If it is set to False, then it is not updated and is said to be "frozen".Ĭonv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)īatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True) By default it is True, and the network updates it in every iteration. requires_grad which defines whether a parameter is trained or frozen. parameters() function to access the parameters/weights of any layer. This lets us look at the contents/layers of a model. Now, let's take a look at the contents of a resnet18. (If interested in knowing more, read this - ). Gains : 1) It will be faster, 2) We need lesser images of cats and bicycles. We could start by taking this model, and train it to learn car v/s bicycle. Admittedly, neither of these 3 look like cars or bicycles. But, given the majority of work already out there, it's easy to find a model trained to identify things like Dogs, cats, and humans. Now, I could potentially gather images of both categories and train a network from scratch. Suppose, I want to train a dataset to learn to differentiate between a car and a bicycle. We could potentially start the training from scratch as well, but it would be like re-inventing the wheel. When fine tuning a model, we are basically taking a model trained on Dataset A, and then training it on a new Dataset B. By freeze we mean that we want the parameters of those layers to be fixed. Let us first explore this model's layers and then make a decision as to which ones we want to freeze.
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