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Training LeNet with dataset in MindSpore.
This is the simple and basic tutorial for constructing a network in MindSpore.
Install MindSpore.
Download the dataset, the directory structure is as follows:
└─Data
├─test
│ t10k-images.idx3-ubyte
│ t10k-labels.idx1-ubyte
│
└─train
train-images.idx3-ubyte
train-labels.idx1-ubyte
# train LeNet, hyperparameter setting in config.py
python train.py --data_path Data
You will get the loss value of each step as following:
epoch: 1 step: 1, loss is 2.3040335
...
epoch: 1 step: 1739, loss is 0.06952668
epoch: 1 step: 1740, loss is 0.05038793
epoch: 1 step: 1741, loss is 0.05018193
...
Then, evaluate LeNet according to network model
# evaluate LeNet
python eval.py --data_path Data --ckpt_path checkpoint_lenet-1_1875.ckpt
Here are some optional parameters:
--device_target {Ascend,GPU,CPU}
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.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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