286 lines
26 KiB
Plaintext
286 lines
26 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"### Uczenie maszynowe — zastosowania\n",
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"# Sieci neuronowe (PyTorch)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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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"
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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": 2,
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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.nn.functional as F\n",
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"import torch.optim as optim\n",
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"from torchvision import datasets, transforms\n",
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"from torch.optim.lr_scheduler import StepLR\n",
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"\n",
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"\n",
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"# Ta sieć ma taką samą architekturę, jak sieć z pliku Sieci_neuronowe_Keras.ipynb\n",
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"\n",
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"class Net(nn.Module):\n",
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" \"\"\"W PyTorchu tworzenie sieci neuronowej\n",
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" polega na zdefiniowaniu klasy, która dziedziczy z nn.Module.\n",
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" \"\"\"\n",
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" \n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" \n",
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" # Warstwy splotowe\n",
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" self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
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" self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
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" \n",
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" # Warstwa dropout\n",
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" self.dropout = nn.Dropout(0.5)\n",
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" \n",
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" # Warstwa liniowa (gęsta)\n",
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" self.dense = nn.Linear(9216, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" \"\"\"Definiujemy przechodzenie \"do przodu\" jako kolejne przekształcenia wejścia x\"\"\"\n",
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" x = self.conv1(x)\n",
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" x = F.relu(x)\n",
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" x = F.max_pool2d(x, 2)\n",
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" x = self.conv2(x)\n",
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" x = F.relu(x)\n",
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" x = F.max_pool2d(x, 2)\n",
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" x = torch.flatten(x, 1)\n",
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" x = self.dropout(x)\n",
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" x = self.dense(x)\n",
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" output = F.log_softmax(x, dim=1)\n",
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" return output\n",
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"\n",
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"\n",
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"def train(model, device, train_loader, optimizer, epoch, log_interval, dry_run):\n",
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" \"\"\"Uczenie modelu\"\"\"\n",
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" model.train()\n",
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" for batch_idx, (data, target) in enumerate(train_loader):\n",
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" data, target = data.to(device), target.to(device) # wrzucenie danych na kartę graficzną (jeśli dotyczy)\n",
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" optimizer.zero_grad() # wyzerowanie gradientu\n",
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" output = model(data) # przejście \"do przodu\"\n",
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" loss = F.nll_loss(output, target) # obliczenie funkcji kosztu\n",
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" loss.backward() # propagacja wsteczna\n",
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" optimizer.step() # krok optymalizatora\n",
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" \n",
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" # Wypisanie wartości funkcji kosztu na ekran\n",
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" if batch_idx % log_interval == 0:\n",
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" print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
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" epoch, batch_idx * len(data), len(train_loader.dataset),\n",
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" 100. * batch_idx / len(train_loader), loss.item()))\n",
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" if dry_run:\n",
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" break\n",
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"\n",
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"\n",
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"def test(model, device, test_loader):\n",
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" \"\"\"Testowanie modelu\"\"\"\n",
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" model.eval()\n",
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" test_loss = 0\n",
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" correct = 0\n",
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" with torch.no_grad(): # nie musimy przechowywać gradientów, bo nie wykonujemy propagacji wstecznej\n",
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" for data, target in test_loader:\n",
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" data, target = data.to(device), target.to(device) # wrzucenie danych na kartę graficzną (jeśli dotyczy)\n",
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" output = model(data) # przejście \"do przodu\"\n",
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" test_loss += F.nll_loss(output, target, reduction='sum').item() # suma kosztów z każdego batcha\n",
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" pred = output.argmax(dim=1, keepdim=True) # predykcja na podstawie maks. logarytmu prawdopodobieństwa\n",
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" correct += pred.eq(target.view_as(pred)).sum().item()\n",
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"\n",
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" test_loss /= len(test_loader.dataset) # obliczenie kosztu na zbiorze testowym\n",
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"\n",
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" print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
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" test_loss, correct, len(test_loader.dataset),\n",
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" 100. * correct / len(test_loader.dataset)))\n",
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"\n",
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"\n",
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"def run(\n",
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" batch_size=64,\n",
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" test_batch_size=1000,\n",
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" epochs=14,\n",
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" lr=1.0,\n",
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" gamma=0.7,\n",
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" no_cuda=False,\n",
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" dry_run=False,\n",
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" seed=1,\n",
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" log_interval=10,\n",
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" save_model=False,\n",
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" ):\n",
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" \"\"\"Główna funkcja służąca do uruchamiania przykładu.\n",
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" \n",
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" Argumenty:\n",
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" batch_size - wielkość batcha podczas uczenia (default: 64),\n",
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" test_batch_size - wielkość batcha podczas testowania (default: 1000)\n",
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" epochs - liczba epok uczenia (default: 14)\n",
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" lr - współczynnik uczenia (learning rate) (default: 1.0)\n",
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" gamma - współczynnik gamma (dla optymalizatora) (default: 0.7)\n",
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" no_cuda - wyłącza uczenie na karcie graficznej (default: False)\n",
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" dry_run - szybko (\"na sucho\") sprawdza pojedyncze przejście (default: False)\n",
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" seed - ziarno generatora liczb pseudolosowych (default: 1)\n",
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" log_interval - interwał logowania stanu uczenia (default: 10)\n",
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" save_model - zapisuje bieżący model (default: False)\n",
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" \"\"\"\n",
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" # Ustawienie ziarna generatora liczb pseudolosowych\n",
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" torch.manual_seed(seed)\n",
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"\n",
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" # Ustawienie, czy uczenie ma odbywać się z wykorzystaniem karty graficznej\n",
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" use_cuda = no_cuda and torch.cuda.is_available()\n",
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" device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
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"\n",
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" # Ustawienie parametrów wsadu\n",
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" train_kwargs = {'batch_size': batch_size}\n",
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" test_kwargs = {'batch_size': test_batch_size}\n",
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" if use_cuda:\n",
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" cuda_kwargs = {'num_workers': 1,\n",
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" 'pin_memory': True,\n",
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" 'shuffle': True}\n",
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" train_kwargs.update(cuda_kwargs)\n",
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" test_kwargs.update(cuda_kwargs)\n",
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"\n",
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" transform=transforms.Compose([\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize((0.1307,), (0.3081,))\n",
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" ])\n",
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" \n",
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" # Załadowanie danych\n",
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" dataset1 = datasets.MNIST('../data', train=True, download=True,\n",
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" transform=transform)\n",
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" dataset2 = datasets.MNIST('../data', train=False,\n",
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" transform=transform)\n",
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" \n",
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" # Klasa DataLoader ułatwia zarządzanie danymi uczącymi\n",
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" train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)\n",
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" test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)\n",
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"\n",
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" # Stworzenie modelu - klasa Net to zdefiniowana przez nas sieć neuronowa\n",
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" model = Net().to(device)\n",
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" \n",
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" # Wybór metody optymalizacji (tutaj: Adadelta)\n",
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" optimizer = optim.Adadelta(model.parameters(), lr=lr)\n",
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"\n",
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" # Scheduler - kwestie techniczne\n",
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" scheduler = StepLR(optimizer, step_size=1, gamma=gamma)\n",
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" \n",
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" # Pętla główna uczenia i testowania - kolejne epoki\n",
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" for epoch in range(1, epochs + 1):\n",
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" train(model, device, train_loader, optimizer, epoch, log_interval, dry_run)\n",
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" test(model, device, test_loader)\n",
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" scheduler.step()\n",
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"\n",
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" # Opcjonalne zapisanie wytrenowanego modelu do pliku\n",
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" if save_model:\n",
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" torch.save(model.state_dict(), \"mnist_cnn.pt\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Uwaga**: uruchomienie tego przykładu długo trwa. Żeby trwało krócej, można zmniejszyć liczbę epok."
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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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"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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"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data\\MNIST\\raw\\train-images-idx3-ubyte.gz\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "02f11492b1ff4fdfa05d7dd086c5989b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"ename": "HTTPError",
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"evalue": "HTTP Error 503: Service Unavailable",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mHTTPError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-3-c80278f8b80c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdry_run\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;32m<ipython-input-2-55aa66586ee8>\u001b[0m in \u001b[0;36mrun\u001b[1;34m(batch_size, test_batch_size, epochs, lr, gamma, no_cuda, dry_run, seed, log_interval, save_model)\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1307\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.3081\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 126\u001b[0m ])\n\u001b[1;32m--> 127\u001b[1;33m dataset1 = datasets.MNIST('../data', train=True, download=True,\n\u001b[0m\u001b[0;32m 128\u001b[0m transform=transform)\n\u001b[0;32m 129\u001b[0m dataset2 = datasets.MNIST('../data', train=False,\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\datasets\\mnist.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, root, train, transform, target_transform, download)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdownload\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_exists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\datasets\\mnist.py\u001b[0m in \u001b[0;36mdownload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 144\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd5\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresources\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m \u001b[0mdownload_and_extract_archive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdownload_root\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw_folder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd5\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmd5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 148\u001b[0m \u001b[1;31m# process and save as torch files\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\datasets\\utils.py\u001b[0m in \u001b[0;36mdownload_and_extract_archive\u001b[1;34m(url, download_root, extract_root, filename, md5, remove_finished)\u001b[0m\n\u001b[0;32m 254\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 255\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m \u001b[0mdownload_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdownload_root\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[0marchive\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdownload_root\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\datasets\\utils.py\u001b[0m in \u001b[0;36mdownload_url\u001b[1;34m(url, root, filename, md5)\u001b[0m\n\u001b[0;32m 82\u001b[0m )\n\u001b[0;32m 83\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 85\u001b[0m \u001b[1;31m# check integrity of downloaded file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcheck_integrity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\datasets\\utils.py\u001b[0m in \u001b[0;36mdownload_url\u001b[1;34m(url, root, filename, md5)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Downloading '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0murl\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m urllib.request.urlretrieve(\n\u001b[0m\u001b[0;32m 71\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mreporthook\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgen_bar_updater\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[1;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[0murl_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_splittype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 248\u001b[0m \u001b[0mheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m 529\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 530\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 533\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m 638\u001b[0m \u001b[1;31m# request was successfully received, understood, and accepted.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 639\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 640\u001b[1;33m response = self.parent.error(\n\u001b[0m\u001b[0;32m 641\u001b[0m 'http', request, response, code, msg, hdrs)\n\u001b[0;32m 642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36merror\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m 567\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 568\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'default'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'http_error_default'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 569\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 571\u001b[0m \u001b[1;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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||
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"\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 501\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 503\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 504\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[1;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[0;32m 647\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPDefaultErrorHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 648\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 649\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 651\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPRedirectHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||
|
|
"\u001b[1;31mHTTPError\u001b[0m: HTTP Error 503: Service Unavailable"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"run(epochs=5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"celltoolbar": "Slideshow",
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.3"
|
||
|
|
},
|
||
|
|
"livereveal": {
|
||
|
|
"start_slideshow_at": "selected",
|
||
|
|
"theme": "amu"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|