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Laboratoria - sieci neuronowe

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Paweł Skórzewski 2023-01-13 14:19:50 +01:00
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lab/Sieci_neuronowe_Keras.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"### AITech — Uczenie maszynowe — laboratoria\n",
"# 11. Sieci neuronowe (Keras)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keras to napisany w języku Python interfejs do platformy TensorFlow, służącej do uczenia maszynowego.\n",
"\n",
"Aby z niego korzystać, trzeba zainstalować bibliotekę TensorFlow:\n",
" * `pip`: https://www.tensorflow.org/install\n",
" * `conda`: https://docs.anaconda.com/anaconda/user-guide/tasks/tensorflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Przykład implementacji sieci neuronowej do rozpoznawania cyfr ze zbioru MNIST, według https://keras.io/examples/vision/mnist_convnet"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Konieczne importy\n",
"\n",
"import numpy as np\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
"11493376/11490434 [==============================] - 1s 0us/step\n",
"x_train shape: (60000, 28, 28, 1)\n",
"60000 train samples\n",
"10000 test samples\n"
]
}
],
"source": [
"# Przygotowanie danych\n",
"\n",
"num_classes = 10\n",
"input_shape = (28, 28, 1)\n",
"\n",
"# podział danych na zbiory uczący i testowy\n",
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
"\n",
"# skalowanie obrazów do przedziału [0, 1]\n",
"x_train = x_train.astype(\"float32\") / 255\n",
"x_test = x_test.astype(\"float32\") / 255\n",
"# upewnienie się, że obrazy mają wymiary (28, 28, 1)\n",
"x_train = np.expand_dims(x_train, -1)\n",
"x_test = np.expand_dims(x_test, -1)\n",
"print(\"x_train shape:\", x_train.shape)\n",
"print(x_train.shape[0], \"train samples\")\n",
"print(x_test.shape[0], \"test samples\")\n",
"\n",
"# konwersja danych kategorycznych na binarne\n",
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d (Conv2D) (None, 26, 26, 32) 320 \n",
"_________________________________________________________________\n",
"max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 1600) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 1600) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 10) 16010 \n",
"=================================================================\n",
"Total params: 34,826\n",
"Trainable params: 34,826\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Stworzenie modelu\n",
"\n",
"model = keras.Sequential(\n",
" [\n",
" keras.Input(shape=input_shape),\n",
" layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" layers.Flatten(),\n",
" layers.Dropout(0.5),\n",
" layers.Dense(num_classes, activation=\"softmax\"),\n",
" ]\n",
")\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"422/422 [==============================] - 38s 91ms/step - loss: 0.0556 - accuracy: 0.9826 - val_loss: 0.0412 - val_accuracy: 0.9893\n"
]
},
{
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x1a50b35a070>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Uczenie modelu\n",
"\n",
"batch_size = 128\n",
"epochs = 15\n",
"\n",
"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
"\n",
"model.fit(x_train, y_train, epochs=1, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.03675819933414459\n",
"Test accuracy: 0.988099992275238\n"
]
}
],
"source": [
"# Ewaluacja modelu\n",
"\n",
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print(\"Test loss:\", score[0])\n",
"print(\"Test accuracy:\", score[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zadanie 11 (6 punktów)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zaimplementuj rozwiązanie wybranego problemu klasyfikacyjnego za pomocą prostej sieci neuronowej stworzonej przy użyciu biblioteki Keras."
]
}
],
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lab/Sieci_neuronowe_PyTorch.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"### AITech — Uczenie maszynowe — laboratoria\n",
"# 10. Sieci neuronowe (PyTorch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Uwaga**: uruchomienie tego przykładu długo trwa. Żeby trwało krócej, można zmniejszyć liczbę epok."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.305400\n",
"Train Epoch: 1 [640/60000 (1%)]\tLoss: 1.359776\n",
"Train Epoch: 1 [1280/60000 (2%)]\tLoss: 0.842885\n",
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"\n",
"Test set: Average loss: 0.0452, Accuracy: 9848/10000 (98%)\n",
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"text": [
"Train Epoch: 2 [44800/60000 (75%)]\tLoss: 0.059499\n",
"Train Epoch: 2 [45440/60000 (76%)]\tLoss: 0.109399\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",
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"\n",
"Test set: Average loss: 0.0319, Accuracy: 9889/10000 (99%)\n",
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"Train Epoch: 4 [28800/60000 (48%)]\tLoss: 0.005579\n",
"Train Epoch: 4 [29440/60000 (49%)]\tLoss: 0.010757\n",
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"Train Epoch: 4 [51840/60000 (86%)]\tLoss: 0.012308\n",
"Train Epoch: 4 [52480/60000 (87%)]\tLoss: 0.024157\n",
"Train Epoch: 4 [53120/60000 (88%)]\tLoss: 0.003395\n",
"Train Epoch: 4 [53760/60000 (90%)]\tLoss: 0.084941\n",
"Train Epoch: 4 [54400/60000 (91%)]\tLoss: 0.057644\n",
"Train Epoch: 4 [55040/60000 (92%)]\tLoss: 0.002062\n",
"Train Epoch: 4 [55680/60000 (93%)]\tLoss: 0.038266\n",
"Train Epoch: 4 [56320/60000 (94%)]\tLoss: 0.006398\n",
"Train Epoch: 4 [56960/60000 (95%)]\tLoss: 0.007706\n",
"Train Epoch: 4 [57600/60000 (96%)]\tLoss: 0.027255\n",
"Train Epoch: 4 [58240/60000 (97%)]\tLoss: 0.044076\n",
"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",
"Train Epoch: 5 [1920/60000 (3%)]\tLoss: 0.053295\n",
"Train Epoch: 5 [2560/60000 (4%)]\tLoss: 0.013971\n",
"Train Epoch: 5 [3200/60000 (5%)]\tLoss: 0.008351\n",
"Train Epoch: 5 [3840/60000 (6%)]\tLoss: 0.000522\n",
"Train Epoch: 5 [4480/60000 (7%)]\tLoss: 0.056046\n",
"Train Epoch: 5 [5120/60000 (9%)]\tLoss: 0.226117\n",
"Train Epoch: 5 [5760/60000 (10%)]\tLoss: 0.024622\n",
"Train Epoch: 5 [6400/60000 (11%)]\tLoss: 0.114540\n",
"Train Epoch: 5 [7040/60000 (12%)]\tLoss: 0.164275\n",
"Train Epoch: 5 [7680/60000 (13%)]\tLoss: 0.015020\n",
"Train Epoch: 5 [8320/60000 (14%)]\tLoss: 0.009615\n",
"Train Epoch: 5 [8960/60000 (15%)]\tLoss: 0.060808\n",
"Train Epoch: 5 [9600/60000 (16%)]\tLoss: 0.021185\n",
"Train Epoch: 5 [10240/60000 (17%)]\tLoss: 0.071090\n",
"Train Epoch: 5 [10880/60000 (18%)]\tLoss: 0.004819\n",
"Train Epoch: 5 [11520/60000 (19%)]\tLoss: 0.044744\n",
"Train Epoch: 5 [12160/60000 (20%)]\tLoss: 0.036432\n",
"Train Epoch: 5 [12800/60000 (21%)]\tLoss: 0.007292\n",
"Train Epoch: 5 [13440/60000 (22%)]\tLoss: 0.005680\n",
"Train Epoch: 5 [14080/60000 (23%)]\tLoss: 0.003425\n",
"Train Epoch: 5 [14720/60000 (25%)]\tLoss: 0.055383\n",
"Train Epoch: 5 [15360/60000 (26%)]\tLoss: 0.007300\n",
"Train Epoch: 5 [16000/60000 (27%)]\tLoss: 0.034897\n",
"Train Epoch: 5 [16640/60000 (28%)]\tLoss: 0.126585\n",
"Train Epoch: 5 [17280/60000 (29%)]\tLoss: 0.001609\n",
"Train Epoch: 5 [17920/60000 (30%)]\tLoss: 0.011380\n",
"Train Epoch: 5 [18560/60000 (31%)]\tLoss: 0.031130\n",
"Train Epoch: 5 [19200/60000 (32%)]\tLoss: 0.030126\n",
"Train Epoch: 5 [19840/60000 (33%)]\tLoss: 0.111376\n",
"Train Epoch: 5 [20480/60000 (34%)]\tLoss: 0.005547\n",
"Train Epoch: 5 [21120/60000 (35%)]\tLoss: 0.123237\n",
"Train Epoch: 5 [21760/60000 (36%)]\tLoss: 0.023191\n",
"Train Epoch: 5 [22400/60000 (37%)]\tLoss: 0.001363\n",
"Train Epoch: 5 [23040/60000 (38%)]\tLoss: 0.057234\n",
"Train Epoch: 5 [23680/60000 (39%)]\tLoss: 0.015569\n",
"Train Epoch: 5 [24320/60000 (41%)]\tLoss: 0.000795\n",
"Train Epoch: 5 [24960/60000 (42%)]\tLoss: 0.000723\n",
"Train Epoch: 5 [25600/60000 (43%)]\tLoss: 0.014871\n",
"Train Epoch: 5 [26240/60000 (44%)]\tLoss: 0.007171\n",
"Train Epoch: 5 [26880/60000 (45%)]\tLoss: 0.117038\n",
"Train Epoch: 5 [27520/60000 (46%)]\tLoss: 0.111855\n",
"Train Epoch: 5 [28160/60000 (47%)]\tLoss: 0.018824\n",
"Train Epoch: 5 [28800/60000 (48%)]\tLoss: 0.012503\n",
"Train Epoch: 5 [29440/60000 (49%)]\tLoss: 0.056160\n",
"Train Epoch: 5 [30080/60000 (50%)]\tLoss: 0.043957\n",
"Train Epoch: 5 [30720/60000 (51%)]\tLoss: 0.001754\n",
"Train Epoch: 5 [31360/60000 (52%)]\tLoss: 0.091498\n",
"Train Epoch: 5 [32000/60000 (53%)]\tLoss: 0.018654\n",
"Train Epoch: 5 [32640/60000 (54%)]\tLoss: 0.023146\n",
"Train Epoch: 5 [33280/60000 (55%)]\tLoss: 0.036612\n",
"Train Epoch: 5 [33920/60000 (57%)]\tLoss: 0.002565\n",
"Train Epoch: 5 [34560/60000 (58%)]\tLoss: 0.003447\n",
"Train Epoch: 5 [35200/60000 (59%)]\tLoss: 0.110711\n",
"Train Epoch: 5 [35840/60000 (60%)]\tLoss: 0.031876\n",
"Train Epoch: 5 [36480/60000 (61%)]\tLoss: 0.009661\n",
"Train Epoch: 5 [37120/60000 (62%)]\tLoss: 0.053748\n",
"Train Epoch: 5 [37760/60000 (63%)]\tLoss: 0.079816\n",
"Train Epoch: 5 [38400/60000 (64%)]\tLoss: 0.052890\n",
"Train Epoch: 5 [39040/60000 (65%)]\tLoss: 0.001838\n",
"Train Epoch: 5 [39680/60000 (66%)]\tLoss: 0.032443\n",
"Train Epoch: 5 [40320/60000 (67%)]\tLoss: 0.016371\n",
"Train Epoch: 5 [40960/60000 (68%)]\tLoss: 0.032993\n",
"Train Epoch: 5 [41600/60000 (69%)]\tLoss: 0.009191\n",
"Train Epoch: 5 [42240/60000 (70%)]\tLoss: 0.012432\n",
"Train Epoch: 5 [42880/60000 (71%)]\tLoss: 0.021050\n",
"Train Epoch: 5 [43520/60000 (72%)]\tLoss: 0.014490\n",
"Train Epoch: 5 [44160/60000 (74%)]\tLoss: 0.003937\n",
"Train Epoch: 5 [44800/60000 (75%)]\tLoss: 0.023810\n",
"Train Epoch: 5 [45440/60000 (76%)]\tLoss: 0.024212\n",
"Train Epoch: 5 [46080/60000 (77%)]\tLoss: 0.032333\n",
"Train Epoch: 5 [46720/60000 (78%)]\tLoss: 0.081611\n",
"Train Epoch: 5 [47360/60000 (79%)]\tLoss: 0.055151\n",
"Train Epoch: 5 [48000/60000 (80%)]\tLoss: 0.046237\n",
"Train Epoch: 5 [48640/60000 (81%)]\tLoss: 0.007069\n",
"Train Epoch: 5 [49280/60000 (82%)]\tLoss: 0.004486\n",
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Polecam również bibliotekę [PyTorch-Lightning](https://www.pytorchlightning.ai), dzięki któej kod PyTorcha staje się trochę bardziej \"uporządkowany\"."
]
},
{
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zadanie 10 (6 punktów)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zaimplementuj rozwiązanie wybranego problemu klasyfikacyjnego za pomocą prostej sieci neuronowej stworzonej przy użyciu biblioteki PyTorch."
]
}
],
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"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
},
"livereveal": {
"start_slideshow_at": "selected",
"theme": "amu"
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"nbformat": 4,
"nbformat_minor": 4
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