano/) * [Pylearn2](http://deeplearning.net/software/pylearn2/) * [PyCUDA](http://mathema.tician.de/software/pycuda/) * [zeromq](http://zeromq.org/bindings:python) * [hickle](https://github.com/telegraphic/hickle) ## How to run ### Prepare raw ImageNet data Download [ImageNet dataset](http://www.image-net.org/download-images) and unzip image files. ### Preprocess the data This involves shuffling training images, generating data batches, computing the mean image and generating label files. #### Steps * Set paths in the preprocessing/paths.yaml. Each path is described in this file. * Run preprocessing/generate_data.sh, which will call 3 python scripts and do all the mentioned steps. It runs for about 1~2 days. For a quick trial of the code, run preprocessing/generate_toy_data.sh, which takes ~10 minutes and proceed. preprocessing/lists.txt is a static file that lists what files should be created by running generate_data.sh. ### Train AlexNet #### Set configurations config.yaml contains common configurations for both the 1-GPU and 2-GPU version. spec_1gpu.yaml and spec_2gpu.yaml contains different configurations for the 1-GPU and 2-GPU version respectively. If you changed preprocessing/paths.yaml, make sure you change corresponding paths in config.yaml, spec_1gpu.yaml and spec_2gpu.yaml accordingly. #### Start training 1-GPU version, run: THEANO_FLAGS=mode=FAST_RUN,floatX=float32 python train.py 2-GPU version, run: THEANO_FLAGS=mode=FAST_RUN,floatX=float32 python train_2gpu.py Validation error and loss values are stored as weights_dir/val_record.npy Here we do not set device to gpu in THEANO_FLAGS. Instead, users should control which GPU(s) to use in spec_1gpu.yaml and spec_2gpu.yaml. ### Pretrained AlexNet Pretrained AlexNet weights and configurations can be found at [pretrained/alexnet](https://github.com/uoguelph-mlrg/theano_alexnet/tree/master/pretrained/alexnet) ## Acknowledgement *Frédéric Bastien*, for providing the example of [Using Multiple GPUs](https://github.com/Theano/Theano/wiki/Using-Multiple-GPUs) *Lev Givon*, for helping on inter process communication between 2 gpus with PyCUDA, Lev's original script https://gist.github.com/lebedov/6408165 *Guangyu Sun*, for help on debugging the code