added deepsad base code

Deep-SAD-PyTorch/src/networks/layers/stochastic.py

@@ -0,0 +1,53 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch.autograd import Variable
+
+
+# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
+class Stochastic(nn.Module):
+    """
+    Base stochastic layer that uses the reparametrization trick (Kingma and Welling, 2013) to draw a sample from a
+    distribution parametrized by mu and log_var.
+    """
+
+    def __init__(self):
+        super(Stochastic, self).__init__()
+
+    def reparametrize(self, mu, log_var):
+        epsilon = Variable(torch.randn(mu.size()), requires_grad=False)
+
+        if mu.is_cuda:
+            epsilon = epsilon.to(mu.device)
+
+        # log_std = 0.5 * log_var
+        # std = exp(log_std)
+        std = log_var.mul(0.5).exp_()
+
+        # z = std * epsilon + mu
+        z = mu.addcmul(std, epsilon)
+
+        return z
+
+    def forward(self, x):
+        raise NotImplementedError
+
+
+class GaussianSample(Stochastic):
+    """
+    Layer that represents a sample from a Gaussian distribution.
+    """
+
+    def __init__(self, in_features, out_features):
+        super(GaussianSample, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+
+        self.mu = nn.Linear(in_features, out_features)
+        self.log_var = nn.Linear(in_features, out_features)
+
+    def forward(self, x):
+        mu = self.mu(x)
+        log_var = F.softplus(self.log_var(x))
+        return self.reparametrize(mu, log_var), mu, log_var