content/en/_sources/docs/model_zoo/examples/cifar10/README.md.txt

<!--
    Licensed to the Apache Software Foundation (ASF) under one
    or more contributor license agreements.  See the NOTICE file
    distributed with this work for additional information
    regarding copyright ownership.  The ASF licenses this file
    to you under the Apache License, Version 2.0 (the
    "License"); you may not use this file except in compliance
    with the License.  You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing,
    software distributed under the License is distributed on an
    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    KIND, either express or implied.  See the License for the
    specific language governing permissions and limitations
    under the License.
-->
# 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,

1. [AlexNet](https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg)
the best validation accuracy (without data augmentation) we achieved was about 82%.

2. [VGGNet](http://torch.ch/blog/2015/07/30/cifar.html), the best validation accuracy (without data augmentation) we achieved was about 89%.
3. [ResNet](https://github.com/facebook/fb.resnet.torch), the best validation accuracy (without data augmentation) we achieved was about 83%.
4. [Alexnet from Caffe](https://github.com/BVLC/caffe/tree/master/examples/cifar10), SINGA is able to convert model from Caffe seamlessly.


## Instructions


### SINGA installation

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.

### Data preparation

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

### Training

There are four training programs

1. 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

2. alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,

        ./run.sh

3. 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.

        ./run-parallel.sh

4. vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.

### Prediction

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

        python predict.py