基于深度学习的面部表情识别系统python源码+部署说明+代码注释(毕设项目).zip

# 定义训练轮 import torch import torch.utils.data as data import torch.nn as nn import numpy as np from torch import optim from models import CNN_face from dataloader import rewrite_dataset def train(train_dataset, val_dataset, batch_size, epochs, learning_rate, wt_decay, print_cost=True, isPlot=True): # 加载数据集并分割batch train_loader = data.DataLoader(train_dataset, batch_size) # 构建模型 model = CNN_face.FaceCNN() # 损失函数和优化器 compute_loss = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=learning_rate, weight_decay=wt_decay) # 学习率衰减 # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.8) for epoch in range(epochs): loss = 0 model.train() for images, labels in train_loader: optimizer.zero_grad() outputs = model.forward(images) loss = compute_loss(outputs, labels) loss.backward() optimizer.step() # 打印损失值 if print_cost: print('epoch{}: train_loss:'.format(epoch + 1), loss.item()) # 评估模型准确率 if epoch % 10 == 9: model.eval() acc_train = validate(model, train_dataset, batch_size) acc_val = validate(model, val_dataset, batch_size) print('acc_train: %.1f %%' % (acc_train * 100)) print('acc_val: %.1f %%' % (acc_val * 100)) return model # 验证模型在验证集上的正确率 def validate(model, dataset, batch_size): val_loader = data.DataLoader(dataset, batch_size) result, total = 0.0, 0 for images, labels in val_loader: pred = model.forward(images) pred = np.argmax(pred.data.numpy(), axis=1) labels = labels.data.numpy() result += np.sum((pred == labels)) total += len(images) acc = result / total return acc def main(): train_dataset = rewrite_dataset.FaceDataset(root=r'D:\01 Desktop\JUST_YAN\DeepLearning\Facial-expression_Reg\datasets\cnn_train') val_dataset = rewrite_dataset.FaceDataset(root=r'D:\01 Desktop\JUST_YAN\DeepLearning\Facial-expression_Reg\datasets\cnn_val') model = train(train_dataset, val_dataset, batch_size=128, epochs=100, learning_rate=0.01, wt_decay=0, print_cost=True, isPlot=True) torch.save(model, 'model_net.pkl') # 保存模型 if __name__ == '__main__': main()