diff --git a/beta-vae/README.md b/beta-vae/README.md
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+# Basic beta-VAE Example
+
+This is an implementation of the paper [beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework](https://openreview.net/pdf?id=Sy2fzU9gl).
+
+We did experimentation on the FashionMNIST dataset, using a very simple convolution neural network architecture.
+
+```bash
+pip install -r requirements.txt
+python main.py
+```
+The main.py script accepts the following arguments:
+
+```bash
+optional arguments:
+  --batch-size		input batch size for training (default: 128)
+  --epochs		number of epochs to train (default: 10)
+  --no-cuda		enables CUDA training
+  --seed		random seed (default: 1)
+  --log-interval	how many batches to wait before logging training status
+```
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diff --git a/beta-vae/main.py b/beta-vae/main.py
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+from __future__ import print_function
+import argparse
+import torch
+import torch.utils.data
+from torch import nn, optim
+from torch.nn import functional as F
+from torchvision import datasets, transforms
+from torchvision.utils import save_image
+
+
+from typing import List, Callable, Union, Any, TypeVar, Tuple
+Tensor = TypeVar('torch.tensor')
+
+
+
+parser = argparse.ArgumentParser(description='VAE MNIST Example')
+parser.add_argument('--batch-size', type=int, default=128, metavar='N',
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                    help='number of epochs to train (default: 10)')
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='enables CUDA training')
+parser.add_argument('--seed', type=int, default=1, metavar='S',
+                    help='random seed (default: 1)')
+parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+                    help='how many batches to wait before logging training status')
+args = parser.parse_args()
+args.cuda = not args.no_cuda and torch.cuda.is_available()
+
+torch.manual_seed(args.seed)
+
+device = torch.device("cuda" if args.cuda else "cpu")
+
+kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
+
+train_loader = torch.utils.data.DataLoader(
+    datasets.FashionMNIST('../data', train=True, download=True,
+                   transform=transforms.ToTensor()),
+    batch_size=args.batch_size, shuffle=True, **kwargs)
+test_loader = torch.utils.data.DataLoader(
+    datasets.FashionMNIST('../data', train=False, transform=transforms.ToTensor()),
+    batch_size=args.batch_size, shuffle=True, **kwargs)
+
+
+class BetaVAE(nn.Module):
+    def __init__(self,
+                 in_channels: int,
+                 latent_dim: int,
+                 hidden_dims: List = None,
+                 beta: int = 4):
+        super(BetaVAE, self).__init__()
+        self.latent_dim = latent_dim
+        self.beta = beta
+        
+        modules = []
+        if hidden_dims is None:
+            hidden_dims = [16, 4]
+        
+        # Build Encoder
+        for h_dim in hidden_dims:
+            modules.append(
+                nn.Sequential(
+                    nn.Conv2d(in_channels, out_channels=h_dim,
+                              kernel_size= 3, padding  = 1),
+                    nn.ReLU(),
+                    nn.MaxPool2d(2, 2))
+            )
+            in_channels = h_dim
+
+        self.encoder = nn.Sequential(*modules)
+        self.fc_mu = nn.Linear(hidden_dims[-1]*7*7, latent_dim) 
+        self.fc_var = nn.Linear(hidden_dims[-1]*7*7, latent_dim)
+        
+        # Build Decoder
+        modules = []
+
+        self.decoder_input = nn.Linear(latent_dim, hidden_dims[-1]*7*7)
+
+        hidden_dims.reverse()
+
+        for i in range(len(hidden_dims) - 1):
+            modules.append(
+                nn.Sequential(
+                    nn.ConvTranspose2d(hidden_dims[i],
+                                       hidden_dims[i + 1],
+                                       kernel_size=2,
+                                       stride = 2),
+                    nn.ReLU())
+            )
+
+        self.decoder = nn.Sequential(*modules)
+        
+        self.final_layer = nn.Sequential(
+                            nn.ConvTranspose2d(hidden_dims[-1],
+                                               1,
+                                               kernel_size=2,
+                                               stride=2),
+                            nn.Sigmoid())
+        
+    
+    def encode(self, input: Tensor) -> List[Tensor]:
+        """
+        Encodes the input by passing through the encoder network
+        and returns the latent codes.
+        :param input: (Tensor) Input tensor to encoder [N x C x H x W]
+        :return: (Tensor) List of latent codes
+        """
+        result = self.encoder(input)
+        result = torch.flatten(result, start_dim=1)
+
+        # Split the result into mu and var components
+        # of the latent Gaussian distribution
+        mu = self.fc_mu(result)
+        log_var = self.fc_var(result)
+
+        return [mu, log_var]
+    
+    def decode(self, z: Tensor) -> Tensor:
+        result = self.decoder_input(z)
+        result = result.view(-1, 4, 7, 7)
+        result = self.decoder(result)
+        result = self.final_layer(result)
+        return result
+    
+    def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:
+        """
+        Will a single z be enough ti compute the expectation
+        for the loss??
+        :param mu: (Tensor) Mean of the latent Gaussian
+        :param logvar: (Tensor) Standard deviation of the latent Gaussian
+        :return:
+        """
+        std = torch.exp(0.5 * logvar)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+    
+    def forward(self, input: Tensor, **kwargs) -> Tensor:
+        mu, log_var = self.encode(input)
+        z = self.reparameterize(mu, log_var)
+        return  self.decode(z), mu, log_var
+                 
+    def loss_function(self,
+                      *args,
+                      **kwargs) -> dict:
+        recons = args[0]
+        input = args[1]
+        mu = args[2]
+        log_var = args[3]
+        criterion = nn.BCELoss(reduction='sum')
+        recons_loss =criterion(recons, input)
+
+        kld_loss = torch.mean(-0.5 * torch.sum(1 + log_var - mu ** 2 - log_var.exp(), dim = 1), dim = 0)
+            
+        return recons_loss + self.beta * kld_loss
+
+model = BetaVAE(1, 100, beta=5).to(device)
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+
+def train(epoch):
+    model.train()
+    train_loss = 0
+    for batch_idx, (data, _) in enumerate(train_loader):
+        data = data.to(device)
+        optimizer.zero_grad()
+        recon_batch, mu, logvar = model(data)
+        loss = model.loss_function(recon_batch, data, mu, logvar)
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader),
+                loss.item() / len(data)))
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+          epoch, train_loss / len(train_loader.dataset)))
+
+
+def test(epoch):
+    model.eval()
+    test_loss = 0
+    with torch.no_grad():
+        for i, (data, _) in enumerate(test_loader):
+            data = data.to(device)
+            recon_batch, mu, logvar = model(data)
+            test_loss += model.loss_function(recon_batch, data, mu, logvar).item()
+            if i == 0:
+                n = min(data.size(0), 8)
+                # Allow to save the orginal image and the generated one as we go
+                comparison = torch.cat([data[:n],
+                                      recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
+                save_image(comparison.cpu(),
+                         'results/reconstruction_' + str(epoch) + '.png', nrow=n)
+
+    test_loss /= len(test_loader.dataset)
+    print('====> Test set loss: {:.4f}'.format(test_loss))
+
+if __name__ == "__main__":
+    for epoch in range(1, args.epochs + 1):
+        train(epoch)
+        test(epoch)
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diff --git a/beta-vae/requirements.txt b/beta-vae/requirements.txt
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+torch
+torchvision
+tqdm
+six
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diff --git a/vae/.vscode/settings.json b/vae/.vscode/settings.json
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+{
+    "python.pythonPath": "/home/aims/anaconda3/envs/aims/bin/python"
+}
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