98 lines
2.8 KiB
Python
98 lines
2.8 KiB
Python
import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from torch import nn
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from tqdm import tqdm
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from torchvision import datasets, transforms
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from torch.utils.data import DataLoader
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import ipdb
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class Model_3_2(nn.Module):
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def __init__(self, num_classes):
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super(Model_3_2, self).__init__()
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self.flatten = nn.Flatten()
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self.linear = nn.Linear(28 * 28, num_classes)
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def forward(self, x: torch.Tensor):
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x = self.flatten(x)
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x = self.linear(x)
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return x
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if __name__ == "__main__":
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learning_rate = 5e-2
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num_epochs = 10
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batch_size = 4096
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num_classes = 10
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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transform = transforms.Compose(
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[
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transforms.ToTensor(),
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transforms.Normalize((0.5,), (1.0,)),
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]
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)
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train_dataset = datasets.FashionMNIST(
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root="../dataset", train=True, transform=transform, download=True
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)
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test_dataset = datasets.FashionMNIST(
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root="../dataset", train=False, transform=transform, download=True
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)
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train_loader = DataLoader(
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dataset=train_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=14,
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pin_memory=True,
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)
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test_loader = DataLoader(
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dataset=test_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=14,
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pin_memory=True,
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)
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model = Model_3_2(num_classes).to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
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for epoch in range(num_epochs):
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total_epoch_loss = 0
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model.train()
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for index, (images, targets) in tqdm(
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enumerate(train_loader), total=len(train_loader)
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):
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optimizer.zero_grad()
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images = images.to(device)
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targets = targets.to(device)
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one_hot_targets = (
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torch.nn.functional.one_hot(targets, num_classes=num_classes)
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.to(device)
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.to(dtype=torch.float32)
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)
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outputs = model(images)
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loss = criterion(outputs, one_hot_targets)
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total_epoch_loss += loss
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loss.backward()
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optimizer.step()
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model.eval()
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total_acc = 0
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with torch.no_grad():
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for index, (image, targets) in tqdm(
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enumerate(test_loader), total=len(test_loader)
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):
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image = image.to(device)
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targets = targets.to(device)
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outputs = model(image)
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total_acc += (outputs.argmax(1) == targets).sum()
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print(
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f"Epoch {epoch + 1}/{num_epochs} Train, Loss: {total_epoch_loss}, Acc: {total_acc / len(test_dataset)}"
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)
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