diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..9b5f630
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+__pycache__/
+*.pyc
+venv/
diff --git a/model.pth b/model.pth
new file mode 100644
index 0000000..3a5a919
Binary files /dev/null and b/model.pth differ
diff --git a/src/main.py b/src/main.py
index 97f3851..825b6b1 100644
--- a/src/main.py
+++ b/src/main.py
@@ -2,24 +2,35 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 
+torch.set_num_threads(torch.get_num_threads())
+print("Потоков используется:", torch.get_num_threads())
+
 # 1. Данные: y = 2x + 1 с шумом
 torch.manual_seed(0)
-x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # shape: [100,1]
-y = 2 * x + 1 + 0.2 * torch.randn(x.size())
+x1 = 2 * torch.rand(1000, 1) - 1
+x2 = 2 * torch.rand(1000, 1) - 1
+x = torch.cat([x1, x2], dim=1)
+y = (((x1**2 + x2**2)**0.5)>=0.5).float()
 
-# 2. Определяем модель (1 скрытый слой, 10 нейронов)
+# 2. Определяем модель
 model = nn.Sequential(
-    nn.Linear(1, 10),  # вход -> скрытый слой
-    nn.ReLU(),         # активация
-    nn.Linear(10, 1)   # скрытый -> выход
+	nn.Linear(2, 100),
+	nn.Tanh(),
+	nn.Linear(100, 200),
+	nn.Tanh(),
+	nn.Linear(200, 100),
+	nn.Tanh(),
+	nn.Linear(100, 1),
+	nn.Sigmoid()
 )
 
 # 3. Функция потерь и оптимизатор
 criterion = nn.MSELoss()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
+optimizer = optim.SGD(model.parameters(), lr=0.1)
 
 # 4. Обучение
-for epoch in range(200):
+epochs = 10_000
+for epoch in range(epochs):
     # прямой проход
     y_pred = model(x)
     loss = criterion(y_pred, y)
@@ -30,9 +41,20 @@ for epoch in range(200):
     optimizer.step()
     
     if (epoch+1) % 40 == 0:
-        print(f'Epoch {epoch+1}/200 | Loss: {loss.item():.4f}')
+        print(f'Epoch {epoch+1}/{epochs} | Loss: {loss.item():.4f}')
 
+
+torch.save(model.state_dict(), "model.pth")
 # 5. Проверка результата
-test_x = torch.tensor([[0.5]])
-pred_y = model(test_x)
-print(f"При x=0.5 сеть предсказывает: {pred_y.item():.3f}")
+tests = 100
+test_x1 = torch.unsqueeze(torch.linspace(-1, 1, tests), dim=1)
+test_x2 = torch.unsqueeze(torch.linspace(-1, 1, tests), dim=1)
+test_y = (((test_x1**2 + test_x2**2)**0.5) >= 0.5).float()
+
+for i in range(tests):
+    # объединяем по dim=0, т.к. каждый x[i] имеет форму [1]
+    test_x = torch.cat([test_x1[i], test_x2[i]], dim=0).unsqueeze(0)  # форма [1,2]
+    pred_y = model(test_x)
+    print(f"Нейросеть предсказала: {pred_y.item():.3f}; правильный ответ: {test_y[i].item():.0f}; точка: ({test_x})")
+
+
diff --git a/src/test.py b/src/test.py
new file mode 100644
index 0000000..f67d45a
--- /dev/null
+++ b/src/test.py
@@ -0,0 +1,21 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+model = nn.Sequential(
+	nn.Linear(2, 100),
+	nn.Tanh(),
+	nn.Linear(100, 200),
+	nn.Tanh(),
+	nn.Linear(200, 100),
+	nn.Tanh(),
+	nn.Linear(100, 1),
+	nn.Sigmoid()
+)
+
+model.load_state_dict(torch.load("model.pth"))
+
+test_x = torch.tensor([0., 0.4])
+
+pred_y = model(test_x)
+print(pred_y.item())
\ No newline at end of file