机器学习 - save和load训练好的模型

如果已经训练好了一个模型，你就可以save和load这模型。

For saving and loading models in PyTorch, there are three main methods you should be aware of.

PyTorch method	What does it do?
`torch.save`	Saves a serialized object to disk using Python’s `pickle` utility. Models, tensors and various other Python objects like dictionaries can be saved using `torch.save`
`torch.load`	Uses pickle’s unpickling features to deserialize and load pickled Python object files (like models, tensors or dictionaries) into memory. You can also set which device to load the object to (CPU, GPU etc)
`torch.nn.Module.load_state_dict`	Loads a model’s parameter dictionary (`model.state_dict()`) using a saved `state_dict()` object

在 PyTorch 中，pickle 是一个用于序列化和反序列化Python对象的标准库模块。它可以将Python对象转换为字节流 (即序列化)，并将字节流转换回Python对象 (即反序列化)。pickle模块在很多情况下都非常有用，特别是在保存和加载模型，保存训练中间状态等方面。
在深度学习中，经常需要保存训练好的模型或者训练过程中的中间结果，以便后续的使用或分析。PyTorch提高了方便的API来保存和加载模型，其中就包括了使用pickle模块进行对象的序列化和反序列化。

save model

import torch
from pathlib import Path # 1. Create models directory
MODEL_PATH = Path("models")
MODEL_PATH.mkdir(parents = True, exist_ok = True)# 2. Create model save path
MODEL_NAME = "trained_model.pth"
MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME# 3. Save the model state dict 
print(f"Saving model to: {MODEL_SAVE_PATH}")
torch.save(obj = model_0.state_dict(),f = MODEL_SAVE_PATH)

就能看到 trained_model.pth 文件下载到所属的文件夹位置。

Load the saved PyTorch model
You can load it in using torch.nn.Module.load_state_dict(torch.load(f)) where f is the filepath of the saved model state_dict().

Why call torch.load() inside torch.nn.Module.load_state_dict()?
Because you only saved the model’s state_dict() which is a dictionary of learned parameters and not the entire model, you first have to load the state_dict() with torch.load() and then pass that state_dict() to a new instance of the model (which is a subclass of nn.Module).

# Instantiate a new instance of the model 
loaded_model_0 = LinearRegressionModel()# Load the state_dict of the saved model
loaded_model_0.load_state_dict(torch.load(f=MODEL_SAVE_PATH))# 结果如下
<All keys matched successfully>

测试 loaded model。

# Put the loaded model into evaluation model 
loaded_model_0.eval() # 2. Use the inference mode context manager to make predictions
with torch.inference_mode():loaded_model_preds = loaded_model_0(X_test)# Compare previous model predictions with loaded model predictions
print(y_preds == loaded_model_preds) # 结果如下
tensor([[True],[True],[True],[True],[True],[True],[True],[True],[True],[True]])