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| import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from tqdm import tqdm
from dataset import EEGDataset,EEGDataset_Batch_normal
from net import IntegratedNet
from sklearn.metrics import classification_report
from matplotlib import pyplot as plt
# 定义归一化操作
def normalize(data):
mean = np.mean(data)
std = np.std(data)
return (data - mean) / std
transform = transforms.Compose([
transforms.Lambda(normalize), # 使用Lambda函数应用自定义归一化操作
transforms.ToTensor()
])
def train_identityformer_model(model, model_name, num_epochs=100, num_classes=3, batch_size=16, learning_rate=0.0001, w_wight=1025, chennal=33):
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
m = nn.Softmax(dim=1)
train_dataset = EEGDataset(csv_file='train_data.csv', transform=transform)
test_dataset = EEGDataset(csv_file='test_data.csv', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=True)
model.to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.RMSprop(model.parameters(), lr=learning_rate)
save_dir = 'loss'
os.makedirs(save_dir, exist_ok=True)
train_loss_arr = []
train_acc_arr = []
val_loss_arr = []
val_acc_arr = []
best_val_acc = 0.0
best_epoch = 0
for epoch in range(num_epochs):
train_loss_total = 0
train_acc_total = 0
val_loss_total = 0
val_acc_total = 0
model.train()
progress_bar = tqdm(enumerate(train_loader), total=len(train_loader))
for i, (train_x, train_y) in progress_bar:
train_x = train_x.to(device)
train_y = train_y.to(device)
train_x = train_x.unsqueeze(1)
train_x = train_x.view(batch_size, 1, chennal, w_wight)
train_y_pred = model(train_x)
train_loss = loss_fn(train_y_pred, train_y)
train_acc = (m(train_y_pred).max(dim=1)[1] == train_y).sum() / train_y.shape[0]
train_loss_total += train_loss.data.item()
train_acc_total += train_acc.data.item()
train_loss.backward()
optimizer.step()
optimizer.zero_grad()
progress_bar.set_description(f"Epoch {epoch+1}/{num_epochs}, Batch {i+1}/{len(train_loader)}, Train Loss: {train_loss.data.item():.4f}, Train Acc: {train_acc.data.item():.4f}")
train_loss_arr.append(train_loss_total / len(train_loader))
train_acc_arr.append(train_acc_total / len(train_loader))
model.eval()
for j, (val_x, val_y) in enumerate(test_loader):
val_x = val_x.to(device)
val_y = val_y.to(device)
val_x = val_x.unsqueeze(1)
val_x = val_x.view(batch_size, 1, chennal, w_wight)
val_y_pred = model(val_x)
val_loss = loss_fn(val_y_pred, val_y)
val_acc = (m(val_y_pred).max(dim=1)[1] == val_y).sum() / val_y.shape[0]
val_loss_total += val_loss.data.item()
val_acc_total += val_acc.data.item()
val_loss_arr.append(val_loss_total / len(test_loader))
val_acc_arr.append(val_acc_total / len(test_loader))
if val_acc_arr[-1] > best_val_acc:
best_val_acc = val_acc_arr[-1]
best_epoch = epoch
torch.save(model.state_dict(), f"{model_name}_best.pth")
print("epoch:{} val_loss:{} val_acc:{}".format(epoch, val_loss_arr[-1], val_acc_arr[-1]))
np.save(os.path.join(save_dir, 'train_loss_arr.npy'), np.array(train_loss_arr))
np.save(os.path.join(save_dir, 'train_acc_arr.npy'), np.array(train_acc_arr))
np.save(os.path.join(save_dir, 'val_loss_arr.npy'), np.array(val_loss_arr))
np.save(os.path.join(save_dir, 'val_acc_arr.npy'), np.array(val_acc_arr))
plt.subplot(1, 2, 1)
plt.title("loss")
plt.plot(train_loss_arr, "r", label="train")
plt.plot(val_loss_arr, "b", label="val")
plt.legend()
plt.subplot(1, 2, 2)
plt.title("acc")
plt.plot(train_acc_arr, "r", label="train")
plt.plot(val_acc_arr, "b", label="val")
plt.legend()
plt.savefig("loss_acc.png")
plt.show()
print(f"Best model at epoch {best_epoch+1}, val_acc={best_val_acc:.4f}")
print('Training completed!')
# 创建模型并训练
model = IntegratedNet(input_size=1,in_feature=157,num_classes=2) # 确保模型的输出层适用于三分类问题
train_identityformer_model(model, model_name='MLPFormer_betch_16_fft_opendata',chennal=19,w_wight=2500)
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