Training the model¶ Now, let’s write a general function to train a model. Here, we will illustrate: Scheduling the learning rate; Saving the best model; In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler. predict pretrained_model.eval() pretrained_model.freeze() y_hat = pretrained_model(x). Restoring Training State¶. If you don't just want to load weights, but instead restore the full training, do the following

When using pretrained models, PyTorch sets the model to be unfrozen (will have its weights adjusted) by default. So we'll be training the whole model: # Setting up the model # load in pretrained and reset final fully connected res_mod = models.resnet34(pretrained=True) num_ftrs = res_mod.fc.in_features res_mod.fc = nn.Linear(num_ftrs, 2)

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