optimizer.py

#Import the optim module from the pytorch package
import torch.optim as optim

#Initialize an optimizer object
learning_rate = 0.001
optimizer = optim.Adam(net.parameters(), lr=learning_rate)

#Set the parameter gradients to 0 and take a step (as part of a training loop)
for epoch in num_epochs:
train(...)
  optimizer.zero_grad()
  optimizer.step()

svm.py

#Import the support vector machine module from the sklearn framework
from sklearn import svm

#Label x and y variables from our dataset
x = ourData.features
y = ourData.labels

#Initialize our algorithm
classifier = svm.SVC()

#Fit model to our data
classifier.fit(x,y)