From c4559c0f653a05138e875371560fa5c874a7eecd Mon Sep 17 00:00:00 2001
From: HypoxiE <kosmaer42@gmail.com>
Date: Tue, 19 Aug 2025 14:15:08 +0700
Subject: [PATCH] =?UTF-8?q?=D0=91=D0=B0=D0=B7=D0=B0=20=D1=83=D1=81=D0=B2?=
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---
 requirements.txt | Bin 0 -> 382 bytes
 src/main.py      |  38 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 38 insertions(+)
 create mode 100644 requirements.txt

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
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GIT binary patch
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diff --git a/src/main.py b/src/main.py
index e69de29..97f3851 100644
--- a/src/main.py
+++ b/src/main.py
@@ -0,0 +1,38 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# 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())
+
+# 2. Определяем модель (1 скрытый слой, 10 нейронов)
+model = nn.Sequential(
+    nn.Linear(1, 10),  # вход -> скрытый слой
+    nn.ReLU(),         # активация
+    nn.Linear(10, 1)   # скрытый -> выход
+)
+
+# 3. Функция потерь и оптимизатор
+criterion = nn.MSELoss()
+optimizer = optim.SGD(model.parameters(), lr=0.01)
+
+# 4. Обучение
+for epoch in range(200):
+    # прямой проход
+    y_pred = model(x)
+    loss = criterion(y_pred, y)
+    
+    # обнуление градиентов и обратное распространение
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    
+    if (epoch+1) % 40 == 0:
+        print(f'Epoch {epoch+1}/200 | Loss: {loss.item():.4f}')
+
+# 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}")