Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Imports
For the sake of organization, all libraries needed throughout the code used in any
given chapter are imported at its very beginning. For this chapter, we’ll need the
following imports:
import numpy as np
from PIL import Image
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, random_split, \
TensorDataset
from torchvision.transforms import Compose, ToTensor, Normalize, \
Resize, ToPILImage, CenterCrop, RandomResizedCrop
from torchvision.datasets import ImageFolder
from torchvision.models import alexnet, resnet18, inception_v3
from torchvision.models.alexnet import model_urls
from torchvision.models.utils import load_state_dict_from_url
from stepbystep.v3 import StepByStep
Transfer Learning
In the previous chapter, I called a model fancier just because it had not one, but
two convolutional blocks, and dropout layers as well. Truth be told, this is far from
fancy—really fancy models have tens of convolutional blocks and other neat
architectural tricks that make them really powerful. They have many million
parameters and require not only humongous amounts of data but also thousands
of (expensive) GPU hours for training.
I don’t know about you, but I have neither! So, what’s left to do? Transfer learning
to the rescue!
The idea is quite simple. First, some big tech company, which has access to virtually
infinite amounts of data and computing power, develops and trains a huge model
for their own purpose. Next, once it is trained, its architecture and the
Transfer Learning | 499