mindspore/example/alexnet_cifar10

AlexNet Example

Description

Training AlexNet with CIFAR-10 dataset in MindSpore.

This is the simple tutorial for training AlexNet in MindSpore.

Requirements

• Install MindSpore.

• Download the CIFAR-10 dataset at http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz. The directory structure is as follows:

├─cifar-10-batches-bin
│
└─cifar-10-verify-bin

Running the example

# train AlexNet, hyperparameter setting in config.py
python train.py --data_path cifar-10-batches-bin

You will get the loss value of each step as following:

epoch: 1 step: 1, loss is 2.2791853
...
epoch: 1 step: 1536, loss is 1.9366643
epoch: 1 step: 1537, loss is 1.6983616
epoch: 1 step: 1538, loss is 1.0221305
...

Then, evaluate AlexNet according to network model

# evaluate AlexNet, 1 epoch training accuracy is up to 51.1%; 10 epoch training accuracy is up to 81.2%
python eval.py --data_path cifar-10-verify-bin --mode test --ckpt_path checkpoint_alexnet-1_1562.ckpt

Note

Here are some optional parameters:

--device_target {Ascend,GPU}
                     device where the code will be implemented (default: Ascend)
--data_path DATA_PATH
                     path where the dataset is saved
--dataset_sink_mode DATASET_SINK_MODE
                     dataset_sink_mode is False or True

You can run python train.py -h or python eval.py -h to get more information.