mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-04 16:34:50 +08:00
Jirka Borovec
724b787cd1
* MNIST digits * increase test acc * smaller parity * drone builds * increase GH action timeout * drone format * fix paths * drone cache * circle cache * fix test * lower nb epochs * circleCI * user orb * fix test * fix test * circle cache * circle cache * circle cache * comment caches * benchmark batch size * cache dataset * smaller dataset * smaller dataset * fix nb samples * batch size * fix test
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import torch
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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import pytorch_lightning as pl
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from tests.base.datasets import TestingMNIST
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# from test_models import assert_ok_test_acc, load_model, \
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# clear_save_dir, get_default_testtube_logger, get_default_hparams, init_save_dir, \
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# init_checkpoint_callback, reset_seed, set_random_master_port
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class CoolModel(pl.LightningModule):
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def __init(self):
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super().__init__()
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# not the best model...
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self.l1 = torch.nn.Linear(28 * 28, 10)
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def forward(self, x):
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return torch.relu(self.l1(x))
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def my_loss(self, y_hat, y):
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return F.cross_entropy(y_hat, y)
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def training_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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return {'training_loss': self.my_loss(y_hat, y)}
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def validation_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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return {'val_loss': self.my_loss(y_hat, y)}
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def validation_epoch_end(self, outputs):
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avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
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return avg_loss
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def configure_optimizers(self):
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return [torch.optim.Adam(self.parameters(), lr=0.02)]
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def train_dataloader(self):
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return DataLoader(TestingMNIST(train=True, num_samples=100), batch_size=16)
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def val_dataloader(self):
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return DataLoader(TestingMNIST(train=False, num_samples=50), batch_size=16)
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def test_dataloader(self):
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return DataLoader(TestingMNIST(train=False, num_samples=50), batch_size=16)
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