import torch
import torch.nn as nn
import torch.optim as optim

# הגדרת הרשת
class SimpleNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super().__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.3)  # regularization
        self.fc2 = nn.Linear(hidden_size, hidden_size // 2)
        self.fc3 = nn.Linear(hidden_size // 2, num_classes)

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.relu(self.fc2(x))
        x = self.fc3(x)  # raw logits, no softmax (CrossEntropyLoss handles it)
        return x

# אימון
model = SimpleNet(input_size=784, hidden_size=256, num_classes=10)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

for epoch in range(20):
    for X_batch, y_batch in train_loader:
        optimizer.zero_grad()
        outputs = model(X_batch)
        loss = criterion(outputs, y_batch)
        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

Neural Networks — איך רשת נוירונים עובדת

Neural Networks — איך רשת נוירונים עובדת

Neural Networks — איך רשת נוירונים עובדת