172 lines
4.9 KiB
Python
172 lines
4.9 KiB
Python
import numpy as np
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import torch
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from torch.utils.data import Dataset, 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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def my_one_hot(indices: torch.Tensor, num_classes: int):
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one_hot_tensor = torch.zeros(len(indices), num_classes).to(indices.device)
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one_hot_tensor.scatter_(1, indices.view(-1, 1), 1)
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return one_hot_tensor
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class My_CrossEntropyLoss:
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def __call__(self, predictions: torch.Tensor, targets: torch.Tensor):
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max_values = torch.max(predictions, dim=1, keepdim=True).values
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exp_values = torch.exp(predictions - max_values)
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softmax_output = exp_values / torch.sum(exp_values, dim=1, keepdim=True)
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log_probs = torch.log(softmax_output)
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nll_loss = -torch.sum(targets * log_probs, dim=1)
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average_loss = torch.mean(nll_loss)
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return average_loss
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class My_optimizer:
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def __init__(self, params: list[torch.Tensor], lr: float):
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self.params = list(params)
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self.lr = lr
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def step(self):
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for param in self.params:
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param.data = param.data - self.lr * param.grad.data
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def zero_grad(self):
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for param in self.params:
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if param.grad is not None:
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param.grad.data.zero_()
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class My_Linear:
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def __init__(self, input_feature: int, output_feature: int):
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self.weight = torch.randn(
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(output_feature, input_feature), requires_grad=True, dtype=torch.float32
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)
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self.bias = torch.randn(1, requires_grad=True, dtype=torch.float32)
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self.params = [self.weight, self.bias]
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def __call__(self, x: torch.Tensor):
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return self.forward(x)
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def forward(self, x: torch.Tensor):
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x = torch.matmul(x, self.weight.T) + self.bias
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return x
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def to(self, device: str):
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for param in self.params:
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param.data = param.data.to(device=device)
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return self
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def parameters(self):
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return self.params
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class My_Flatten:
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def __call__(self, x: torch.Tensor):
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return self.forward(x)
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def forward(self, x: torch.Tensor):
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x = x.view(x.shape[0], -1)
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return x
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class Model_3_1:
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def __init__(self, num_classes):
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self.flatten = My_Flatten()
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self.linear = My_Linear(28 * 28, num_classes)
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self.params = self.linear.params
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def __call__(self, x: torch.Tensor):
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return self.forward(x)
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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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def to(self, device: str):
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for param in self.params:
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param.data = param.data.to(device=device)
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return self
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def parameters(self):
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return self.params
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if __name__ == "__main__":
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learning_rate = 5e-1
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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_1(num_classes).to(device)
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criterion = My_CrossEntropyLoss()
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optimizer = My_optimizer(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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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).to(dtype=torch.long)
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one_hot_targets = (
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my_one_hot(targets, num_classes=num_classes)
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.to(device)
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.to(dtype=torch.long)
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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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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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