✨ Adds graphing to train

.gitignore

@@ -5,10 +5,11 @@
 venv/
 __pycache__/
 
+results/
+
 *.zip
 *.csv
 *.npy
 
 *.jasp
-results.txt
 *.pth

train_nn.py

@@ -1,9 +1,18 @@
 import numpy as np
 import torch
 import torch.nn as nn
+import argparse
 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
+import matplotlib.pyplot as plt
+
+parser = argparse.ArgumentParser(
+    prog="train_nn"
+)
+parser.add_argument("-g", "--graph", action="store_true", default=False, help="Graph losses")
+args = parser.parse_args()
+graph = args.graph
 
 
 class NeuralNetwork(nn.Module):
@@ -76,6 +85,9 @@ loss_fn = nn.MSELoss()
 optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-5)
 scheduler = ReduceLROnPlateau(optimizer, mode='min', patience=5, factor=0.5)
 
+train_losses = []
+test_losses = []
+
 for epoch in range(epochs):
     print(f"Epoch {epoch + 1}/{epochs}...\r", end="")
 
@@ -95,6 +107,7 @@ for epoch in range(epochs):
         train_loss += loss.item() * X.size(0)
 
     train_loss /= len(train_dataset)
+    train_losses.append(train_loss)
 
     model.eval()
     test_loss = 0.0
@@ -109,6 +122,7 @@ for epoch in range(epochs):
             test_loss = loss.item() * X.size(0)
 
     test_loss /= len(test_dataset)
+    test_losses.append(test_loss)
 
     print(
         f"Epoch {epoch + 1}/{epochs}\n"
@@ -117,6 +131,9 @@ for epoch in range(epochs):
     )
     scheduler.step(test_loss)
 
+print(f"Average train loss: {sum(train_losses) / len(train_losses):.4f}")
+print(f"Average test loss: {sum(test_losses) / len(test_losses):.4f}")
+
 torch.save(model.state_dict(), "model.pth")
 print("Model saved to model.pth")
 
@@ -144,3 +161,22 @@ print(f"Accuracy:  {accuracy:.4f}")
 print(f"Precision: {precision:.4f}")
 print(f"Recall:    {recall:.4f}")
 print(f"F1 Score:  {f1:.4f}")
+
+if graph:
+    x = np.arange(1, epochs + 1, 1)
+
+    plt.figure(figsize=(8, 6))
+    plt.plot(x, train_losses, color="red", label="Strata trénovania")
+    plt.plot(x, test_losses, color="blue", label="Strata testovania")
+
+    plt.xlabel("Epocha")
+    plt.ylabel("Strata")
+    plt.title("Priebeh trénovania")
+
+    plt.text(0.99, 0.99,
+             f"Presnosť: {accuracy:.4f}\nPrecíznosť: {precision:.4f}\nOdvolanie: {recall:.4f}\nF1 skóre: {f1:.4f}",
+             ha="right", va="top", transform=plt.gca().transAxes, fontweight="bold")
+
+    plt.legend()
+    plt.tight_layout()
+    plt.show()