vae.py

# prerequisites
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
F.sigmoid = torch.sigmoid
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from torchvision.utils import save_image
import matplotlib.pyplot as plt
import numpy as np

class VAE(nn.Module):
    def __init__(self, x_dim, h_dim1, h_dim2, z_dim):
        super(VAE, self).__init__()
        
        # encoder part
        self.fc1 = nn.Linear(x_dim, h_dim1)
        self.fc2 = nn.Linear(h_dim1, h_dim2)
        self.fc31 = nn.Linear(h_dim2, z_dim)
        self.fc32 = nn.Linear(h_dim2, z_dim)
        # decoder part
        self.fc4 = nn.Linear(z_dim, h_dim2)
        self.fc5 = nn.Linear(h_dim2, h_dim1)
        self.fc6 = nn.Linear(h_dim1, x_dim)
        
    def encoder(self, x):
        h = F.relu(self.fc1(x))
        h = F.relu(self.fc2(h))
        return self.fc31(h), self.fc32(h) # mu, log_var
    
    def sampling(self, mu, log_var):
        std = torch.exp(0.5*log_var)
        eps = torch.randn_like(std)
        return eps.mul(std).add_(mu) # return z sample
        
    def decoder(self, z):
        h = F.relu(self.fc4(z))
        h = F.relu(self.fc5(h))
        return F.sigmoid(self.fc6(h)) 
    
    def forward(self, x):
        mu, log_var = self.encoder(x.view(-1, 784))
        z = self.sampling(mu, log_var)
        return self.decoder(z), mu, log_var

# build model
vae = VAE(x_dim=784, h_dim1= 512, h_dim2=256, z_dim=2)

optimizer = optim.Adam(vae.parameters())
# return reconstruction error + KL divergence losses
def loss_function(recon_x, x, mu, log_var):
    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')
    KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
    return BCE + KLD
def train(epoch,train_loader, view_data):
    vae.train()
    train_loss = 0
    for batch_idx, (data, _) in tqdm(enumerate(train_loader),total=len(train_loader)):
        data = data
        optimizer.zero_grad()
        
        recon_batch, mu, log_var = vae(data)
        loss = loss_function(recon_batch, data, mu, log_var)
        
        loss.backward()
        train_loss += loss.item()
        optimizer.step()
        
    f, a = plt.subplots(1, 5, figsize=(5, 2))

    # plotting decoded image (second row)
    decoded_data,_,_ = vae(view_data)
    for i in range(5):
        a[i].clear()
        a[i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')
        a[i].set_xticks(()); a[i].set_yticks(())
    plt.draw(); plt.pause(0.1)
    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))