Category Archives: PyTorch

You need to reset the weights of the model so that each cross-validation fold starts from some random initial state and not learning from the previous folds. You could call reset_parameters() on all child modules.

A macro-average will compute the metric independently for each class and then take the average hence treating all classes equally, whereas a micro-average will aggregate the contributions of all classes to compute the average metric.

The optimal learning rate will be dependent on both your model architecture and your dataset. While using a default learning rate may provide decent results, you can often improve the performance or speed up training by searching for an optimal learning rate.

We use something called samplers for OverSampling. Though we did not use samplers exclusively, PyTorch used it for us internally. When we say shuffle=False, PyTorch ended up using SequentialSampler it gives an index from zero to the length of the dataset. When shuffle=True it ends up using a RandomSampler.

classification layer of the pre-trained model is specific to the original classification task, and subsequently specific to the set of classes on which the model was trained. You simply add a new classifier layer, which will be trained from scratch.

how to create a loss function for an imbalanced dataset in which minority class proportionally to its underrepresentation. You will use PyTorch to define the loss function and class weights to help the model learn from the imbalanced data.

Here, we will be able to see our network graph. One of TensorBoard’s strengths is its ability to visualize complex model structures.

How to Collect and review metrics during the training of your deep learning models and how to plots from the data collected during training

The network training converges faster if its weights are linearly transformed to have zero means and unit variances.

We’re gonna check instant m if it’s convolution layer then we can initialize with a variety of different initialization techniques we’re just gonna do the kaiming_uniform_ on the weight of that specific module and we’re only gonna do if it’s a conv2d.