# Lightning Module
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.data import random_split
from torchvision.datasets import MNIST
from torchvision import transforms
import pytorch_lightning as pl
class
LitAutoEncoder
(pl.LightningModule):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(28 * 28, 64),
nn.ReLU(),
nn.Linear(64, 3))
self.decoder = nn.Sequential(
nn.Linear(3, 64),
nn.ReLU(),
nn.Linear(64, 28 * 28))
embedding = self.encoder(x)
def
configure_optimizers
(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
def
training_step
(self, train_batch, batch_idx):
x, y = train_batch
x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = F.mse_loss(x_hat, x)
self.log('train_loss', loss)
def
validation_step
(self, val_batch, batch_idx):
x, y = val_batch
x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = F.mse_loss(x_hat, x)
self.log('val_loss', loss)
# data
dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
mnist_train, mnist_val = random_split(dataset, [55000, 5000])
train_loader = DataLoader(mnist_train, batch_size=32)
val_loader = DataLoader(mnist_val, batch_size=32)
# model
# training
trainer = pl.Trainer(gpus=4, precision=16, limit_train_batches=0.5)
trainer.fit(model, train_loader, val_loader)