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Копия блокнота _test 7.2 fashionmnist.ipynb_
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Копия блокнота _test 7.2 fashionmnist.ipynb_
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# FashionMNIST\n","\n"," ## Задание 7.2"]},{"cell_type":"markdown","metadata":{"id":"AygNFOvoWsxY"},"source":["<img src=\"https://emiliendupont.github.io/imgs/mnist-chicken/mnist-and-fashion-examples.png\">"]},{"cell_type":"markdown","metadata":{"id":"YNF5C2b_Wsxh"},"source":["На практике построим нейронную сеть для классификации картиной. В качестве входных данных используются картинки одежды (в градациях серого, то есть цветовых каналов не 3, а 1). По входной картинке нужно предсказать тип одежды."]},{"cell_type":"code","metadata":{"id":"2IYPMzDudZeN"},"source":["# !pip install torchvision\n","\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","import torch\n","import torchvision"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZEr2x6meSqNj"},"source":["Скачаем обучающую и контрольную выборки."]},{"cell_type":"code","metadata":{"id":"FCedv50ReIUZ","colab":{"base_uri":"https://localhost:8080/","height":386,"referenced_widgets":["d4e57fea90484360a8865ebf7752f013","dc74802e90124793b0d5d8d9e014f42c","3bb932ffeb784716a86796c0f5da0a59","de6d09b0ea2a46d8b12d1b1a950fe98e","f1aa5f7225ca4b1ba185a8561fe275b6","558d922044d34954b08ee6af0a1638b2","b380c7670c914c01b5670c56a7a272a5","741f38aa69d547f1aedd746b470f58a6","33ba0d30e1264387bfe08df73134abe1","4c9ecf913f8a44e09610a93e668e5764","070c027625af4d2283e6cfb0eb3da584","f1625f216f5c49b9b270bbd10cb829ec","e8513607dc144f86b8887a17ee4c4171","08c7399973984e00b9ea75b454442f25","fb917a68e3334105ac6870196237de38","ba857584aab14fb7be84e408b721f4e1","51acb21e3e8b436f99d5a108f8fbc6a3","601d4367a6a348dbbf3f66e6d73dc520","143b3eceb3864fc58f9bc8d5ea8045f8","abbbfdb6f58c4cd493e54a6b5fe8c034","6c9ace61c8004e07ad3b8538deda6b98","5fe0917e96b74e7581989a370efa57e0","ab02b755176d4e19986e99960f05bcc7","693b25daa9fd4bd883042052cf0fb288","e4fe92a10938407dbea8ebc98a54a2d3","853390719e08434aa7ff18b1dbc80b12","2605b0438aa343499d29c3bb48bb81ed","0b80ba98226b449d8c24bf848563cd46","aa1ad3efb3d94827be26174da8b4ea34","a98028da5a8a4050bb727c9d805397d9","9f6ac613c36948e781e8c92948afd319","49a22f7d29bd4f2db9a47a83bb8cbc98"]},"executionInfo":{"status":"ok","timestamp":1606736476166,"user_tz":-180,"elapsed":4939,"user":{"displayName":"Maksim Khlopotov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gjrfi2fUKbxZsEmFSi6eHxb-YT0aNnTzgBjBm83jg=s64","userId":"17573813484649782255"}},"outputId":"b831883b-46f2-4240-aca9-6aab8471192b"},"source":["trainset = torchvision.datasets.FashionMNIST(root='./data', train=True,\n"," download=True, transform=None)\n","trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n"," shuffle=True, num_workers=2)\n","\n","testset = torchvision.datasets.FashionMNIST(root='./data', train=False,\n"," download=True, transform=None)\n","testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n"," shuffle=False, num_workers=2)\n","\n","#classes = (<перечислите классы как строки>)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d4e57fea90484360a8865ebf7752f013","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"33ba0d30e1264387bfe08df73134abe1","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"51acb21e3e8b436f99d5a108f8fbc6a3","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e4fe92a10938407dbea8ebc98a54a2d3","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n","Processing...\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n"," return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"],"name":"stderr"},{"output_type":"stream","text":["Done!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CZTfJ0dBWszP"},"source":["Каждая картинка имеет класс от 0 до 9, расшифровка меток класса: \n","\n","|class_id|class_name|\n","|----|----|\n","|0| T-shirt/top|\n","|1| Trouser|\n","|2| Pullover|\n","|3| Dress|\n","|4| Coat|\n","|5| Sandal|\n","|6| Shirt|\n","|7| Sneaker|\n","|8| Bag|\n","|9| Ankle boot| "]},{"cell_type":"markdown","metadata":{"id":"RkDjjoj2Ws04"},"source":["<h2 style=\"text-align: center;\"><b>Задание</b></h2>"]},{"cell_type":"markdown","metadata":{"id":"b_DjMGZPWszt"},"source":["Посмотрите на датасет.\n","* Сколько картинок в train? В test?\n","* Равномерно ли распределено количество объектов разных классов в train датасете (это можно понять, поработав с полем `train_data` у переменной `trainset`)? в test?\n","* Выведите несколько картинок с помощью `pyplot`\n","\n","Используя материалы семинаров по многослойным и свёрточным сетям, напишите:\n","1. Многослойную нейронную сеть MLP (Multi-Layer Perceptron) классифицирующую этот датасет\n","2. Свёрточную сеть, классифицирующую этот датасет\n","3. Сравните их качество (по графикам лосса и по Accuracy Score)\n","\n","Если качество, которое получается у нейросети, выше 95%, то Вы решили задачу с точностью, которую можно использовать в жизни."]},{"cell_type":"code","metadata":{"id":"Ev5LShUSWs06"},"source":["<Ваш код здесь (может занимать много ячеек)> "],"execution_count":null,"outputs":[]}]}