Hence, we do it here if necessary !pip install -quiet pytorch-lightning> = 1.4 set () # Progress bar from tqdm.notebook import tqdm # PyTorch import torch import torch.nn as nn import torch.nn.functional as F import as data import torch.optim as optim # Torchvision import torchvision from torchvision.datasets import CIFAR10 from torchvision import transforms # PyTorch Lightning try : import pytorch_lightning as pl except ModuleNotFoundError : # Google Colab does not have PyTorch Lightning installed by default. rcParams = 2.0 import seaborn as sns sns. # Standard libraries import os import json import math import numpy as np # Imports for plotting import matplotlib.pyplot as plt % matplotlib inlineįrom IPython.display import set_matplotlib_formats set_matplotlib_formats ( 'svg', 'pdf' ) # For export from lors import to_rgb import matplotlib matplotlib. We will use PyTorch Lightning to reduce the training code overhead. in VAE, GANs, or super-resolution applications).įirst of all, we again import most of our standard libraries. Such deconvolution networks are necessary wherever we start from a small feature vector and need to output an image of full size (e.g. Besides learning about the autoencoder framework, we will also see the “deconvolution” (or transposed convolution) operator in action for scaling up feature maps in height and width. This property is useful in many applications, in particular in compressing data or comparing images on a metric beyond pixel-levelĬomparisons. The feature vector is called the “bottleneck” of the network as we aim to compress the input data into a smaller amount of features. Autoencoders are trained on encoding input data such as images into a smaller feature vector, and afterward, reconstruct it by a second neural network, called a decoder. It is very important to underscore that author created this wonderful project for us.In this tutorial, we will take a closer look at autoencoders (AE). It is not very fair, project really worth some reviews.
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