Recent Posts by Pragati
How to change the learning rate in the PyTorch using Learning Rate Scheduler?
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.
Filters, kernel size, input shape in Conv2d layer
We don’t explicitly define the filters that our convolutional layer will use, instead parameterize the filters and let the network learn the best filters to use during training. We need to define “how many filters we’ll use at each layer”.
How to deal with an imbalanced dataset using WeightedRandomSampler in PyTorch.
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.
How to modify pre-train PyTorch model for Finetuning and Feature Extraction?
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 save Keras training History object to File using Callback?
You can learn a lot about Keras models by observing their History objects after training. In this post, you will discover how you can save the history object into a CSV file of deep learning models training metrics over time during training.
How to calculate the number of parameters for a Convolutional and Dense layer in Keras?
we need to understand whether or not the layer contains biases for each layer. If it is, then we simply add the number of biases. The number of biases will be equal to the number of nodes(filters) in the layer.