Upload CustomModel.py

CustomModel.py

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+def ba_activation(x, weights, a, epsilon):
+    # Ensure x is a torch tensor
+    x = torch.as_tensor(x, dtype=torch.float32)
+    
+    # Modulate inputs based on weights for the activation
+    x = weights * x
+    
+    # Apply the Ba-inspired operation
+    # Clamp and normalize x to stabilize the operation
+    x_normalized = torch.clamp(x, -1, 1)
+    fractional_inspired = torch.pow(torch.abs(x_normalized), x_normalized)
+    activation_result = epsilon * torch.cos(np.pi * a * fractional_inspired * torch.log(torch.abs(fractional_inspired) + 1e-7))
+    
+    # Apply an additional non-linearity to ensure the output is stable
+    activation_result = torch.tanh(activation_result)
+    
+    return activation_result
+
+# Define a custom model using the Ba-inspired activation function
+class CustomModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(CustomModel, self).__init__()
+        self.linear1 = nn.Linear(input_size, hidden_size)
+        self.linear2 = nn.Linear(hidden_size, output_size)
+        self.weights = nn.Parameter(torch.randn(hidden_size))
+        self.a = 0.5  # Parameter for the Ba-inspired activation
+        self.epsilon = 0.1  # Parameter for the Ba-inspired activation
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = ba_activation(x, self.weights, self.a, self.epsilon)  # Use Ba-inspired activation
+        x = self.linear2(x)
+        return x