Derin \u00f6\u011frenme, yapay zekan\u0131n en h\u0131zl\u0131 geli\u015fen ve en etkili alanlar\u0131ndan biridir. G\u00f6r\u00fcnt\u00fc tan\u0131ma, do\u011fal dil i\u015fleme, ses sentezi ve \u00f6zerk s\u00fcr\u00fc\u015f gibi bir\u00e7ok alanda \u00e7\u0131\u011f\u0131r a\u00e7an ba\u015far\u0131lara imza atm\u0131\u015ft\u0131r. Bu ba\u015far\u0131n\u0131n arkas\u0131nda, karma\u015f\u0131k n\u00f6ral a\u011f modellerini tasarlamak, e\u011fitmek ve da\u011f\u0131tmak i\u00e7in kullan\u0131lan g\u00fc\u00e7l\u00fc yaz\u0131l\u0131m k\u00fct\u00fcphaneleri yatmaktad\u0131r. Bu k\u00fct\u00fcphaneler aras\u0131nda PyTorch, esnekli\u011fi, Pythonic yap\u0131s\u0131 ve y\u00fcksek performans\u0131 sayesinde ara\u015ft\u0131rmac\u0131lar ve geli\u015ftiriciler aras\u0131nda h\u0131zla pop\u00fclerlik kazanm\u0131\u015ft\u0131r.<\/p>\n
PyTorch, Facebook’un yapay zeka ara\u015ft\u0131rma grubu taraf\u0131ndan geli\u015ftirilen a\u00e7\u0131k kaynakl\u0131 bir makine \u00f6\u011frenimi k\u00fct\u00fcphanesidir. Temel olarak, NumPy benzeri bir tensor k\u00fct\u00fcphanesi \u00fczerine kurulmu\u015ftur ve GPU h\u0131zland\u0131rmas\u0131 ile derin \u00f6\u011frenme modellerinin e\u011fitimini kolayla\u015ft\u0131r\u0131r. Dinamik hesaplama grafikleri (define-by-run) yakla\u015f\u0131m\u0131 sayesinde, model geli\u015ftirme s\u00fcrecinde daha fazla esneklik sunar. Bu, \u00f6zellikle karma\u015f\u0131k veya deneysel model mimarileri \u00fczerinde \u00e7al\u0131\u015fan ara\u015ft\u0131rmac\u0131lar i\u00e7in b\u00fcy\u00fck bir avantajd\u0131r. PyTorch’un sezgisel API’si, Python diline a\u015fina olan herkesin derin \u00f6\u011frenme projelerine kolayca ba\u015flamas\u0131n\u0131 sa\u011flar. Bu makalede, PyTorch’u nas\u0131l kuraca\u011f\u0131n\u0131z\u0131, temel bile\u015fenlerini nas\u0131l kullanaca\u011f\u0131n\u0131z\u0131 ve basit bir derin \u00f6\u011frenme modelini nas\u0131l olu\u015fturup e\u011fitece\u011finizi ayr\u0131nt\u0131l\u0131 bir \u015fekilde inceleyece\u011fiz.<\/p>\n
PyTorch’u sisteminize kurmak, derin \u00f6\u011frenme yolculu\u011funuzdaki ilk ad\u0131md\u0131r. Kurulum s\u00fcreci, i\u015fletim sisteminize, kullanmak istedi\u011finiz Python ortam\u0131na ve \u00f6zellikle GPU deste\u011fi isteyip istemedi\u011finize g\u00f6re de\u011fi\u015fiklik g\u00f6sterebilir.<\/p>\n
PyTorch’u verimli bir \u015fekilde kullanabilmek i\u00e7in belirli sistem gereksinimlerini kar\u015f\u0131laman\u0131z \u00f6nemlidir:<\/p>\n
pip<\/code> (Python’\u0131n standart paket y\u00f6neticisi) veya conda<\/code> (Anaconda da\u011f\u0131t\u0131m\u0131 ile gelen bir ortam ve paket y\u00f6neticisi). Conda, \u00f6zellikle bilimsel hesaplama ve derin \u00f6\u011frenme ortamlar\u0131 i\u00e7in daha iyi bir izolasyon ve ba\u011f\u0131ml\u0131l\u0131k y\u00f6netimi sunar.<\/li>\n- GPU Deste\u011fi (\u0130ste\u011fe Ba\u011fl\u0131 ama \u015eiddetle \u00d6nerilir):<\/b> E\u011fer NVIDIA GPU’nuz varsa, CUDA teknolojisini kullanarak PyTorch’un GPU h\u0131zland\u0131rmas\u0131ndan faydalanabilirsiniz. Bunun i\u00e7in:\n
\n- Uyumlu bir NVIDIA GPU (genellikle GTX 10 serisi ve \u00fczeri, RTX serisi).<\/li>\n
- NVIDIA’n\u0131n g\u00fcncel GPU s\u00fcr\u00fcc\u00fcleri.<\/li>\n
- CUDA Toolkit (PyTorch’un destekledi\u011fi bir s\u00fcr\u00fcm).<\/li>\n
- cuDNN (CUDA Deep Neural Network library), CUDA Toolkit ile birlikte gelir ve derin \u00f6\u011frenme i\u015flemlerini daha da optimize eder.<\/li>\n<\/ul>\n
CPU \u00fczerinde de PyTorch \u00e7al\u0131\u015ft\u0131r\u0131labilir ancak b\u00fcy\u00fck modellerin e\u011fitimi veya geni\u015f veri k\u00fcmeleri i\u00e7in \u00e7ok yava\u015f olacakt\u0131r.<\/li>\n<\/ul>\n
Ortam Kurulumu<\/h4>\n
Derin \u00f6\u011frenme projeleri genellikle belirli k\u00fct\u00fcphane versiyonlar\u0131na ba\u011f\u0131ml\u0131d\u0131r. Bu ba\u011f\u0131ml\u0131l\u0131k \u00e7ak\u0131\u015fmalar\u0131n\u0131 \u00f6nlemek ve projelerinizi izole etmek i\u00e7in sanal ortamlar kullanmak en iyi pratiktir.<\/p>\n
Conda ile Sanal Ortam Olu\u015fturma<\/h5>\n
Anaconda veya Miniconda y\u00fckl\u00fcyse:<\/p>\n
conda create -n pytorch_env python=3.9\nconda activate pytorch_env<\/code><\/pre>\nBu komut, pytorch_env<\/code> ad\u0131nda Python 3.9’u kullanan yeni bir sanal ortam olu\u015fturur ve bu ortama ge\u00e7i\u015f yapar.<\/p>\nvenv ile Sanal Ortam Olu\u015fturma<\/h5>\n
Python’\u0131n kendi sanal ortam mod\u00fcl\u00fc olan venv<\/code>‘i kullanmak i\u00e7in:<\/p>\npython -m venv pytorch_env\nsource pytorch_env\/bin\/activate # Linux\/macOS\npytorch_env\\Scripts\\activate # Windows<\/code><\/pre>\nBu komutlar da benzer \u015fekilde bir sanal ortam olu\u015fturur ve etkinle\u015ftirir.<\/p>\n
PyTorch Kurulum Komutlar\u0131<\/h4>\n
Ortam\u0131n\u0131z\u0131 etkinle\u015ftirdikten sonra, PyTorch’un resmi web sitesindeki kurulum talimatlar\u0131n\u0131 takip etmek en g\u00fcvenilir yoldur. PyTorch, size i\u015fletim sisteminize, paket y\u00f6neticinize, Python s\u00fcr\u00fcm\u00fcn\u00fcze ve \u00f6zellikle CUDA s\u00fcr\u00fcm\u00fcn\u00fcze g\u00f6re \u00f6zelle\u015ftirilmi\u015f bir komut sunar.<\/p>\n
GPU Destekli Kurulum (\u00d6nerilen)<\/h5>\n
E\u011fer NVIDIA GPU’nuz ve uyumlu bir CUDA s\u00fcr\u00fcm\u00fcn\u00fcz varsa (\u00f6rne\u011fin CUDA 11.8):<\/p>\n
# Conda i\u00e7in\nconda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia\n\nPip i\u00e7in<\/h2>\npip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118<\/code><\/pre>\n
Buradaki cu118<\/code> ifadesi CUDA 11.8 s\u00fcr\u00fcm\u00fcn\u00fc temsil eder. Kendi sisteminizdeki CUDA s\u00fcr\u00fcm\u00fcne g\u00f6re bu k\u0131sm\u0131 de\u011fi\u015ftirmelisiniz. torchvision<\/code> ve torchaudio<\/code> k\u00fct\u00fcphaneleri, s\u0131ras\u0131yla bilgisayar g\u00f6r\u00fc\u015f\u00fc ve ses i\u015fleme i\u00e7in PyTorch ile entegre \u00e7al\u0131\u015fan pop\u00fcler yard\u0131mc\u0131 k\u00fct\u00fcphanelerdir.<\/p>\nCPU Destekli Kurulum<\/h5>\n
E\u011fer GPU’nuz yoksa veya GPU kullanmak istemiyorsan\u0131z, sadece CPU s\u00fcr\u00fcm\u00fcn\u00fc kurabilirsiniz:<\/p>\n
# Conda i\u00e7in\nconda install pytorch torchvision torchaudio cpuonly -c pytorch\n\nPip i\u00e7in<\/h2>\npip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cpu<\/code><\/pre>\nKurulumu Do\u011frulama<\/h4>\n
Kurulum tamamland\u0131ktan sonra, Python etkile\u015fimli kabu\u011funu a\u00e7arak PyTorch’un do\u011fru \u015fekilde kuruldu\u011funu ve GPU’nuzun tan\u0131n\u0131p tan\u0131nmad\u0131\u011f\u0131n\u0131 kontrol edebilirsiniz:<\/p>\n
import torch\nprint(torch.__version__)\nprint(torch.cuda.is_available())\nprint(torch.cuda.device_count())\nif torch.cuda.is_available():\n print(torch.cuda.current_device())\n print(torch.cuda.get_device_name(0))<\/code><\/pre>\nE\u011fer torch.cuda.is_available()<\/code> True<\/code> d\u00f6nd\u00fcr\u00fcrse, GPU’nuz ba\u015far\u0131yla alg\u0131lanm\u0131\u015f demektir.<\/p>\nPyTorch Temelleri<\/h3>\n
PyTorch’un kalbinde “Tensor”lar yatar. Tensor’lar, \u00e7ok boyutlu dizilerdir ve NumPy dizilerine benzerler, ancak GPU \u00fczerinde \u00e7al\u0131\u015fabilme yetenekleri ve otomatik t\u00fcrevlendirme deste\u011fi gibi ek \u00f6zelliklere sahiptirler.<\/p>\n
Tensor’lar: Tan\u0131m\u0131 ve Olu\u015fturma<\/h4>\n
Tensor, PyTorch’taki temel veri yap\u0131s\u0131d\u0131r. Skaler (0 boyutlu tensor), vekt\u00f6r (1 boyutlu tensor), matris (2 boyutlu tensor) veya daha y\u00fcksek boyutlu dizileri temsil edebilir.<\/p>\n
Tensor Olu\u015fturma Y\u00f6ntemleri<\/h5>\nimport torch\n\nDo\u011frudan veriden tensor olu\u015fturma<\/h2>\ndata = [[1, 2], [3, 4]]\nx_data = torch.tensor(data)\nprint(f\"Do\u011frudan veriden: \\n{x_data}\\n\")\n\nNumPy dizisinden tensor olu\u015fturma<\/h2>\nimport numpy as np\nnp_array = np.array(data)\nx_np = torch.from_numpy(np_array)\nprint(f\"NumPy'dan: \\n{x_np}\\n\")\n\nBa\u015fka bir tensordan olu\u015fturma (\u00f6zelliklerini miras al\u0131r)<\/h2>\nx_ones = torch.ones_like(x_data) # x_data ile ayn\u0131 \u015fekil ve veri tipi\nprint(f\"Birlerden olu\u015fan: \\n{x_ones}\\n\")\n\nx_rand = torch.rand_like(x_data, dtype=torch.float) # x_data ile ayn\u0131 \u015fekil, farkl\u0131 veri tipi\nprint(f\"Rastgele say\u0131lardan olu\u015fan: \\n{x_rand}\\n\")\n\nBelirli \u015fekillerde tensor olu\u015fturma<\/h2>\nshape = (2, 3,)\nones_tensor = torch.ones(shape)\nzeros_tensor = torch.zeros(shape)\nrand_tensor = torch.rand(shape)\nprint(f\"Birler tens\u00f6r\u00fc: \\n{ones_tensor}\\n\")\nprint(f\"S\u0131f\u0131rlar tens\u00f6r\u00fc: \\n{zeros_tensor}\\n\")\nprint(f\"Rastgele tens\u00f6r: \\n{rand_tensor}\\n\")<\/code><\/pre>\nTensor \u00d6zellikleri<\/h5>\n
Bir tensor’un \u015feklini, veri tipini ve depoland\u0131\u011f\u0131 cihaz\u0131 (CPU veya GPU) sorgulayabilirsiniz:<\/p>\n
tensor = torch.rand(3, 4)\nprint(f\"Tensor'un \u015fekli: {tensor.shape}\")\nprint(f\"Tensor'un veri tipi: {tensor.dtype}\")\nprint(f\"Tensor'un depoland\u0131\u011f\u0131 cihaz: {tensor.device}\")<\/code><\/pre>\nTensor \u0130\u015flemleri<\/h4>\n
Tensor’lar \u00fczerinde bir\u00e7ok aritmetik, mant\u0131ksal ve \u015fekil de\u011fi\u015ftirme i\u015flemi yapabilirsiniz.<\/p>\n
Aritmetik \u0130\u015flemler<\/h5>\n
Tensor’lar \u00fczerinde toplama, \u00e7\u0131karma, \u00e7arpma, b\u00f6lme gibi temel aritmetik i\u015flemler do\u011frudan uygulanabilir.<\/p>\n
tensor = torch.ones(4, 4)\ntensor[:,1] = 0 # \u0130kinci s\u00fctunu s\u0131f\u0131r yap\nprint(f\"Orijinal tensor:\\n{tensor}\\n\")\n\n\u0130ki tensor'u toplama<\/h2>\nt1 = torch.ones(4, 4)\nt2 = torch.rand(4, 4)\nprint(f\"t1 + t2:\\n{t1 + t2}\\n\")\n\nMatris \u00e7arp\u0131m\u0131<\/h2>\ntensor_A = torch.tensor([[1, 2], [3, 4]])\ntensor_B = torch.tensor([[5, 6], [7, 8]])\nresult_matmul = tensor_A.matmul(tensor_B) # veya tensor_A @ tensor_B\nprint(f\"Matris \u00e7arp\u0131m\u0131:\\n{result_matmul}\\n\")\n\nElement-wise \u00e7arp\u0131m<\/h2>\nresult_mul = tensor_A * tensor_B\nprint(f\"Element-wise \u00e7arp\u0131m:\\n{result_mul}\\n\")<\/code><\/pre>\n\u0130ndeksleme ve Dilimleme<\/h5>\n
NumPy’daki gibi, tensor’lar\u0131 indeksleyebilir ve dilimleyebilirsiniz.<\/p>\n
tensor = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nprint(f\"\u0130lk sat\u0131r: {tensor[0]}\\n\")\nprint(f\"\u0130lk s\u00fctun: {tensor[:, 0]}\\n\")\nprint(f\"\u0130kinci sat\u0131r, ikinci eleman: {tensor[1, 1]}\\n\")\nprint(f\"Son sat\u0131r: {tensor[-1]}\\n\")<\/code><\/pre>\n\u015eekil De\u011fi\u015ftirme<\/h5>\n
Bir tensor’un \u015feklini view()<\/code> veya reshape()<\/code> metotlar\u0131yla de\u011fi\u015ftirebilirsiniz.<\/p>\ntensor = torch.arange(1, 10) # [1, 2, ..., 9]\nprint(f\"Orijinal tensor: {tensor} (\u015eekil: {tensor.shape})\\n\")\n\nreshaped_tensor = tensor.reshape(3, 3)\nprint(f\"Yeniden \u015fekillendirilmi\u015f tensor:\\n{reshaped_tensor} (\u015eekil: {reshaped_tensor.shape})\\n\")\n\n-1 kullanarak otomatik boyut \u00e7\u0131kar\u0131m\u0131<\/h2>\nreshaped_auto = tensor.reshape(-1, 3) # 3 s\u00fctun olacak \u015fekilde sat\u0131r say\u0131s\u0131n\u0131 otomatik ayarla\nprint(f\"Otomatik boyut \u00e7\u0131kar\u0131m\u0131 ile:\\n{reshaped_auto} (\u015eekil: {reshaped_auto.shape})\\n\")<\/code><\/pre>\nGPU H\u0131zland\u0131rma<\/h4>\n
PyTorch’un en g\u00fc\u00e7l\u00fc \u00f6zelliklerinden biri, hesaplamalar\u0131 GPU’ya ta\u015f\u0131yarak h\u0131zland\u0131rma yetene\u011fidir.<\/p>\n
if torch.cuda.is_available():\n device = \"cuda\"\nelse:\n device = \"cpu\"\nprint(f\"Kullan\u0131lan cihaz: {device}\\n\")\n\ntensor = torch.ones(5, 5)\nprint(f\"CPU \u00fczerindeki tensor:\\n{tensor}\\n\")\n\ntensor_on_gpu = tensor.to(device)\nprint(f\"GPU \u00fczerindeki tensor:\\n{tensor_on_gpu}\\n\")\n\nGPU \u00fczerinde i\u015flem yapma<\/h2>\nresult_gpu = tensor_on_gpu * tensor_on_gpu\nprint(f\"GPU \u00fczerinde yap\u0131lan \u00e7arp\u0131m:\\n{result_gpu}\\n\")<\/code><\/pre>\n
Unutmay\u0131n, farkl\u0131 cihazlardaki tensor’lar aras\u0131nda do\u011frudan i\u015flem yapamazs\u0131n\u0131z. Her iki tensor’un da ayn\u0131 cihazda olmas\u0131 gerekir.<\/p>\n
Otomatik T\u00fcrevlendirme (Autograd)<\/h4>\n
Derin \u00f6\u011frenmede, modellerin a\u011f\u0131rl\u0131klar\u0131n\u0131 optimize etmek i\u00e7in gradyan ini\u015fi gibi algoritmalar kullan\u0131l\u0131r. Bu algoritmalar, kay\u0131p fonksiyonunun model parametrelerine g\u00f6re t\u00fcrevlerini (gradyanlar\u0131n\u0131) hesaplamay\u0131 gerektirir. PyTorch’un autograd<\/code> motoru, bu karma\u015f\u0131k t\u00fcrev hesaplamalar\u0131n\u0131 otomatik olarak yapar.<\/p>\nBir tensor’un gradyanlar\u0131n\u0131 hesaplanabilir hale getirmek i\u00e7in requires_grad=True<\/code> \u00f6zelli\u011fini ayarlaman\u0131z gerekir.<\/p>\nx = torch.ones(5, requires_grad=True)\ny = x + 2\nz = y y <\/em> 3\nout = z.mean()\n\nprint(f\"x: {x}\")\nprint(f\"y: {y}\")\nprint(f\"z: {z}\")\nprint(f\"out: {out}\\n\")\n\nout.backward() # Geri yay\u0131l\u0131m\u0131 ba\u015flat\u0131r, x'in gradyanlar\u0131 hesaplan\u0131r\n\nprint(f\"x'in gradyanlar\u0131: {x.grad}\")<\/code><\/pre>\nBu \u00f6rnekte, out<\/code>‘un x<\/code>‘e g\u00f6re t\u00fcrevi otomatik olarak hesaplan\u0131r ve x.grad<\/code> \u00f6zelli\u011finde saklan\u0131r. Bu, n\u00f6ral a\u011flar\u0131n e\u011fitiminde kritik bir ad\u0131md\u0131r.<\/p>\nBasit Bir N\u00f6ral A\u011f Olu\u015fturma<\/h3>\n
\u015eimdi PyTorch’un temel yap\u0131 ta\u015flar\u0131n\u0131 kullanarak basit bir n\u00f6ral a\u011f olu\u015ftural\u0131m ve e\u011fitelim. Bir regresyon problemi i\u00e7in sentetik veri \u00fczerinde \u00e7al\u0131\u015fan k\u00fc\u00e7\u00fck bir a\u011f tan\u0131mlayaca\u011f\u0131z.<\/p>\n
Veri Haz\u0131rl\u0131\u011f\u0131<\/h4>\n
\u00d6ncelikle, modelimizi e\u011fitmek i\u00e7in basit, sentetik bir veri seti olu\u015ftural\u0131m.<\/p>\n
import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\n\nSentetik veri olu\u015fturma<\/h2>\ny = 2x + 1 + g\u00fcr\u00fclt\u00fc<\/h2>\nX = torch.randn(100, 1) * 10\ny = 2 X + 1 + torch.randn(100, 1) <\/em> 2\n\nVeriyi g\u00f6rselle\u015ftirme<\/h2>\nplt.figure(figsize=(8, 6))\nplt.scatter(X.numpy(), y.numpy(), label='Sentetik Veri')\nplt.title('Sentetik Regresyon Veri Seti')\nplt.xlabel('X')\nplt.ylabel('y')\nplt.legend()\nplt.grid(True)\nplt.show()<\/code><\/pre>\n
Bu kod, 100 adet veri noktas\u0131 i\u00e7eren basit bir lineer ili\u015fki (y = 2x + 1<\/code>) ve biraz g\u00fcr\u00fclt\u00fc ile sentetik bir veri seti olu\u015fturur.<\/p>\nModel Tan\u0131mlama<\/h4>\n
PyTorch’ta n\u00f6ral a\u011flar, torch.nn.Module<\/code> s\u0131n\u0131f\u0131ndan t\u00fcretilerek tan\u0131mlan\u0131r. Bu s\u0131n\u0131f, modelin katmanlar\u0131n\u0131 ve ileri yay\u0131l\u0131m mant\u0131\u011f\u0131n\u0131 i\u00e7erir.<\/p>\nclass SimpleLinearRegression(nn.Module):\n def __init__(self):\n super(SimpleLinearRegression, self).__init__()\n # Tek bir giri\u015f \u00f6zelli\u011fi (X) ve tek bir \u00e7\u0131k\u0131\u015f \u00f6zelli\u011fi (y) i\u00e7in lineer katman\n self.linear = nn.Linear(1, 1)\n\n def forward(self, x):\n # \u0130leri yay\u0131l\u0131mda verinin katmanlardan nas\u0131l ge\u00e7ece\u011fini tan\u0131mlar\u0131z\n return self.linear(x)\n\nModeli ba\u015flatma<\/h2>\nmodel = SimpleLinearRegression()\nprint(model)<\/code><\/pre>\n
nn.Linear(in_features, out_features)<\/code> katman\u0131, giri\u015f ve \u00e7\u0131k\u0131\u015f boyutlar\u0131n\u0131 belirtir. forward<\/code> metodu, modelin giri\u015f verisini al\u0131p \u00e7\u0131kt\u0131 \u00fcretme s\u00fcrecini tan\u0131mlar.<\/p>\nKay\u0131p Fonksiyonu ve Optimizat\u00f6r<\/h4>\n
Modeli e\u011fitmek i\u00e7in bir kay\u0131p fonksiyonuna (loss function) ve bir optimizat\u00f6re ihtiyac\u0131m\u0131z var.<\/p>\n
\n- Kay\u0131p Fonksiyonu:<\/b> Modelin tahminlerinin ger\u00e7ek de\u011ferlerden ne kadar sapt\u0131\u011f\u0131n\u0131 \u00f6l\u00e7er. Regresyon problemleri i\u00e7in Genellikle
nn.MSELoss<\/code> (Mean Squared Error Loss) kullan\u0131l\u0131r.<\/li>\n- Optimizat\u00f6r:<\/b> Modelin parametrelerini (a\u011f\u0131rl\u0131klar ve bias’lar) kay\u0131p fonksiyonunu minimize edecek \u015fekilde g\u00fcnceller.
torch.optim<\/code> mod\u00fcl\u00fc SGD, Adam, RMSprop gibi bir\u00e7ok optimiz\u00f6r i\u00e7erir.<\/li>\n<\/ul>\n# Kay\u0131p fonksiyonu: Ortalama Kare Hata (Mean Squared Error)\ncriterion = nn.MSELoss()\n\nOptimizat\u00f6r: Stokastik Gradyan \u0130ni\u015fi (SGD)<\/h2>\nmodel.parameters() ile modelin \u00f6\u011frenilebilir parametrelerini optimizat\u00f6re veririz<\/h2>\noptimizer = optim.SGD(model.parameters(), lr=0.01) # lr: \u00f6\u011frenme oran\u0131 (learning rate)<\/code><\/pre>\nE\u011fitim D\u00f6ng\u00fcs\u00fc<\/h4>\n
Modeli e\u011fitmek, veriyi a\u011fdan ge\u00e7irmek, kay\u0131p hesaplamak, gradyanlar\u0131 geri yaymak ve a\u011f\u0131rl\u0131klar\u0131 g\u00fcncellemek ad\u0131mlar\u0131n\u0131 i\u00e7eren bir d\u00f6ng\u00fcd\u00fcr.<\/p>\n
num_epochs = 100 # E\u011fitim d\u00f6ng\u00fcs\u00fc say\u0131s\u0131\n\nModeli GPU'ya ta\u015f\u0131ma (e\u011fer varsa)<\/h2>\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel.to(device)\nX = X.to(device)\ny = y.to(device)\n\nloss_history = []\n\nfor epoch in range(num_epochs):\n # 1. \u0130leri yay\u0131l\u0131m (Forward pass): Tahminleri hesapla\n outputs = model(X)\n\n # 2. Kayb\u0131 hesapla\n loss = criterion(outputs, y)\n\n # 3. Geri yay\u0131l\u0131m (Backward pass): Gradyanlar\u0131 s\u0131f\u0131rla, gradyanlar\u0131 hesapla\n optimizer.zero_grad() # \u00d6nceki iterasyonlardan kalan gradyanlar\u0131 s\u0131f\u0131rla\n loss.backward() # Kayb\u0131n model parametrelerine g\u00f6re gradyanlar\u0131n\u0131 hesapla\n\n # 4. A\u011f\u0131rl\u0131klar\u0131 g\u00fcncelle\n optimizer.step() # Optimizat\u00f6r kullanarak parametreleri g\u00fcncelle\n\n loss_history.append(loss.item())\n\n if (epoch + 1) % 10 == 0:\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {loss.item():.4f}')\n\nE\u011fitim sonras\u0131 modeli CPU'ya geri ta\u015f\u0131 (gerekirse)<\/h2>\nmodel.to(\"cpu\")\nX = X.to(\"cpu\")\ny = y.to(\"cpu\")\n\nE\u011fitim kayb\u0131n\u0131 g\u00f6rselle\u015ftirme<\/h2>\nplt.figure(figsize=(8, 6))\nplt.plot(range(num_epochs), loss_history, label='E\u011fitim Kayb\u0131')\nplt.title('E\u011fitim Kayb\u0131n\u0131n Epochlara G\u00f6re De\u011fi\u015fimi')\nplt.xlabel('Epoch')\nplt.ylabel('Kay\u0131p (MSE)')\nplt.legend()\nplt.grid(True)\nplt.show()<\/code><\/pre>\n
Bu d\u00f6ng\u00fcde, her epoch’ta:
\n1. Model, giri\u015f verisi X<\/code> \u00fczerinde tahminler (outputs<\/code>) yapar.
\n2. Hesaplanan tahminler ile ger\u00e7ek de\u011ferler y<\/code> aras\u0131ndaki kay\u0131p criterion<\/code> kullan\u0131larak hesaplan\u0131r.
\n3. optimizer.zero_grad()<\/code> ile \u00f6nceki iterasyonlardan kalan gradyanlar temizlenir.
\n4. loss.backward()<\/code> \u00e7a\u011fr\u0131s\u0131, kay\u0131p fonksiyonunun modelin \u00f6\u011frenilebilir parametrelerine g\u00f6re gradyanlar\u0131n\u0131 hesaplar.
\n5. optimizer.step()<\/code> \u00e7a\u011fr\u0131s\u0131, hesaplanan gradyanlar\u0131 kullanarak modelin parametrelerini g\u00fcnceller.<\/p>\nModeli Kaydetme ve Y\u00fckleme<\/h4>\n
E\u011fitilmi\u015f bir modeli daha sonra kullanmak veya payla\u015fmak i\u00e7in kaydetmek \u00f6nemlidir.<\/p>\n
# Modeli kaydetme\ntorch.save(model.state_dict(), 'simple_linear_model.pth')\nprint(\"Model ba\u015far\u0131yla kaydedildi.\")\n\nModeli y\u00fckleme<\/h2>\n\u00d6nce bo\u015f bir model \u00f6rne\u011fi olu\u015fturmal\u0131s\u0131n\u0131z<\/h2>\nloaded_model = SimpleLinearRegression()\nloaded_model.load_state_dict(torch.load('simple_linear_model.pth'))\nloaded_model.eval() # \u00c7\u0131kar\u0131m moduna ge\u00e7 (dropout, batch norm gibi katmanlar i\u00e7in \u00f6nemli)\nprint(\"Model ba\u015far\u0131yla y\u00fcklendi.\")\n\nY\u00fcklenen model ile tahmin yapma<\/h2>\nwith torch.no_grad(): # Gradyan hesaplamay\u0131 devre d\u0131\u015f\u0131 b\u0131rak\n test_X = torch.tensor([[15.0], [-5.0]])\n predictions = loaded_model(test_X)\n print(f\"Test X: {test_X.numpy().flatten()}, Tahminler: {predictions.numpy().flatten()}\")<\/code><\/pre>\n
model.state_dict()<\/code> metodu, modelin \u00f6\u011frenilebilir parametrelerini i\u00e7eren bir s\u00f6zl\u00fck d\u00f6nd\u00fcr\u00fcr. Bu s\u00f6zl\u00fck, torch.save()<\/code> ile disk’e kaydedilir. Y\u00fckleme yaparken, \u00f6nce modelin mimarisini tan\u0131mlayan bo\u015f bir model \u00f6rne\u011fi olu\u015fturulmal\u0131, ard\u0131ndan load_state_dict()<\/code> ile kaydedilen parametreler y\u00fcklenmelidir. model.eval()<\/code> \u00e7a\u011fr\u0131s\u0131, modeli \u00e7\u0131kar\u0131m (inference) moduna ge\u00e7irir; bu, e\u011fitim s\u0131ras\u0131nda aktif olan baz\u0131 katmanlar\u0131n (\u00f6rne\u011fin dropout) davran\u0131\u015f\u0131n\u0131 de\u011fi\u015ftirir. with torch.no_grad():<\/code> blo\u011fu, \u00e7\u0131kar\u0131m s\u0131ras\u0131nda gereksiz gradyan hesaplamalar\u0131n\u0131 devre d\u0131\u015f\u0131 b\u0131rakarak bellek kullan\u0131m\u0131n\u0131 ve h\u0131z\u0131 optimize eder.<\/p>\nDaha \u0130leri Konulara K\u0131sa Bir Bak\u0131\u015f<\/h3>\n
PyTorch’un temelini anlad\u0131ktan sonra, daha karma\u015f\u0131k ve ger\u00e7ek d\u00fcnya uygulamalar\u0131na y\u00f6nelik konulara ge\u00e7i\u015f yapabilirsiniz.<\/p>\n
Veri Y\u00fckleyiciler (torch.utils.data<\/code>)<\/h4>\nGer\u00e7ek d\u00fcnya veri k\u00fcmeleri genellikle \u00e7ok b\u00fcy\u00fck olur ve belle\u011fe s\u0131\u011fmaz. torch.utils.data<\/code> mod\u00fcl\u00fc, veri k\u00fcmelerini y\u00f6netmek ve partiler (batches) halinde y\u00fcklemek i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lar sunar:<\/p>\n\nDataset<\/code>: Veri \u00f6rneklerini ve etiketlerini temsil eden bir soyut s\u0131n\u0131ft\u0131r. Kendi \u00f6zel veri setlerinizi olu\u015fturmak i\u00e7in bu s\u0131n\u0131f\u0131 t\u00fcretebilirsiniz.<\/li>\nDataLoader<\/code>: Dataset<\/code>‘ten verileri partiler halinde y\u00fckler, kar\u0131\u015ft\u0131r\u0131r (shuffle), paralel y\u00fckleme i\u00e7in \u00e7oklu i\u015flem kullanabilir.<\/li>\n<\/ul>\nfrom torch.utils.data import Dataset, DataLoader\n\nclass CustomDataset(Dataset):\n def __init__(self, X, y):\n self.X = X\n self.y = y\n\n def __len__(self):\n return len(self.X)\n\n def __getitem__(self, idx):\n return self.X[idx], self.y[idx]\n\n\u00d6rnek kullan\u0131m<\/h2>\ndataset = CustomDataset(X, y)\ndataloader = DataLoader(dataset, batch_size=32, shuffle=True)\n\nfor batch_X, batch_y in dataloader:\n # Her bir batch \u00fczerinde e\u011fitim yapabilirsiniz\n # print(batch_X.shape, batch_y.shape)\n pass<\/code><\/pre>\n\u00d6nceden E\u011fitilmi\u015f Modeller (torchvision.models)<\/h4>\n
Bilgisayar g\u00f6r\u00fc\u015f\u00fc alan\u0131nda, ImageNet gibi b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde e\u011fitilmi\u015f g\u00fc\u00e7l\u00fc modeller (ResNet, VGG, AlexNet vb.) mevcuttur. torchvision.models<\/code> mod\u00fcl\u00fc, bu modelleri kolayca y\u00fcklemenizi ve transfer \u00f6\u011frenimi i\u00e7in kullanman\u0131z\u0131 sa\u011flar.<\/p>\nimport torchvision.models as models\n\n\u00d6nceden e\u011fitilmi\u015f bir ResNet-18 modelini y\u00fckle<\/h2>\nresnet18 = models.resnet18(pretrained=True)\nprint(resnet18)\n\nSon katman\u0131 kendi problemimize g\u00f6re de\u011fi\u015ftirme (transfer \u00f6\u011frenimi i\u00e7in)<\/h2>\nnum_ftrs = resnet18.fc.in_features\nresnet18.fc = nn.Linear(num_ftrs, num_classes) # num_classes kendi s\u0131n\u0131f say\u0131n\u0131z<\/code><\/pre>\nTransfer \u00d6\u011frenimi<\/h4>\n
\u00d6nceden e\u011fitilmi\u015f modelleri kullanarak, az veriyle bile yeni g\u00f6revler i\u00e7in y\u00fcksek performansl\u0131 modeller geli\u015ftirebilirsiniz. Bu teknik, modelin genel \u00f6zellikleri \u00f6\u011frenmi\u015f katmanlar\u0131n\u0131 dondurup (e\u011fitmemek) sadece son katmanlar\u0131n\u0131 kendi veriniz \u00fczerinde e\u011fitmeyi i\u00e7erir.<\/p>\n
Da\u011f\u0131t\u0131k E\u011fitim<\/h4>\n
B\u00fcy\u00fck modelleri ve veri k\u00fcmelerini e\u011fitmek i\u00e7in birden fazla GPU veya birden fazla makine kullanmak gerekebilir. PyTorch, torch.distributed<\/code> mod\u00fcl\u00fc ile da\u011f\u0131t\u0131k e\u011fitimi destekler, bu da e\u011fitim s\u00fcrecini paralel hale getirerek h\u0131zland\u0131r\u0131r.<\/p>\nSonu\u00e7<\/h3>\n
PyTorch, esnek, Pythonic ve g\u00fc\u00e7l\u00fc bir derin \u00f6\u011frenme k\u00fct\u00fcphanesidir. Dinamik hesaplama grafikleri, sezgisel API’si ve g\u00fc\u00e7l\u00fc GPU h\u0131zland\u0131rma deste\u011fi sayesinde, hem ara\u015ft\u0131rmac\u0131lar hem de geli\u015ftiriciler i\u00e7in derin \u00f6\u011frenme projelerini hayata ge\u00e7irmeyi kolayla\u015ft\u0131r\u0131r. Bu makalede, PyTorch’u nas\u0131l kuraca\u011f\u0131n\u0131z\u0131, tensor’lar gibi temel yap\u0131 ta\u015flar\u0131n\u0131 nas\u0131l kullanaca\u011f\u0131n\u0131z\u0131, otomatik t\u00fcrevlendirme mekanizmas\u0131n\u0131 ve basit bir n\u00f6ral a\u011f\u0131 nas\u0131l olu\u015fturup e\u011fitece\u011finizi ayr\u0131nt\u0131l\u0131 bir \u015fekilde ele ald\u0131k. Ayr\u0131ca, veri y\u00fckleyiciler ve \u00f6nceden e\u011fitilmi\u015f modeller gibi daha ileri konulara da k\u0131saca de\u011findik.<\/p>\n
PyTorch’un mod\u00fcler yap\u0131s\u0131, karma\u015f\u0131k model mimarilerini denemenize ve kendi \u00f6zel katmanlar\u0131n\u0131z\u0131 kolayca tan\u0131mlaman\u0131za olanak tan\u0131r. Geni\u015f ve aktif bir toplulu\u011fa sahip olmas\u0131, kar\u015f\u0131la\u015fabilece\u011finiz sorunlara h\u0131zl\u0131ca \u00e7\u00f6z\u00fcm bulman\u0131z\u0131 sa\u011flar. Derin \u00f6\u011frenme d\u00fcnyas\u0131na ad\u0131m atmak veya mevcut projelerinizi daha verimli hale getirmek istiyorsan\u0131z, PyTorch \u00f6\u011frenmeye de\u011fer bir yat\u0131r\u0131md\u0131r.<\/p>\n
\u00d6\u011frenme yolculu\u011funuza devam ederken, PyTorch’un resmi belgelerini, online dersleri ve GitHub’daki \u00f6rnek projeleri ke\u015ffetmeniz \u015fiddetle tavsiye edilir. Pratik yaparak ve farkl\u0131 model mimarileriyle deneyler yaparak PyTorch’taki yetkinli\u011finizi art\u0131rabilirsiniz. Derin \u00f6\u011frenmenin heyecan verici d\u00fcnyas\u0131nda PyTorch ile ba\u015far\u0131l\u0131 projelere imza atman\u0131z dile\u011fiyle!<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"PyTorch Kurulumu ve Kullan\u0131m\u0131: Derin \u00d6\u011frenmeye Kapsaml\u0131 Bir Giri\u015f\nGiri\u015f\nDerin \u00f6\u011frenme, yapay zekan\u0131n en h\u0131zl\u0131 geli\u015fen ve en etkili alanl","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-38988","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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