|
1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162 |
- # LeNet Example
-
- ## Description
-
- Training LeNet with MNIST dataset in MindSpore.
-
- This is the simple and basic tutorial for constructing a network in MindSpore.
-
- ## Requirements
-
- - Install [MindSpore](https://www.mindspore.cn/install/en).
-
- - Download the MNIST dataset at <http://yann.lecun.com/exdb/mnist/>. The directory structure is as follows:
-
- ```
- └─MNIST_Data
- ├─test
- │ t10k-images.idx3-ubyte
- │ t10k-labels.idx1-ubyte
- │
- └─train
- train-images.idx3-ubyte
- train-labels.idx1-ubyte
- ```
-
- ## Running the example
-
- ```python
- # train LeNet, hyperparameter setting in config.py
- python train.py --data_path MNIST_Data
- ```
-
- You will get the loss value of each step as following:
-
- ```bash
- 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
- ```python
- # evaluate LeNet, after 1 epoch training, the accuracy is up to 96.5%
- python eval.py --data_path MNIST_Data --mode test --ckpt_path checkpoint_lenet-1_1875.ckpt
- ```
-
- ## Note
- Here are some optional parameters:
-
- ```bash
- --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.
|