mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
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90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
import torch
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import torch.nn.functional as F
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import torchvision.datasets as datasets
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import torchvision.transforms as transforms
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from torch import nn, optim
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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import pytorch_lightning as pl
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import torchmetrics
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from torchmetrics import Metric
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import torchvision
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class NN(pl.LightningModule):
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def __init__(self, input_size, learning_rate, num_classes):
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super().__init__()
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self.lr = learning_rate
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self.fc1 = nn.Linear(input_size, 1_000_000)
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self.fc2 = nn.Linear(1_000_000, num_classes)
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self.loss_fn = nn.CrossEntropyLoss()
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self.accuracy = torchmetrics.Accuracy(
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task="multiclass", num_classes=num_classes
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)
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self.f1_score = torchmetrics.F1Score(task="multiclass", num_classes=num_classes)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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def training_step(self, batch, batch_idx):
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x, y = batch
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loss, scores, y = self._common_step(batch, batch_idx)
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self.log_dict(
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{
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"train_loss": loss,
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},
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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)
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if batch_idx % 100 == 0:
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x = x[:8]
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grid = torchvision.utils.make_grid(x.view(-1, 1, 28, 28))
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self.logger.experiment.add_image("mnist_images", grid, self.global_step)
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return {"loss": loss, "scores": scores, "y": y}
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def training_epoch_end(self, outputs):
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scores = torch.cat([x["scores"] for x in outputs])
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y = torch.cat([x["y"] for x in outputs])
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self.log_dict(
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{
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"train_acc": self.accuracy(scores, y),
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"train_f1": self.f1_score(scores, y),
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},
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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)
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def validation_step(self, batch, batch_idx):
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loss, scores, y = self._common_step(batch, batch_idx)
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self.log("val_loss", loss)
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return loss
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def test_step(self, batch, batch_idx):
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loss, scores, y = self._common_step(batch, batch_idx)
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self.log("test_loss", loss)
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return loss
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def _common_step(self, batch, batch_idx):
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x, y = batch
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x = x.reshape(x.size(0), -1)
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scores = self.forward(x)
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loss = self.loss_fn(scores, y)
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return loss, scores, y
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def predict_step(self, batch, batch_idx):
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x, y = batch
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x = x.reshape(x.size(0), -1)
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scores = self.forward(x)
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preds = torch.argmax(scores, dim=1)
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return preds
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def configure_optimizers(self):
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return optim.Adam(self.parameters(), lr=self.lr)
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