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"source": [
"### AITech — Uczenie maszynowe — laboratoria\n",
"# 10. Sieci neuronowe (PyTorch)"
]
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"Przykład implementacji sieci neuronowej do rozpoznawania cyfr ze [zbioru MNIST](https://en.wikipedia.org/wiki/MNIST_database), według https://github.com/pytorch/examples/tree/master/mnist"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"from torchvision import datasets, transforms\n",
"from torch.optim.lr_scheduler import StepLR\n",
"\n",
"\n",
"class Net(nn.Module):\n",
" \"\"\"W PyTorchu tworzenie sieci neuronowej\n",
" polega na zdefiniowaniu klasy, która dziedziczy z nn.Module.\n",
" \"\"\"\n",
" \n",
" def __init__(self):\n",
" super().__init__()\n",
" \n",
" # Warstwy splotowe\n",
" self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
" self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
" \n",
" # Warstwy dropout\n",
" self.dropout1 = nn.Dropout(0.25)\n",
" self.dropout2 = nn.Dropout(0.5)\n",
" \n",
" # Warstwy liniowe\n",
" self.fc1 = nn.Linear(9216, 128)\n",
" self.fc2 = nn.Linear(128, 10)\n",
"\n",
" def forward(self, x):\n",
" \"\"\"Definiujemy przechodzenie \"do przodu\" jako kolejne przekształcenia wejścia x\"\"\"\n",
" x = self.conv1(x)\n",
" x = F.relu(x)\n",
" x = self.conv2(x)\n",
" x = F.relu(x)\n",
" x = F.max_pool2d(x, 2)\n",
" x = self.dropout1(x)\n",
" x = torch.flatten(x, 1)\n",
" x = self.fc1(x)\n",
" x = F.relu(x)\n",
" x = self.dropout2(x)\n",
" x = self.fc2(x)\n",
" output = F.log_softmax(x, dim=1)\n",
" return output\n",
"\n",
"\n",
"def train(model, device, train_loader, optimizer, epoch, log_interval, dry_run):\n",
" \"\"\"Uczenie modelu\"\"\"\n",
" model.train()\n",
" for batch_idx, (data, target) in enumerate(train_loader):\n",
" data, target = data.to(device), target.to(device) # wrzucenie danych na kartę graficzną (jeśli dotyczy)\n",
" optimizer.zero_grad() # wyzerowanie gradientu\n",
" output = model(data) # przejście \"do przodu\"\n",
" loss = F.nll_loss(output, target) # obliczenie funkcji kosztu\n",
" loss.backward() # propagacja wsteczna\n",
" optimizer.step() # krok optymalizatora\n",
" if batch_idx % log_interval == 0:\n",
" print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
" epoch, batch_idx * len(data), len(train_loader.dataset),\n",
" 100. * batch_idx / len(train_loader), loss.item()))\n",
" if dry_run:\n",
" break\n",
"\n",
"\n",
"def test(model, device, test_loader):\n",
" \"\"\"Testowanie modelu\"\"\"\n",
" model.eval()\n",
" test_loss = 0\n",
" correct = 0\n",
" with torch.no_grad():\n",
" for data, target in test_loader:\n",
" data, target = data.to(device), target.to(device) # wrzucenie danych na kartę graficzną (jeśli dotyczy)\n",
" output = model(data) # przejście \"do przodu\"\n",
" test_loss += F.nll_loss(output, target, reduction='sum').item() # suma kosztów z każdego batcha\n",
" pred = output.argmax(dim=1, keepdim=True) # predykcja na podstawie maks. logarytmu prawdopodobieństwa\n",
" correct += pred.eq(target.view_as(pred)).sum().item()\n",
"\n",
" test_loss /= len(test_loader.dataset) # obliczenie kosztu na zbiorze testowym\n",
"\n",
" print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
" test_loss, correct, len(test_loader.dataset),\n",
" 100. * correct / len(test_loader.dataset)))\n",
"\n",
"\n",
"def run(\n",
" batch_size=64,\n",
" test_batch_size=1000,\n",
" epochs=14,\n",
" lr=1.0,\n",
" gamma=0.7,\n",
" no_cuda=False,\n",
" dry_run=False,\n",
" seed=1,\n",
" log_interval=10,\n",
" save_model=False,\n",
" ):\n",
" \"\"\"Main training function.\n",
" \n",
" Arguments:\n",
" batch_size - wielkość batcha podczas uczenia (default: 64),\n",
" test_batch_size - wielkość batcha podczas testowania (default: 1000)\n",
" epochs - liczba epok uczenia (default: 14)\n",
" lr - współczynnik uczenia (learning rate) (default: 1.0)\n",
" gamma - współczynnik gamma (dla optymalizatora) (default: 0.7)\n",
" no_cuda - wyłącza uczenie na karcie graficznej (default: False)\n",
" dry_run - szybko (\"na sucho\") sprawdza pojedyncze przejście (default: False)\n",
" seed - ziarno generatora liczb pseudolosowych (default: 1)\n",
" log_interval - interwał logowania stanu uczenia (default: 10)\n",
" save_model - zapisuje bieżący model (default: False)\n",
" \"\"\"\n",
" use_cuda = no_cuda and torch.cuda.is_available()\n",
"\n",
" torch.manual_seed(seed)\n",
"\n",
" device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
"\n",
" train_kwargs = {'batch_size': batch_size}\n",
" test_kwargs = {'batch_size': test_batch_size}\n",
" if use_cuda:\n",
" cuda_kwargs = {'num_workers': 1,\n",
" 'pin_memory': True,\n",
" 'shuffle': True}\n",
" train_kwargs.update(cuda_kwargs)\n",
" test_kwargs.update(cuda_kwargs)\n",
"\n",
" transform=transforms.Compose([\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.1307,), (0.3081,))\n",
" ])\n",
" dataset1 = datasets.MNIST('../data', train=True, download=True,\n",
" transform=transform)\n",
" dataset2 = datasets.MNIST('../data', train=False,\n",
" transform=transform)\n",
" train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)\n",
" test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)\n",
"\n",
" model = Net().to(device)\n",
" optimizer = optim.Adadelta(model.parameters(), lr=lr)\n",
"\n",
" scheduler = StepLR(optimizer, step_size=1, gamma=gamma)\n",
" for epoch in range(1, epochs + 1):\n",
" train(model, device, train_loader, optimizer, epoch, log_interval, dry_run)\n",
" test(model, device, test_loader)\n",
" scheduler.step()\n",
"\n",
" if save_model:\n",
" torch.save(model.state_dict(), \"mnist_cnn.pt\")"
]
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"source": [
"**Uwaga**: uruchomienie tego przykładu długo trwa. Żeby trwało krócej, można zmniejszyć liczbę epok."
]
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"scrolled": true
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"C:\\Users\\pawel\\anaconda3\\lib\\site-packages\\torch\\autograd\\__init__.py:130: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 9020). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ..\\c10\\cuda\\CUDAFunctions.cpp:100.)\n",
" Variable._execution_engine.run_backward(\n"
]
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"\n",
"Test set: Average loss: 0.0452, Accuracy: 9848/10000 (98%)\n",
"\n",
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"\n",
"Test set: Average loss: 0.0345, Accuracy: 9876/10000 (99%)\n",
"\n",
"Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.017473\n",
"Train Epoch: 3 [640/60000 (1%)]\tLoss: 0.018726\n",
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"Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.004794\n",
"Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.000842\n",
"\n",
"Test set: Average loss: 0.0319, Accuracy: 9889/10000 (99%)\n",
"\n",
"Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.011954\n",
"Train Epoch: 4 [640/60000 (1%)]\tLoss: 0.011093\n",
"Train Epoch: 4 [1280/60000 (2%)]\tLoss: 0.022356\n",
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"Train Epoch: 4 [28160/60000 (47%)]\tLoss: 0.065214\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Epoch: 4 [28800/60000 (48%)]\tLoss: 0.005579\n",
"Train Epoch: 4 [29440/60000 (49%)]\tLoss: 0.010757\n",
"Train Epoch: 4 [30080/60000 (50%)]\tLoss: 0.013080\n",
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"Train Epoch: 4 [58880/60000 (98%)]\tLoss: 0.000889\n",
"Train Epoch: 4 [59520/60000 (99%)]\tLoss: 0.001196\n",
"\n",
"Test set: Average loss: 0.0311, Accuracy: 9886/10000 (99%)\n",
"\n",
"Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.015992\n",
"Train Epoch: 5 [640/60000 (1%)]\tLoss: 0.012034\n",
"Train Epoch: 5 [1280/60000 (2%)]\tLoss: 0.012463\n",
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"Train Epoch: 5 [49920/60000 (83%)]\tLoss: 0.021935\n",
"Train Epoch: 5 [50560/60000 (84%)]\tLoss: 0.009369\n",
"Train Epoch: 5 [51200/60000 (85%)]\tLoss: 0.133733\n",
"Train Epoch: 5 [51840/60000 (86%)]\tLoss: 0.004490\n",
"Train Epoch: 5 [52480/60000 (87%)]\tLoss: 0.004431\n",
"Train Epoch: 5 [53120/60000 (88%)]\tLoss: 0.022499\n",
"Train Epoch: 5 [53760/60000 (90%)]\tLoss: 0.111768\n",
"Train Epoch: 5 [54400/60000 (91%)]\tLoss: 0.021636\n",
"Train Epoch: 5 [55040/60000 (92%)]\tLoss: 0.002808\n",
"Train Epoch: 5 [55680/60000 (93%)]\tLoss: 0.007162\n",
"Train Epoch: 5 [56320/60000 (94%)]\tLoss: 0.012326\n",
"Train Epoch: 5 [56960/60000 (95%)]\tLoss: 0.002056\n",
"Train Epoch: 5 [57600/60000 (96%)]\tLoss: 0.003829\n",
"Train Epoch: 5 [58240/60000 (97%)]\tLoss: 0.013328\n",
"Train Epoch: 5 [58880/60000 (98%)]\tLoss: 0.000146\n",
"Train Epoch: 5 [59520/60000 (99%)]\tLoss: 0.000575\n",
"\n",
"Test set: Average loss: 0.0299, Accuracy: 9903/10000 (99%)\n",
"\n"
]
}
],
"source": [
"run(epochs=5)"
]
},
{
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"attachments": {},
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"Polecam również bibliotekę [PyTorch-Lightning](https://www.pytorchlightning.ai), dzięki któej kod PyTorcha staje się trochę bardziej \"uporządkowany\"."
]
},
{
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"attachments": {},
2023-01-13 14:19:50 +01:00
"cell_type": "markdown",
"metadata": {},
"source": [
"Tutaj artykuł o tym, jak stworzyć dataloader dla danych z własnego pliku CSV: https://androidkt.com/load-pandas-dataframe-using-dataset-and-dataloader-in-pytorch"
]
}
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