Use of ‘model.eval()’ and ‘with torch.no_grad()’ in PyTorch model evaluate
Using the designated settings for training model.train() and evaluation model.eval() will automatically set the mode for the dropout layer and batch normalization layers and rescale appropriately so that we do not have to worry about that at all.
How to calculate running loss using loss.item() in PyTorch?
you could just sum it and calculate the mean after the epoch finishes or at the end of the epoch, we divide by the number of steps(dataset size). It gives you the correct average sample loss for this particular epoch. This training loss is used to see, “how well your model performs on the training dataset”.
Advantage of using LogSoftmax vs Softmax vs Crossentropyloss in PyTorch
The workaround is to use log probability instead of probability, which takes care to make the calculation numerically stable. The reformulated version allows us to evaluate softmax with only small numerical errors even when z contains extremely large or extremely negative numbers.
How to create a Contiguous tensor in Pytorch?
Contiguous tensors are convenient because we can visit them efficiently in order without jumping around in the storage. It improves data locality and improves performance because of the way memory access works on modern CPUs. This advantage of course depends on the way algorithms visit.
Advantages of ReLU vs Tanh vs Sigmoid activation function in deep neural networks.
The saturated neurons can kill gradients if we’re too positive or too negative of an input. They’re also not zero-centered and so we get these, this inefficient kind of gradient update. The third problem is an exponential function. This is a little bit computationally expensive.
Find Correlation between features and target using the correlation matrix.
You can evaluate the relationship between each feature and target using a correlation and selecting those features that have the strongest relationship with the target variable. Such as methods that remove redundant variables using correlation.
Plot two overlay Histograms on single chart with Pandas and Matplotlib.
We’ll look at a real-world example with data that I’ll load from a CSV file and also we’re going to learn how to draw overlapping histograms using Pandas and Matplotlib. Before we go and set up how we create the chart let’s look at what the end results would be.