criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01) ''' 模型训练 ''' EPOCHS = 5 for epoch in range(EPOCHS): running_loss = 0 for i, data in enumerate(train_loader): inputs, label = data y_predict = model(inputs) loss = criterion(y_...
(self.model.parameters(), lr=args.lr, weight_decay=args.weight_decay) else: raise Exception("optimizer not implement") if args.lr_scheduler == 'step': steps = [int(step) for step in args.steps.split(',')] self.lr_scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, steps, ...
The internal validation dataset was used to tune the abovementioned hyperparameters. Training process For training, ECG-AIO uses the Adam optimizer27, with default parameters β1 = 0.9 and β2 = 0.999, along with momentum and weight decay regularization. Binary cross-entropy is employed...
The entire measurement process of P-wave parameters was conducted by the same physician throughout the study to ensure consistency. The definition, normal range values, measurement and calculation methods of P-wave parameters was detailed in Supplemental information. Catheter ablation surgical scheme ...
The purpose of this chapter is to look at the normal parameters that should be measured systematically. Later chapters discuss the meaning behind values that fall outside this 'normal' range.doi:10.1007/978-1-4471-4962-0_4Alan DaviesAlwyn Scott...
Orthogonal experiments are used to select hyper-parameters. In the evaluation model stage, we use ensemble learning based on a voting strategy to obtain classification performance. Materials and methods Problem definition This paper aims to realize the automatic classification of normal rhythm and 8 car...
7. Repeat this for all parameters you want to change. Note: If you change the minute first, you can only change the hour directly after that, not the date. If you also want to change the date, you need to step through the sequence twice. 8. Press the clock set button (repeatedly)...
generateSeries(params: rrParameters) The rrSeries can be used multiple times such as to generate multilead ECG signals. The attractors parameter specify the morphology of the exponential extrema of the ECG. For a normal ECG there are 5 attractors corresponding to PQRST. The parameters θ, a, b...
Luengo-Fernandez, Burns, Rayner, & Townsend). The traditional CVD diagnosis paradigm is based on individual patient’smedical historyand clinical examinations. These results are interpreted according to a set of the quantitative medical parameters to classify the patients based on the taxonomy of medic...
The first layer is the input layer, and the input parameters determine the number of neurons in this layer. The last layer is the output layer, and the number of neurons in this layer represents the number of output classes. The layers between the input and output layers are called the ...