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train.py
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train.py
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# 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()