114 lines
3.4 KiB
Python
114 lines
3.4 KiB
Python
import time
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import numpy as np
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import torch
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from torch.nn.functional import *
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from torch.utils.data import Dataset, DataLoader
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from torch import nn
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from torchvision import datasets, transforms
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from tqdm import tqdm
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from utils import *
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import ipdb
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class Model_2_1(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(in_features=500, out_features=1)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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x = self.linear(x)
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x = self.sigmoid(x)
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return x
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class My_Regression_Dataset(Dataset):
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def __init__(self, train=True):
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data_size = 7000 if train else 3000
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np.random.seed(0)
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x = np.random.random((data_size, 500)) * 0.005
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noise = np.random.randn(data_size) * 1e-7
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y = 0.028 - 0.0056 * x.sum(axis=1) + noise
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y = y.reshape(-1, 1)
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self.data = [[x[i], y[i]] for i in range(x.shape[0])]
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def __len__(self):
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return len(self.data)
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def __getitem__(self, index):
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x, y = self.data[index]
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x = torch.FloatTensor(x)
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y = torch.FloatTensor(y)
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return x, y
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if __name__ == "__main__":
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learning_rate = 5
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num_epochs = 10
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batch_size = 512
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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train_regression_dataset = My_Regression_Dataset(train=True)
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test_regression_dataset = My_Regression_Dataset(train=False)
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train_dataloader = DataLoader(
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dataset=train_regression_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_dataloader = DataLoader(
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dataset=test_regression_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_2_1().to(device)
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criterion = nn.BCELoss()
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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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model.train()
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total_epoch_loss = 0
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start_time = time.time()
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for index, (x, targets) in tqdm(enumerate(train_dataloader), total=len(train_dataloader)):
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optimizer.zero_grad()
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x = x.to(device)
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targets = targets.to(device)
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y_pred = model(x)
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loss = criterion(y_pred, targets)
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total_epoch_loss += loss.item()
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loss.backward()
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optimizer.step()
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end_time = time.time()
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train_time = end_time - start_time
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model.eval()
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with torch.no_grad():
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total_epoch_acc = 0
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start_time = time.time()
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for index, (x, targets) in tqdm(enumerate(test_dataloader), total=len(test_dataloader)):
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x = x.to(device)
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targets = targets.to(device)
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y_pred = model(x)
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total_epoch_acc += (1 - torch.abs(y_pred - targets) / torch.abs(targets)).sum().item()
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end_time = time.time()
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test_time = end_time - start_time
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avg_epoch_acc = total_epoch_acc / len(test_regression_dataset)
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print(
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f"Epoch [{epoch + 1}/{num_epochs}],",
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f"Train Loss: {total_epoch_loss:.10f},",
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f"Used Time: {train_time * 1000:.3f}ms,",
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f"Test Acc: {avg_epoch_acc * 100:.3f}%,",
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f"Used Time: {test_time * 1000:.3f}ms",
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) |