Train CNN over Cifar-10¶
Convolution neural network (CNN) is a type of feed-forward artificial neural network widely used for image and video classification. In this example, we will train three deep CNN models to do image classification for the CIFAR-10 dataset,
AlexNet the best validation accuracy (without data augmentation) we achieved was about 82%.
VGGNet, the best validation accuracy (without data augmentation) we achieved was about 89%.
ResNet, the best validation accuracy (without data augmentation) we achieved was about 83%.
Alexnet from Caffe, SINGA is able to convert model from Caffe seamlessly.
Users can compile and install SINGA from source or install the Python version. The code can ran on both CPU and GPU. For GPU training, CUDA and CUDNN (V4 or V5) are required. Please refer to the installation page for detailed instructions.
The binary Cifar-10 dataset could be downloaded by
python download_data.py bin
The Python version could be downloaded by
python download_data.py py
There are four training programs
train.py. The following command would train the VGG model using the python version of the Cifar-10 dataset in ‘cifar-10-batches-py’ folder.
python train.py vgg cifar-10-batches-py
To train other models, please replace ‘vgg’ to ‘alexnet’, ‘resnet’ or ‘caffe’, where ‘caffe’ refers to the alexnet model converted from Caffe. By default the training would run on a CudaGPU device, to run it on CppCPU, add an additional argument
python train.py vgg cifar-10-batches-py --use_cpu
alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,
alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices. The two devices run synchronously to compute the gradients of the mode parameters, which are averaged on the host CPU device and then be applied to update the parameters.
vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.
predict.py includes the prediction function
def predict(net, images, dev, topk=5)
The net is created by loading the previously trained model; Images consist of a numpy array of images (one row per image); dev is the training device, e.g., a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.
The predict.py file’s main function provides an example of using the pre-trained alexnet model to do prediction for new images. The ‘model.bin’ file generated by the training program should be placed at the cifar10 folder to run