optimizer.py

# import modules
import torch.nn as nn
import torch.optim as optim

# define optimizer
# using previous model that we have built
# using Adam optimizer and learning rate = 0.001
optimizer = optim.Adam(model.parameters(), lr=1e-3)

print(optimizer)

# define loss function
# if the task is regression probem we can use Mean Squared Error
loss_fn = nn.MSELoss()

# if the task is binary classification problem, we can use BCELoss
loss_fn = nn.BCELoss()

# if the task is multiclass classification problem, we can use CrossEntropy
loss_fn = nn.CrossEntropyLoss()