Create neural_network_optimizer.py

aurora_axiom_ui/src/neural_network_optimizer.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+
+class NeuralNetworkOptimizer(nn.Module):
+    def __init__(self):
+        super(NeuralNetworkOptimizer, self).__init__()
+        self.fc1 = nn.Linear(784, 256)
+        self.fc2 = nn.Linear(256, 128)
+        self.fc3 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+class CustomDataset(Dataset):
+    def __init__(self, data, labels):
+        self.data = data
+        self.labels = labels
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx], self.labels[idx]
+
+def train(model, device, train_loader, optimizer, epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = nn.CrossEntropyLoss()(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % 100 == 0:
+            print(f'Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item()}')
+
+def main():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = NeuralNetworkOptimizer()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    dataset = CustomDataset(data, labels)
+    train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
+    for epoch in range(10):
+        train(model, device, train_loader, optimizer, epoch)
+
+if __name__ == '__main__':
+    main()