Category Archives: PyTorch

The PyTorch model is mutable if we change any of the two models this action will have a direct impact on the other model too, as they both point to the same object reference in memory.

The weight matrix defined the linear function this basically demonstrates how the network mapping changes as the weights are updated during the training process when we update the weight we are changing the function.

We want to convert this to a tensor of size bs x ch, so we take the average over the last two dimensions and flatten the trailing 1×1 dimension as we did in our previous model. 

The total number of parameters is shown at the end, which is equal to the number of trainable and non-trainable parameters. In this model, all the layers are trainable. 

Create a custom Dataset and DataLoader to preprocess the time series like data into a matrix-like shape. We reshape the data in that way to just illustrate the point. 

The softmax activation function transforms a vector of K real values into values between 0 and 1 so that they can be interpreted as probabilities. The input values can be positive, negative, zero, or greater than one.

Forcing your network to learn redundant representations might sound very inefficient. But in practice, it makes things more robust and prevents overfitting. It also makes your network act as if taking the consensus over an ensemble of networks.

A custom collate_fn can be used to customize collation, e.g., padding sequential data to a max length of a batch.collate_fn is called with a list of data samples at each time. It is expected to collate the input samples into a batch for yielding from the data loader iterator. 

The pre-trained model is “frozen” and only the weights of the classifier get updated during training. In this case, the convolutional base extracted all the features associated with each image

In this tutorial, we’ll discuss what regularization is and when and why it may be helpful to add it to our model.