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diff --git a/code/sunlab/suntorch/models/variational/autoencoder.py b/code/sunlab/suntorch/models/variational/autoencoder.py
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+++ b/code/sunlab/suntorch/models/variational/autoencoder.py
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+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+from .encoder import Encoder
+from .decoder import Decoder
+from .common import *
+
+
+class VariationalAutoencoder:
+    """# Variational Autoencoder Model"""
+
+    def __init__(
+        self, data_dim, latent_dim, enc_dec_size, negative_slope=0.3, dropout=0.0
+    ):
+        self.encoder = Encoder(
+            data_dim,
+            enc_dec_size,
+            latent_dim,
+            negative_slope=negative_slope,
+            dropout=dropout,
+        )
+        self.decoder = Decoder(
+            data_dim,
+            enc_dec_size,
+            latent_dim,
+            negative_slope=negative_slope,
+            dropout=dropout,
+        )
+        self.data_dim = data_dim
+        self.latent_dim = latent_dim
+        self.p = dropout
+        self.negative_slope = negative_slope
+        return
+
+    def parameters(self):
+        return (*self.encoder.parameters(), *self.decoder.parameters())
+
+    def train(self):
+        self.encoder.train(True)
+        self.decoder.train(True)
+        return self
+
+    def eval(self):
+        self.encoder.train(False)
+        self.decoder.train(False)
+        return self
+
+    def encode(self, data):
+        return self.encoder(data)
+
+    def decode(self, data):
+        return self.decoder(data)
+
+    def reparameterization(self, mean, var):
+        epsilon = torch.randn_like(var)
+        if torch.cuda.is_available():
+            epsilon = epsilon.cuda()
+        z = mean + var * epsilon
+        return z
+
+    def forward(self, x):
+        mean, log_var = self.encoder(x)
+        z = self.reparameterization(mean, torch.exp(0.5 * log_var))
+        X = self.decoder(z)
+        return X, mean, log_var
+
+    def __call__(self, data):
+        return self.forward(data)
+
+    def save(self, base="./"):
+        torch.save(self.encoder.state_dict(), base + "weights_encoder.pt")
+        torch.save(self.decoder.state_dict(), base + "weights_decoder.pt")
+        return self
+
+    def load(self, base="./"):
+        self.encoder.load_state_dict(torch.load(base + "weights_encoder.pt"))
+        self.encoder.eval()
+        self.decoder.load_state_dict(torch.load(base + "weights_decoder.pt"))
+        self.decoder.eval()
+        return self
+
+    def to(self, device):
+        self.encoder.to(device)
+        self.decoder.to(device)
+        return self
+
+    def cuda(self):
+        if torch.cuda.is_available():
+            self.encoder = self.encoder.cuda()
+            self.decoder = self.decoder.cuda()
+        return self
+
+    def cpu(self):
+        self.encoder = self.encoder.cpu()
+        self.decoder = self.decoder.cpu()
+        return self
+
+    def init_optimizers(self, recon_lr=1e-4):
+        self.optim_E_enc = torch.optim.Adam(self.encoder.parameters(), lr=recon_lr)
+        self.optim_D = torch.optim.Adam(self.decoder.parameters(), lr=recon_lr)
+        return self
+
+    def init_losses(self, recon_loss_fn=F.binary_cross_entropy):
+        self.recon_loss_fn = recon_loss_fn
+        return self
+
+    def train_step(self, raw_data):
+        data = to_var(raw_data.view(raw_data.size(0), -1))
+
+        self.encoder.zero_grad()
+        self.decoder.zero_grad()
+        X, _, _ = self.forward(data)
+        #         mean, log_var = self.encoder(data)
+        #         z = self.reparameterization(mean, torch.exp(0.5 * log_var))
+        #         X = self.decoder(z)
+        self.recon_loss = self.recon_loss_fn(X + EPS, data + EPS)
+        self.recon_loss.backward()
+        self.optim_E_enc.step()
+        self.optim_D.step()
+        return
+
+    def losses(self):
+        try:
+            return self.recon_loss
+        except:
+            ...
+        return