{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"from PIL import Image\n\nfrom torchvision import datasets, transforms, models\n\nimport torch\nfrom torch import nn, optim\nfrom torch.optim import lr_scheduler\nimport torch.nn.functional as F","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_dir = \"../input/train/\"\ntest_dir = \"../input/test/\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"imgs = []\n\nfor f in os.listdir(train_dir):\n    ext = os.path.splitext(f)[1]\n    imgs.append(Image.open(os.path.join(train_dir,f)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"imgs[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class_df = pd.read_csv(\"../input/labels.csv\")\nclass_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class_to_idx = {breed: index for index, breed in enumerate(class_df.breed.unique())}\nclass_to_idx","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Define transforms for the training and validation sets\ntrain_transforms = transforms.Compose([transforms.RandomRotation(10),\n                                transforms.RandomResizedCrop(224),\n                                transforms.RandomHorizontalFlip(),\n                                transforms.ToTensor(),\n                                transforms.Normalize([0.485, 0.456, 0.406], \n                                                     [0.229, 0.224, 0.225])])\n\ntest_transforms = transforms.Compose([transforms.Resize(255),\n                                transforms.CenterCrop(224),\n                                transforms.ToTensor(),\n                                transforms.Normalize([0.485, 0.456, 0.406], \n                                                     [0.229, 0.224, 0.225])])\n\n# Load the datasets with ImageFolder\n# !! not working for folders already separated with the training/testing data...\ntrain_data = datasets.ImageFolder(\"../input/\", transform = train_transforms)\ntest_data = datasets.ImageFolder(\"../input/\", transform = test_transforms)\n\n# Using the image datasets and the trainforms, define the dataloaders\ntrainloader = torch.utils.data.DataLoader(train_data, batch_size = 64, shuffle = True)\ntestloader = torch.utils.data.DataLoader(test_data, batch_size = 64, shuffle = False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}