⚡ Changes structure and adds metrics

train_nn.py

@@ -2,6 +2,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 from torch.utils.data import random_split, DataLoader, TensorDataset
+from torch.optim.lr_scheduler import ReduceLROnPlateau
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 
 
@@ -10,35 +11,31 @@ class NeuralNetwork(nn.Module):
         super().__init__()
 
         self.features = nn.Sequential(
-            nn.Linear(8, 256),
+            nn.Linear(8, 512),
             nn.ReLU(),
-            nn.Dropout(p=0.2),
+            nn.BatchNorm1d(512),
+            nn.Dropout(p=0.4),
 
-            nn.Linear(256, 256),
+            nn.Linear(512, 256),
             nn.ReLU(),
-            nn.Dropout(p=0.2),
+            nn.BatchNorm1d(256),
+            nn.Dropout(p=0.4),
 
-            nn.Linear(256, 256),
+            nn.Linear(256, 128),
             nn.ReLU(),
-            nn.Dropout(p=0.2),
+            nn.BatchNorm1d(128),
+            nn.Dropout(p=0.4),
 
-            nn.Linear(256, 256),
+            nn.Linear(128, 64),
             nn.ReLU(),
-            nn.Dropout(p=0.2),
+            nn.BatchNorm1d(64),
+            nn.Dropout(p=0.4),
 
-            nn.Linear(256, 4),
+            nn.Linear(64, 4)
-            nn.ReLU()
         )
 
-        self.lstm = nn.LSTM(input_size=4, hidden_size=64, batch_first=True)
-        self.output_layer = nn.Linear(64, 4)
-
     def forward(self, x):
-        x = self.features(x)
+        return self.features(x)
-        x = x.unsqueeze(1)
-        lstm_out, _ = self.lstm(x)
-        x = lstm_out.squeeze(1)
-        return self.output_layer(x)
 
 
 data = np.load("clean.npy")
@@ -54,7 +51,7 @@ test_size = len(data) - train_size
 torch.manual_seed(42)
 
 batch_size = 32
-epochs = 100
+epochs = 200
 lr = 0.001
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -76,7 +73,8 @@ print(model)
 print("")
 
 loss_fn = nn.MSELoss()
-optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-5)
+scheduler = ReduceLROnPlateau(optimizer, mode='min', patience=5, factor=0.5)
 
 for epoch in range(epochs):
     print(f"Epoch {epoch + 1}/{epochs}...\r", end="")
@@ -117,6 +115,7 @@ for epoch in range(epochs):
         f"Train loss: {train_loss:.4f}\n"
         f"Test loss: {test_loss:.4f}\n"
     )
+    scheduler.step(test_loss)
 
 torch.save(model.state_dict(), "model.pth")
 print("Model saved to model.pth")