MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb

{"cells":[{"cell_type":"markdown","id":"128136fb","metadata":{"id":"128136fb"},"source":["The following additional libraries are needed to run this\n","notebook. Note that running on Colab is experimental, please report a Github\n","issue if you have any problem."]},{"cell_type":"code","execution_count":1,"id":"f30f7b75","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":34928,"status":"ok","timestamp":1700816435398,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"f30f7b75","outputId":"9ef355b8-a92f-41fb-b466-5b78ad5506c9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting d2l==1.0.3\n","  Downloading d2l-1.0.3-py3-none-any.whl.metadata (556 bytes)\n","Collecting jupyter==1.0.0 (from d2l==1.0.3)\n","  Downloading jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)\n","Collecting numpy==1.23.5 (from d2l==1.0.3)\n","  Downloading numpy-1.23.5-cp311-cp311-win_amd64.whl (14.6 MB)\n","     ---------------------------------------- 0.0/14.6 MB ? eta -:--:--\n","     ---------------------------------------- 0.1/14.6 MB 2.2 MB/s eta 0:00:07\n","      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pandas-2.1.3\n","  Attempting uninstall: matplotlib\n","    Found existing installation: matplotlib 3.8.2\n","    Uninstalling matplotlib-3.8.2:\n","      Successfully uninstalled matplotlib-3.8.2\n","Successfully installed d2l-1.0.3 ipywidgets-8.1.1 jupyter-1.0.0 jupyter-console-6.6.3 jupyterlab-widgets-3.0.9 matplotlib-3.7.2 numpy-1.23.5 pandas-2.0.3 pyparsing-3.0.9 qtconsole-5.5.1 qtpy-2.4.1 scipy-1.10.1 widgetsnbextension-4.0.9\n"]}],"source":["!pip install d2l==1.0.3\n"]},{"cell_type":"code","execution_count":2,"id":"e5a3fb64","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:26.260293Z","iopub.status.busy":"2023-08-18T19:29:26.259545Z","iopub.status.idle":"2023-08-18T19:29:29.089576Z","shell.execute_reply":"2023-08-18T19:29:29.088675Z"},"executionInfo":{"elapsed":8782,"status":"ok","timestamp":1700816449710,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"e5a3fb64","origin_pos":2,"tab":["pytorch"]},"outputs":[],"source":["import collections\n","import math\n","import os\n","import shutil\n","import pandas as pd\n","import torch\n","import torchvision\n","from torch import nn\n","from d2l import torch as d2l"]},{"cell_type":"code","execution_count":3,"id":"0d41dcd1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2023-08-18T19:29:29.095074Z","iopub.status.busy":"2023-08-18T19:29:29.094404Z","iopub.status.idle":"2023-08-18T19:29:29.393994Z","shell.execute_reply":"2023-08-18T19:29:29.393137Z"},"executionInfo":{"elapsed":1076,"status":"ok","timestamp":1700816462848,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"0d41dcd1","origin_pos":4,"outputId":"add815ed-abd5-464b-a554-4af1ea2f8162","tab":["pytorch"]},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading ../data\\kaggle_cifar10_tiny.zip from http://d2l-data.s3-accelerate.amazonaws.com/kaggle_cifar10_tiny.zip...\n"]}],"source":["#@save\n","d2l.DATA_HUB['cifar10_tiny'] = (d2l.DATA_URL + 'kaggle_cifar10_tiny.zip',\n","                                '2068874e4b9a9f0fb07ebe0ad2b29754449ccacd')\n","\n","# If you use the full dataset downloaded for the Kaggle competition, set\n","# `demo` to False\n","demo = True\n","\n","if demo:\n","    data_dir = d2l.download_extract('cifar10_tiny')\n","else:\n","    data_dir = '../data/cifar-10/'"]},{"cell_type":"markdown","id":"0f716217","metadata":{"id":"0f716217","origin_pos":5},"source":["### [**Organizing the Dataset**]\n","\n","We need to organize datasets to facilitate model training and testing.\n","Let's first read the labels from the csv file.\n","The following function returns a dictionary that maps\n","the non-extension part of the filename to its label.\n"]},{"cell_type":"code","execution_count":4,"id":"04bf8387","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2023-08-18T19:29:29.399003Z","iopub.status.busy":"2023-08-18T19:29:29.398718Z","iopub.status.idle":"2023-08-18T19:29:29.406335Z","shell.execute_reply":"2023-08-18T19:29:29.405552Z"},"executionInfo":{"elapsed":259,"status":"ok","timestamp":1700816468973,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"04bf8387","origin_pos":6,"outputId":"f4646eac-d7ba-4041-de04-de32723f1504","tab":["pytorch"]},"outputs":[{"name":"stdout","output_type":"stream","text":["# training examples: 1000\n","# classes: 10\n"]}],"source":["#@save\n","def read_csv_labels(fname):\n","    \"\"\"Read `fname` to return a filename to label dictionary.\"\"\"\n","    with open(fname, 'r') as f:\n","        # Skip the file header line (column name)\n","        lines = f.readlines()[1:]\n","    tokens = [l.rstrip().split(',') for l in lines]\n","    return dict(((name, label) for name, label in tokens))\n","\n","labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))\n","print('# training examples:', len(labels))\n","print('# classes:', len(set(labels.values())))"]},{"cell_type":"markdown","id":"abdf4d16","metadata":{"id":"abdf4d16","origin_pos":7},"source":["Next, we define the `reorg_train_valid` function to [**split the validation set out of the original training set.**]\n","The argument `valid_ratio` in this function is the ratio of the number of examples in the validation set to the number of examples in the original training set.\n","More concretely,\n","let $n$ be the number of images of the class with the least examples, and $r$ be the ratio.\n","The validation set will split out\n","$\\max(\\lfloor nr\\rfloor,1)$ images for each class.\n","Let's use `valid_ratio=0.1` as an example. Since the original training set has 50000 images,\n","there will be 45000 images used for training in the path `train_valid_test/train`,\n","while the other 5000 images will be split out\n","as validation set in the path `train_valid_test/valid`. After organizing the dataset, images of the same class will be placed under the same folder.\n"]},{"cell_type":"code","execution_count":5,"id":"0ae3357e","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.411145Z","iopub.status.busy":"2023-08-18T19:29:29.410869Z","iopub.status.idle":"2023-08-18T19:29:29.418258Z","shell.execute_reply":"2023-08-18T19:29:29.417439Z"},"executionInfo":{"elapsed":320,"status":"ok","timestamp":1700816472033,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"0ae3357e","origin_pos":8,"tab":["pytorch"]},"outputs":[],"source":["#@save\n","def copyfile(filename, target_dir):\n","    \"\"\"Copy a file into a target directory.\"\"\"\n","    os.makedirs(target_dir, exist_ok=True)\n","    shutil.copy(filename, target_dir)\n","\n","#@save\n","def reorg_train_valid(data_dir, labels, valid_ratio):\n","    \"\"\"Split the validation set out of the original training set.\"\"\"\n","    # The number of examples of the class that has the fewest examples in the\n","    # training dataset\n","    n = collections.Counter(labels.values()).most_common()[-1][1]\n","    # The number of examples per class for the validation set\n","    n_valid_per_label = max(1, math.floor(n * valid_ratio))\n","    label_count = {}\n","    for train_file in os.listdir(os.path.join(data_dir, 'train')):\n","        label = labels[train_file.split('.')[0]]\n","        fname = os.path.join(data_dir, 'train', train_file)\n","        copyfile(fname, os.path.join(data_dir, 'train_valid_test',\n","                                     'train_valid', label))\n","        if label not in label_count or label_count[label] < n_valid_per_label:\n","            copyfile(fname, os.path.join(data_dir, 'train_valid_test',\n","                                         'valid', label))\n","            label_count[label] = label_count.get(label, 0) + 1\n","        else:\n","            copyfile(fname, os.path.join(data_dir, 'train_valid_test',\n","                                         'train', label))\n","    return n_valid_per_label"]},{"cell_type":"markdown","id":"6d2cfa19","metadata":{"id":"6d2cfa19","origin_pos":9},"source":["The `reorg_test` function below [**organizes the testing set for data loading during prediction.**]\n"]},{"cell_type":"code","execution_count":6,"id":"890972a8","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.422565Z","iopub.status.busy":"2023-08-18T19:29:29.422289Z","iopub.status.idle":"2023-08-18T19:29:29.426856Z","shell.execute_reply":"2023-08-18T19:29:29.426083Z"},"executionInfo":{"elapsed":229,"status":"ok","timestamp":1700816474917,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"890972a8","origin_pos":10,"tab":["pytorch"]},"outputs":[],"source":["#@save\n","def reorg_test(data_dir):\n","    \"\"\"Organize the testing set for data loading during prediction.\"\"\"\n","    for test_file in os.listdir(os.path.join(data_dir, 'test')):\n","        copyfile(os.path.join(data_dir, 'test', test_file),\n","                 os.path.join(data_dir, 'train_valid_test', 'test',\n","                              'unknown'))"]},{"cell_type":"markdown","id":"0e790936","metadata":{"id":"0e790936","origin_pos":11},"source":["Finally, we use a function to [**invoke**]\n","the `read_csv_labels`, `reorg_train_valid`, and `reorg_test` (**functions defined above.**)\n"]},{"cell_type":"code","execution_count":7,"id":"00f50b41","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.431514Z","iopub.status.busy":"2023-08-18T19:29:29.430810Z","iopub.status.idle":"2023-08-18T19:29:29.434961Z","shell.execute_reply":"2023-08-18T19:29:29.434181Z"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1700816477295,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"00f50b41","origin_pos":12,"tab":["pytorch"]},"outputs":[],"source":["def reorg_cifar10_data(data_dir, valid_ratio):\n","    labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))\n","    reorg_train_valid(data_dir, labels, valid_ratio)\n","    reorg_test(data_dir)"]},{"cell_type":"markdown","id":"d0454395","metadata":{"id":"d0454395","origin_pos":13},"source":["Here we only set the batch size to 32 for the small-scale sample of the dataset.\n","When training and testing\n","the complete dataset of the Kaggle competition,\n","`batch_size` should be set to a larger integer, such as 128.\n","We split out 10% of the training examples as the validation set for tuning hyperparameters.\n"]},{"cell_type":"code","execution_count":8,"id":"1daf58c4","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.439643Z","iopub.status.busy":"2023-08-18T19:29:29.438882Z","iopub.status.idle":"2023-08-18T19:29:29.700309Z","shell.execute_reply":"2023-08-18T19:29:29.699321Z"},"executionInfo":{"elapsed":782,"status":"ok","timestamp":1700816479818,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"1daf58c4","origin_pos":14,"tab":["pytorch"]},"outputs":[],"source":["batch_size = 32 if demo else 128\n","valid_ratio = 0.1\n","reorg_cifar10_data(data_dir, valid_ratio)"]},{"cell_type":"markdown","id":"240c5c50","metadata":{"id":"240c5c50","origin_pos":15},"source":["## [**Image Augmentation**]\n","\n","We use image augmentation to address overfitting.\n","For example, images can be flipped horizontally at random during training.\n","We can also perform standardization for the three RGB channels of color images. Below lists some of these operations that you can tweak.\n"]},{"cell_type":"code","execution_count":9,"id":"70e97f85","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.705470Z","iopub.status.busy":"2023-08-18T19:29:29.704953Z","iopub.status.idle":"2023-08-18T19:29:29.710326Z","shell.execute_reply":"2023-08-18T19:29:29.709544Z"},"executionInfo":{"elapsed":294,"status":"ok","timestamp":1700816482937,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"70e97f85","origin_pos":17,"tab":["pytorch"]},"outputs":[],"source":["transform_train = torchvision.transforms.Compose([\n","    # Scale the image up to a square of 40 pixels in both height and width\n","    torchvision.transforms.Resize(40),\n","    # Randomly crop a square image of 40 pixels in both height and width to\n","    # produce a small square of 0.64 to 1 times the area of the original\n","    # image, and then scale it to a square of 32 pixels in both height and\n","    # width\n","    torchvision.transforms.RandomResizedCrop(32, scale=(0.64, 1.0),\n","                                                   ratio=(1.0, 1.0)),\n","    torchvision.transforms.RandomHorizontalFlip(),\n","    torchvision.transforms.ToTensor(),\n","    # Standardize each channel of the image\n","    torchvision.transforms.Normalize([0.4914, 0.4822, 0.4465],\n","                                     [0.2023, 0.1994, 0.2010])])"]},{"cell_type":"markdown","id":"d7593105","metadata":{"id":"d7593105","origin_pos":18},"source":["During testing,\n","we only perform standardization on images\n","so as to\n","remove randomness in the evaluation results.\n"]},{"cell_type":"code","execution_count":10,"id":"be0d5428","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.714890Z","iopub.status.busy":"2023-08-18T19:29:29.714292Z","iopub.status.idle":"2023-08-18T19:29:29.718602Z","shell.execute_reply":"2023-08-18T19:29:29.717807Z"},"executionInfo":{"elapsed":222,"status":"ok","timestamp":1700816490540,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"be0d5428","origin_pos":20,"tab":["pytorch"]},"outputs":[],"source":["transform_test = torchvision.transforms.Compose([\n","    torchvision.transforms.ToTensor(),\n","    torchvision.transforms.Normalize([0.4914, 0.4822, 0.4465],\n","                                     [0.2023, 0.1994, 0.2010])])"]},{"cell_type":"markdown","id":"27f918e0","metadata":{"id":"27f918e0","origin_pos":21},"source":["## Reading the Dataset\n","\n","Next, we [**read the organized dataset consisting of raw image files**]. Each example includes an image and a label.\n"]},{"cell_type":"code","execution_count":11,"id":"056ac33a","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.722917Z","iopub.status.busy":"2023-08-18T19:29:29.722506Z","iopub.status.idle":"2023-08-18T19:29:29.733889Z","shell.execute_reply":"2023-08-18T19:29:29.733119Z"},"executionInfo":{"elapsed":264,"status":"ok","timestamp":1700816494616,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"056ac33a","origin_pos":23,"tab":["pytorch"]},"outputs":[],"source":["train_ds, train_valid_ds = [torchvision.datasets.ImageFolder(\n","    os.path.join(data_dir, 'train_valid_test', folder),\n","    transform=transform_train) for folder in ['train', 'train_valid']]\n","\n","valid_ds, test_ds = [torchvision.datasets.ImageFolder(\n","    os.path.join(data_dir, 'train_valid_test', folder),\n","    transform=transform_test) for folder in ['valid', 'test']]"]},{"cell_type":"code","execution_count":12,"id":"06fa7207","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.738557Z","iopub.status.busy":"2023-08-18T19:29:29.737952Z","iopub.status.idle":"2023-08-18T19:29:29.743073Z","shell.execute_reply":"2023-08-18T19:29:29.742323Z"},"executionInfo":{"elapsed":230,"status":"ok","timestamp":1700816496763,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"06fa7207","origin_pos":26,"tab":["pytorch"]},"outputs":[],"source":["train_iter, train_valid_iter = [torch.utils.data.DataLoader(\n","    dataset, batch_size, shuffle=True, drop_last=True)\n","    for dataset in (train_ds, train_valid_ds)]\n","\n","valid_iter = torch.utils.data.DataLoader(valid_ds, batch_size, shuffle=False,\n","                                         drop_last=True)\n","\n","test_iter = torch.utils.data.DataLoader(test_ds, batch_size, shuffle=False,\n","                                        drop_last=False)"]},{"cell_type":"markdown","id":"9e84ffa3","metadata":{"id":"9e84ffa3","origin_pos":27},"source":["## Defining the [**Model**]\n"]},{"cell_type":"markdown","id":"aceea7ad","metadata":{"id":"aceea7ad","origin_pos":33,"tab":["pytorch"]},"source":["We define the ResNet-18 model described in\n",":numref:`sec_resnet`.\n"]},{"cell_type":"code","execution_count":13,"id":"d527425d","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.747678Z","iopub.status.busy":"2023-08-18T19:29:29.747059Z","iopub.status.idle":"2023-08-18T19:29:29.751129Z","shell.execute_reply":"2023-08-18T19:29:29.750380Z"},"executionInfo":{"elapsed":259,"status":"ok","timestamp":1700816498831,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"d527425d","origin_pos":35,"tab":["pytorch"]},"outputs":[],"source":["def get_net():\n","    num_classes = 10\n","    net = d2l.resnet18(num_classes, 3)\n","    return net\n","\n","loss = nn.CrossEntropyLoss(reduction=\"none\")"]},{"cell_type":"markdown","id":"b0d9de60","metadata":{"id":"b0d9de60","origin_pos":36},"source":["## Defining the [**Training Function**]\n","\n","We will select models and tune hyperparameters according to the model's performance on the validation set.\n","In the following, we define the model training function `train`.\n"]},{"cell_type":"code","execution_count":14,"id":"bde40789","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:29:29.755665Z","iopub.status.busy":"2023-08-18T19:29:29.755131Z","iopub.status.idle":"2023-08-18T19:29:29.764392Z","shell.execute_reply":"2023-08-18T19:29:29.763621Z"},"executionInfo":{"elapsed":226,"status":"ok","timestamp":1700816504059,"user":{"displayName":"","userId":""},"user_tz":-120},"id":"bde40789","origin_pos":38,"tab":["pytorch"]},"outputs":[],"source":["def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,\n","          lr_decay):\n","    trainer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9,\n","                              weight_decay=wd)\n","    scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)\n","    num_batches, timer = len(train_iter), d2l.Timer()\n","    legend = ['train loss', 'train acc']\n","    if valid_iter is not None:\n","        legend.append('valid acc')\n","    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\n","                            legend=legend)\n","    net = nn.DataParallel(net, device_ids=devices).to(devices[0])\n","    for epoch in range(num_epochs):\n","        net.train()\n","        metric = d2l.Accumulator(3)\n","        for i, (features, labels) in enumerate(train_iter):\n","            timer.start()\n","            l, acc = d2l.train_batch_ch13(net, features, labels,\n","                                          loss, trainer, devices)\n","            metric.add(l, acc, labels.shape[0])\n","            timer.stop()\n","            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:\n","                animator.add(epoch + (i + 1) / num_batches,\n","                             (metric[0] / metric[2], metric[1] / metric[2],\n","                              None))\n","        if valid_iter is not None:\n","            valid_acc = d2l.evaluate_accuracy_gpu(net, valid_iter)\n","            animator.add(epoch + 1, (None, None, valid_acc))\n","        scheduler.step()\n","    measures = (f'train loss {metric[0] / metric[2]:.3f}, '\n","                f'train acc {metric[1] / metric[2]:.3f}')\n","    if valid_iter is not None:\n","        measures += f', valid acc {valid_acc:.3f}'\n","    print(measures + f'\\n{metric[2] * num_epochs / timer.sum():.1f}'\n","          f' examples/sec on {str(devices)}')"]},{"cell_type":"code","execution_count":15,"id":"cd4a55c7","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":266},"execution":{"iopub.execute_input":"2023-08-18T19:29:29.768734Z","iopub.status.busy":"2023-08-18T19:29:29.768227Z","iopub.status.idle":"2023-08-18T19:30:37.496878Z","shell.execute_reply":"2023-08-18T19:30:37.495860Z"},"id":"cd4a55c7","origin_pos":41,"outputId":"cf0d58e2-2d66-4383-e638-d107d8abf639","tab":["pytorch"]},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\nicka\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n","  warnings.warn('Lazy modules are a new feature under heavy development '\n"]},{"ename":"IndexError","evalue":"list index out of range","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)","\u001b[1;32mc:\\Users\\nicka\\Desktop\\ΤΗΜΜΥ\\7ο Εξάμηνο\\Νευρωνικά Δίκτυα\\1h_ergasia\\MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb Cell 27\u001b[0m line \u001b[0;36m4\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m lr_period, lr_decay, net \u001b[39m=\u001b[39m \u001b[39m4\u001b[39m, \u001b[39m0.9\u001b[39m, get_net()\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m net(\u001b[39mnext\u001b[39m(\u001b[39miter\u001b[39m(train_iter))[\u001b[39m0\u001b[39m])\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m       lr_decay)\n","\u001b[1;32mc:\\Users\\nicka\\Desktop\\ΤΗΜΜΥ\\7ο Εξάμηνο\\Νευρωνικά Δίκτυα\\1h_ergasia\\MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb Cell 27\u001b[0m line \u001b[0;36m1\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     legend\u001b[39m.\u001b[39mappend(\u001b[39m'\u001b[39m\u001b[39mvalid acc\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m animator \u001b[39m=\u001b[39m d2l\u001b[39m.\u001b[39mAnimator(xlabel\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mepoch\u001b[39m\u001b[39m'\u001b[39m, xlim\u001b[39m=\u001b[39m[\u001b[39m1\u001b[39m, num_epochs],\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m                         legend\u001b[39m=\u001b[39mlegend)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m net \u001b[39m=\u001b[39m nn\u001b[39m.\u001b[39mDataParallel(net, device_ids\u001b[39m=\u001b[39mdevices)\u001b[39m.\u001b[39mto(devices[\u001b[39m0\u001b[39;49m])\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/nicka/Desktop/%CE%A4%CE%97%CE%9C%CE%9C%CE%A5/7%CE%BF%20%CE%95%CE%BE%CE%AC%CE%BC%CE%B7%CE%BD%CE%BF/%CE%9D%CE%B5%CF%85%CF%81%CF%89%CE%BD%CE%B9%CE%BA%CE%AC%20%CE%94%CE%AF%CE%BA%CF%84%CF%85%CE%B1/1h_ergasia/MPL_CNN_NIKOLAOS_EVNGELIDIS_10571_2.ipynb#X40sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_epochs):\n\u001b[0;32m     <a 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set.\n"]},{"cell_type":"code","execution_count":null,"id":"a66ef205","metadata":{"execution":{"iopub.execute_input":"2023-08-18T19:30:37.501313Z","iopub.status.busy":"2023-08-18T19:30:37.500748Z","iopub.status.idle":"2023-08-18T19:31:40.934103Z","shell.execute_reply":"2023-08-18T19:31:40.932837Z"},"id":"a66ef205","origin_pos":44,"outputId":"271c08f6-91a7-49df-8bab-31dcb03f2d83","tab":["pytorch"]},"outputs":[{"name":"stdout","output_type":"stream","text":["train loss 0.608, train acc 0.786\n","1040.8 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"]},{"data":{"image/svg+xml":["<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n","<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n","  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n","<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"238.965625pt\" height=\"183.35625pt\" viewBox=\"0 0 238.965625 183.35625\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n"," <metadata>\n","  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nested functions, αθξάνοντας την πολυπλοκότητα."]},{"cell_type":"markdown","id":"74fe0ee3","metadata":{"id":"74fe0ee3","origin_pos":50,"tab":["pytorch"]},"source":["[Discussions](https://discuss.d2l.ai/t/1479)\n"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"https://github.com/d2l-ai/d2l-pytorch-colab/blob/master/chapter_computer-vision/kaggle-cifar10.ipynb","timestamp":1700816660693}]},"kernelspec":{"display_name":"Python 3","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":"3.11.4"},"required_libs":[]},"nbformat":4,"nbformat_minor":5}