add variation inference training given to core team

trainings/variational_inference_core_team/.gitignore

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+.ipynb_checkpoints/
+.idea/
+__pycache__/
+data/

trainings/variational_inference_core_team/models.py

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+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class AutoEncoder(nn.Module):
+    def __init__(self, input_size=784, z_size=20):
+        super().__init__()
+        hidden_size = int((input_size - z_size) / 2 + z_size)
+        self.encoder = nn.Sequential(
+            nn.Linear(input_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, z_size)
+        )
+        self.decoder = nn.Sequential(
+            nn.Linear(z_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, input_size),
+        )
+
+    def forward(self, x):
+        x = x.view(-1, 784)
+        z = self.encoder(x)
+        x = self.decoder(z)
+
+        if self.training:
+            return x
+        else:
+            return F.sigmoid(x)
+
+
+class VAE(nn.Module):
+    def __init__(self, input_size=784, z_size=20):
+        super().__init__()
+        hidden_size = int((input_size - z_size) / 2 + z_size)
+        self.z_size = z_size
+        self.encoder = nn.Sequential(
+            nn.Linear(input_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, z_size * 2)
+        )
+        self.decoder = nn.Sequential(
+            nn.Linear(z_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, input_size),
+            nn.Sigmoid()
+        )
+
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)  # unit gaussian
+        z = mu + eps * std
+        return z
+
+    def forward(self, x):
+        x = x.view(-1, 784)
+        variational_params = self.encoder(x)
+        mu = variational_params[..., :self.z_size]
+        log_var = variational_params[..., self.z_size:]
+        z = self.reparameterize(mu, log_var)
+        return self.decoder(z), z, mu, log_var

trainings/variational_inference_core_team/requirements.txt

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+torch==1.3.0
+torchvision>=0.4
+numpy==1.17.2
+matplotlib==3.1.1
+scipy==1.3.1