285 lines
7.5 KiB
Plaintext
285 lines
7.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a11ebdff",
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"metadata": {},
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"source": [
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"## Manual approach"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "3163fb75",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"X = torch.tensor([[0.0],[10.0], [20.0], [30.0], [40.0]])\n",
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"Y = torch.tensor([[32.0], [50.0], [68.0], [86.0], [104.0]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "64a02514",
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"metadata": {},
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"outputs": [],
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"source": [
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"W = torch.randn((1, 1), requires_grad=True)\n",
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"B = torch.randn((1, 1), requires_grad=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "35200e59",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0: Loss = 42.0600\n",
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"Epoch 400: Loss = 24.6847\n",
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"Epoch 800: Loss = 14.4873\n",
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"Epoch 1200: Loss = 8.5024\n",
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"Epoch 1600: Loss = 4.9900\n",
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"Epoch 2000: Loss = 2.9286\n",
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"Epoch 2400: Loss = 1.7188\n",
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"Epoch 2800: Loss = 1.0087\n",
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"Epoch 3200: Loss = 0.5920\n",
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"Epoch 3600: Loss = 0.3474\n",
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"Epoch 4000: Loss = 0.2039\n",
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"Epoch 4400: Loss = 0.1197\n",
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"Epoch 4800: Loss = 0.0702\n",
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"Epoch 5200: Loss = 0.0412\n",
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"Epoch 5600: Loss = 0.0242\n",
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"Epoch 6000: Loss = 0.0142\n",
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"Epoch 6400: Loss = 0.0083\n",
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"Epoch 6800: Loss = 0.0049\n",
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"Epoch 7200: Loss = 0.0029\n",
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"Epoch 7600: Loss = 0.0017\n",
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"Epoch 8000: Loss = 0.0010\n",
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"Epoch 8400: Loss = 0.0006\n",
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"Epoch 8800: Loss = 0.0003\n",
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"Epoch 9200: Loss = 0.0002\n",
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"Epoch 9600: Loss = 0.0001\n",
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"Epoch 10000: Loss = 0.0001\n",
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"Epoch 10400: Loss = 0.0000\n",
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"Epoch 10800: Loss = 0.0000\n",
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"Epoch 11200: Loss = 0.0000\n",
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"Epoch 11600: Loss = 0.0000\n",
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"Epoch 12000: Loss = 0.0000\n",
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"Epoch 12400: Loss = 0.0000\n",
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"Epoch 12800: Loss = 0.0000\n",
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"Epoch 13200: Loss = 0.0000\n",
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"Epoch 13600: Loss = 0.0000\n",
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"Epoch 14000: Loss = 0.0000\n",
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"Epoch 14400: Loss = 0.0000\n",
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"Epoch 14800: Loss = 0.0000\n",
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"Epoch 15200: Loss = 0.0000\n",
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"Epoch 15600: Loss = 0.0000\n",
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"Epoch 16000: Loss = 0.0000\n",
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"Epoch 16400: Loss = 0.0000\n",
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"Epoch 16800: Loss = 0.0000\n",
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"Epoch 17200: Loss = 0.0000\n",
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"Epoch 17600: Loss = 0.0000\n",
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"Epoch 18000: Loss = 0.0000\n",
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"Epoch 18400: Loss = 0.0000\n",
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"Epoch 18800: Loss = 0.0000\n",
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"Epoch 19200: Loss = 0.0000\n",
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"Epoch 19600: Loss = 0.0000\n"
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]
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}
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],
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"source": [
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"learning_rate = 0.001\n",
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"\n",
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"for epoch in range(20000):\n",
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" Y_pred = X.matmul(W) + B\n",
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"\n",
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" loss = ((Y_pred - Y)**2).mean()\n",
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"\n",
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" loss.backward()\n",
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" with torch.no_grad():\n",
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" W -= learning_rate * W.grad\n",
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" B -= learning_rate * B.grad\n",
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"\n",
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" W.grad.zero_()\n",
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" B.grad.zero_()\n",
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"\n",
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" if epoch % 400 == 0:\n",
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" print(f\"Epoch {epoch}: Loss = {loss.item():.4f}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "be1eb1bd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"--- Результаты обучения ---\n",
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"Предсказание для 100°C: 211.99°F (Ожидалось: 212.00)\n",
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"Итоговый вес W: 1.7999 (Ожидалось: 1.8)\n",
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"Итоговое смещение B: 32.0029 (Ожидалось: 32.0)\n"
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]
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}
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],
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"source": [
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"with torch.no_grad():\n",
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" X_test = torch.tensor([[100.0]])\n",
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" Y_test_pred = X_test.matmul(W) + B\n",
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" print(\"\\n--- Результаты обучения ---\")\n",
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" print(f\"Предсказание для 100°C: {Y_test_pred.item():.2f}°F (Ожидалось: 212.00)\")\n",
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" print(f\"Итоговый вес W: {W.item():.4f} (Ожидалось: 1.8)\")\n",
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" print(f\"Итоговое смещение B: {B.item():.4f} (Ожидалось: 32.0)\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a066894",
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"metadata": {},
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"source": [
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"## Professional approach"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "fa3c1e81",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"\n",
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"X = torch.tensor([[0.0],[10.0], [20.0], [30.0], [40.0]])\n",
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"Y = torch.tensor([[32.0], [50.0], [68.0], [86.0], [104.0]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "8860a027",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = nn.Linear(in_features=1, out_features=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "b1213a8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"criterion = nn.MSELoss()\n",
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"optimizer = optim.SGD(model.parameters(), lr=0.001)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "27d203c4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0: loss = 6039.51318359375\n",
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"Epoch 400: loss = 199.59164428710938\n",
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"Epoch 800: loss = 117.13835144042969\n",
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"Epoch 1200: loss = 68.74734497070312\n",
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"Epoch 1600: loss = 40.34719467163086\n"
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]
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}
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],
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"source": [
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"for epoch in range(2000):\n",
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" Y_pred = model(X)\n",
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"\n",
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" loss = criterion(Y_pred, Y)\n",
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"\n",
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" optimizer.zero_grad()\n",
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"\n",
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" loss.backward()\n",
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"\n",
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" optimizer.step()\n",
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"\n",
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" if epoch % 400 == 0:\n",
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" print(f\"Epoch {epoch}: loss = {loss.item()}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "024b4276",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"--- Результаты обучения ---\n",
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"Предсказание для 100°C: 231.68°F\n",
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"Итоговый вес W: 2.0811\n",
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"Итоговое смещение B: 23.5716\n"
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]
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}
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],
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"source": [
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"with torch.no_grad():\n",
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" X_test = torch.tensor([[100.0]])\n",
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" Y_test_pred = model(X_test)\n",
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" print(\"\\n--- Результаты обучения ---\")\n",
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" print(f\"Предсказание для 100°C: {Y_test_pred.item():.2f}°F\")\n",
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" \n",
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" # Доступ к обученным весам внутри слоя\n",
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" print(f\"Итоговый вес W: {model.weight.item():.4f}\")\n",
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" print(f\"Итоговое смещение B: {model.bias.item():.4f}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "df8942cd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv (3.11.15)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.15"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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