**PyTorch**, model weights are the learnable parameters of a neural network model that are updated during the training process. They enable the model to create/make accurate predictions. These weights are stored in the “state_dict” of the model. PyTorch provides various functions to save and load model weights.

This blog will illustrate the method of saving and loading particular model weights in PyTorch.

**How to Save and Load Specific Model Weights in PyTorch?**

To save and load particular model weights in PyTorch, follow the provided steps:

- Import PyTorch library and modules
- Build the model and define weights
- Save model weights using the “torch.save()” method
- Load model weights using the “load_state_dict()” method

**Step 1: Import PyTorch Library and Modules**

First, import the desired libraries and modules. For instance, we have imported the main “**PyTorch**” library, and pre-trained model architectures from the “**torchvision**” and “**torch.nn**” modules for building neural network layers and architectures:

import torchvision.models as models

import torch.nn as nn

**Step 2: Build the Model and Define Weights**

Then, create a specific model and define weights. For instance, we have used the “**ResNet-50**” model and initialized it with weights pre-trained on the “**ImageNet**” dataset using the “**IMAGENET1K_V2**”:

mod_weights='IMAGENET1K_V2',

fine_tune=False,

num_classes=10

):

model = models.resnet50(weights=mod_weights)

model.fc = nn.Linear(in_feat=2048, out_feat=num_classes)

return model

**Step 3: Save Model Weights Using the “torch.save()” Method**

To save model weights, load pre-trained weights from the defined model. Then, use the “**torch.save()**” function to save the “state_dict” (which includes the weights) of the model to the specific file. For instance, we are saving weights to a file called “**savedModel_weights.pth**”:

torch.save(model.state_dict(), 'savedModel_weights.pth')

Upon doing so, the “**savedModel_weights.pth**” file is created in our current directory as seen below:

The model weights have been saved successfully.

**Step 4: Load Model Weights**

To load model weights, create/make an instance of the exact model, and then load the parameters by utilizing the “**load_state_dict()**” method:

We have efficiently explained the method to save and load model weights in PyTorch.

**Conclusion**

To save and load model weights in PyTorch, first, import the desired PyTorch library and modules. Then, build a model and define weights. Next, save model weights to a specific file using the “torch.save()” method and finally load it using the “load_state_dict()” method. This blog has illustrated the method of saving and loading model weights in PyTorch.