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October 18, 2021 17:10
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Catalyst + Comet
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| import comet_ml | |
| import os | |
| import torch | |
| from torch import nn, optim | |
| from torch.utils.data import DataLoader | |
| from catalyst import dl | |
| from catalyst.data import ToTensor | |
| from catalyst.contrib.datasets import MNIST | |
| from torch.utils.data import DataLoader | |
| model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10)) | |
| criterion = nn.CrossEntropyLoss() | |
| logger = dl.CometLogger() | |
| hparams = {"lr": 1.0e-3, "batch_size": 32} | |
| optimizer = optim.Adam(model.parameters(), lr=hparams["lr"]) | |
| loaders = { | |
| "train": DataLoader( | |
| MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), | |
| batch_size=hparams["batch_size"], | |
| ), | |
| "valid": DataLoader( | |
| MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), | |
| batch_size=hparams["batch_size"], | |
| ), | |
| } | |
| runner = dl.SupervisedRunner( | |
| input_key="features", output_key="logits", target_key="targets", loss_key="loss" | |
| ) | |
| # model training | |
| runner.train( | |
| model=model, | |
| criterion=criterion, | |
| optimizer=optimizer, | |
| loaders=loaders, | |
| hparams=hparams, | |
| num_epochs=1, | |
| callbacks=[ | |
| dl.AccuracyCallback( | |
| input_key="logits", target_key="targets", topk_args=(1, 3, 5) | |
| ), | |
| dl.PrecisionRecallF1SupportCallback( | |
| input_key="logits", target_key="targets", num_classes=10 | |
| ), | |
| ], | |
| logdir="./logs", | |
| valid_loader="valid", | |
| valid_metric="loss", | |
| minimize_valid_metric=True, | |
| verbose=True, | |
| load_best_on_end=True, | |
| loggers={"comet": logger}, | |
| ) |
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