Model checkpoint

Model checkpoint keras

my_callbacks = [
    tf.keras.callbacks.EarlyStopping(patience=2),
    tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.h5'),
    tf.keras.callbacks.TensorBoard(log_dir='./logs'),
]
model.fit(dataset, epochs=10, callbacks=my_callbacks)

Source: keras.io

Model checkpoint

#加载恢复
if RESUME:
    path_checkpoint = "./model_parameter/test/ckpt_best_50.pth"  # 断点路径
    checkpoint = torch.load(path_checkpoint)  # 加载断点

    model.load_state_dict(checkpoint['net'])  # 加载模型可学习参数

    optimizer.load_state_dict(checkpoint['optimizer'])  # 加载优化器参数
    start_epoch = checkpoint['epoch']  # 设置开始的epoch
    lr_schedule.load_state_dict(checkpoint['lr_schedule'])#加载lr_scheduler



#保存
for epoch in range(start_epoch+1,80):

    optimizer.zero_grad()

    optimizer.step()
    lr_schedule.step()


    if epoch %10 ==0:
        print('epoch:',epoch)
        print('learning rate:',optimizer.state_dict()['param_groups'][0]['lr'])
        checkpoint = {
            "net": model.state_dict(),
            'optimizer': optimizer.state_dict(),
            "epoch": epoch,
            'lr_schedule': lr_schedule.state_dict()
        }
        if not os.path.isdir("./model_parameter/test"):
            os.mkdir("./model_parameter/test")
        torch.save(checkpoint, './model_parameter/test/ckpt_best_%s.pth' % (str(epoch)))

Source: zhuanlan.zhihu.com

Model checkpoint

if RESUME:
    path_checkpoint = "./models/checkpoint/ckpt_best_1.pth"  # 断点路径
    checkpoint = torch.load(path_checkpoint)  # 加载断点

    model.load_state_dict(checkpoint['net'])  # 加载模型可学习参数

    optimizer.load_state_dict(checkpoint['optimizer'])  # 加载优化器参数
    start_epoch = checkpoint['epoch']  # 设置开始的epoch

Source: zhuanlan.zhihu.com

Model checkpoint

start_epoch = -1if RESUME:
    path_checkpoint = "./models/checkpoint/ckpt_best_1.pth"  # 断点路径
    checkpoint = torch.load(path_checkpoint)  # 加载断点

    model.load_state_dict(checkpoint['net'])  # 加载模型可学习参数

    optimizer.load_state_dict(checkpoint['optimizer'])  # 加载优化器参数
    start_epoch = checkpoint['epoch']  # 设置开始的epochfor epoch in range(start_epoch + 1 ,EPOCH):  # print('EPOCH:',epoch)  for step, (b_img,b_label) in enumerate(train_loader):    train_output = model(b_img)    loss = loss_func(train_output,b_label)    # losses.append(loss)    optimizer.zero_grad()    loss.backward()    optimizer.step()

Source: zhuanlan.zhihu.com

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