early-stopping-pytorch:提前停止PyTorch

# Early Stopping for PyTorch Early stopping is a form of regularization used to avoid overfitting on the training dataset. Early stopping keeps track of the validation loss, if the loss stops decreasing for several epochs in a row the training stops. The ```EarlyStopping``` class in ```pytorchtool.py``` is used to create an object to keep track of the validation loss while training a [PyTorch](https://pytorch.org/) model. It will save a checkpoint of the model each time the validation loss decrease. We set the ```patience``` argument in the ```EarlyStopping``` class to how many epochs we want to wait after the last time the validation loss improved before breaking the training loop. There is a simple example of how to use the ```EarlyStopping``` class in the [MNIST_Early_Stopping_example](MNIST_Early_Stopping_example.ipynb) notebook. Underneath is a plot from the example notebook, which shows the last checkpoint made by the EarlyStopping object, right before the model started to overfit. It had patience set to 20.  ## Usage You can run this project directly in the browser by clicking this button: [](https://mybinder.org/v2/gh/Bjarten/early-stopping-pytorch/master), or you can clone the project to your computer and install the required pip packages specified in the requirements text file. ``` pip install -r requirements.txt ``` ## References The ```EarlyStopping``` class in ```pytorchtool.py``` is inspired by the [ignite EarlyStopping class](https://github.com/pytorch/ignite/blob/master/ignite/handlers/early_stopping.py).