Create model_probit.py

model_probit.py

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+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.nn.functional as F
+
+class latent_space_old(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.relu = nn.ReLU()
+        # self.fc1 = nn.Linear(28 * 28, 20)
+        # self.fc2 = nn.Linear(20, 2)
+        self.fc = nn.Linear(2,2)
+        # self.final = nn.BCEWithLogitsLoss()
+        self.flatten = nn.Flatten()
+
+    def forward(self, x):
+        x = self.flatten(x)
+        x = self.fc(x)
+        x = self.final(x)
+        return x
+
+class latent_space(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.relu = nn.ReLU()
+        # self.fc1 = nn.Linear(28 * 28, 20)
+        # self.fc2 = nn.Linear(20, 2)
+        self.fc = nn.Linear(2,1)
+        # self.fc20 = nn.Linear(10,1)
+        # self.final = nn.BCEWithLogitsLoss()
+        self.activation = nn.Sigmoid()
+        self.flatten = nn.Flatten()
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        # x = self.flatten(x)
+        x = self.fc(x)
+        # x = self.relu(self.fc20(x))
+        x = self.relu(x)
+        x = self.activation(x)
+        return x