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相关问题

1. Write a method with the following specifications: name: DisplayMenu arguments: none return value: none tasks: display the following menu choice on the screen Calculation Menu 1) Calculate Sum 2) Calculate Sum of Squares 3) Calculate Sum of Cubes 0) To Exit Enter the number that corresponds to your choice: You may beautify the output to your own likings. You don’t have to implement the functionalities of the various menu choices at this stage Call this method from your main.

Sure, here's the method that meets the specifications you provided:

public static void DisplayMenu() {
    System.out.println("Calculation Menu");
    System.out.println("1) Calculate Sum");
    System.out.println("2) Calculate Sum of Squares");
    System.out.println("3) Calculate Sum of Cubes");
    System.out.println("0) To Exit");
    System.out.print("Enter the number that corresponds to your choice: ");
}

You can call this method from your main method like this:

public static void main(String[] args) {
    DisplayMenu();
}

This will display the menu on the screen when you run the program.

DLR. A total number of 512 deep features are extracted (256 from PET images and 256 from CT images) as a byproduct of a multimodal neural network. The network is trained on CT and PET simultaneously [39], with two identical and parallel convolutional branches merged in a fully connected layer (see Fig. 4 for the details on the architecture). An internal transfer learning procedure is applied by first training the whole network on the T-stage dataset, then predicting LR by fine-tuning, i.e. retraining only the linear blocks (final blue box in Fig. 4). Fixed hyper-parameters are used to regulate the training process with Adam [43] optimizer (batch size: 32, epochs: 500, learning rate 10−3). Data augmentation procedures were used to improve the performance and reduce overfitting: i.e., minimal rotations, translations and Gaussian noise. The transformed images were resized to cubes of 64 × 64 × 64 to better fit the GPU memory size. 解释

这段文字描述了一个多模态神经网络，该网络从 PET（正电子发射断层扫描）和 CT（计算机断层扫描）图像中提取512个深度特征（256个来自PET图像，256个来自CT图像）。网络采用两个相同的卷积分支，然后将它们合并成一个全连接层。该网络首先在T-stage数据集上进行训练，然后通过微调（只重新训练线性块）来预测LR。训练过程中使用了固定的超参数来调节，使用Adam优化器，批大小为32，训练时代数为500，学习率为10^-3。数据增强过程被用来提高性能和减少过拟合：最小旋转、平移和高斯噪声等。转换后的图像被调整为64×64×64的立方体以更好地适应GPU内存大小。