Rtx 2080 ti gpu

The total loss of our framework is then calculated by considering the calibrator, reconstructor(s), and discriminator as a whole, i.e., to minimize $$\mathcal{L}=\alpha ·{\mathcal{L}}_R+\beta ·{\mathcal{L}}_C+\gamma ·{\mathcal{L}}_D,$$ where $\alpha$ , $\beta$ , $\gamma$ are scalar weights for each component network.
We implement the proposed solution with PyTorch (version 1.3.1) and Sklearn (version 0.21.3). The downstream analysis has been carried out using Python (version 3.6.8), and R (version 3.6.3) for visualization. For details, we use ADAM [33 ] for training with default settings (i.e., the exponential decay rate of the first/second moment estimation). All the experiments are run on the same host with 16 GB memory and an Nvidia RTX 2080Ti GPU.

In the pretraining and clustering stages, our model is trained for 50k iterations with a learning rate of 1e-4 and another 1k iterations with a learning rate of 1e-5. The batch size of training is 1024 and Adam (Kingma and Ba, 2014 ) is used for optimization. All methods were tested on a computer equipped with a 2.1 GHz Intel Xeon E5 CPU (8 DIMMs; 32 GB Memory) and an NVIDIA RTX 2080Ti GPU. Deep learning methods were implemented with Pytorch (v1.7.1) (Paszke et al., 2019 ). The weight of clustering loss $\lambda$ was set to be 0.1, as suggested in (Guo et al., 2017 ). The source code and the trained model will be made available at https://github.com/SlicerDMRI/DFC.

CCGNet is constructed under the opensource ML framework of TensorFlow⁸² . CCGNet outputs two-dimensional vectors [a, b] which represent the predictive scores for negative and positive class, respectively. If b > a, the output is labeled as the positive sample, and vice versa. Supplementary Methods describes details regarding constructions of SVM, RF, DNN-des, DNN-FP, GCN, Graph-CNN, and enn-s2s. Bayesian optimization is used to search the optimal hyper-parameters for all the models (see Supplementary Methods). The representation of the samples is implemented by RDkit, OpenBabel, and CCDC Python Application Programming Interface. We train the models on Nvidia RTX 2080ti GPU.

In Section 2.2, a tooth image database is collected consisting of 6028 teeth, which will be divided into training, validation and testing sets. We use the training set to train all the models, save the model according to the performance on the validation set, and test the models on the testing set with the saved model. Before training, we resize all the images of size $224\times 224$ , which is essential because different tooth images have different sizes due to the non-restricting rectangular boxes. Additionally, we perform image contrast enhancement by adjusting the intensity of each pixel, which we found to be useful in the experiments.
As for the hyper-parameters used, we set the dimension (C in Figure 2), mini-batch size, learning rate and the maximum training iterations to be 96, 32, ${10}^{-3}$ and 3000, respectively. As for the computing resources, we used an assembled server, which is configured with 2 × Intel(R) Xeon(R) Gold 6240R CPU and NVIDIA RTX 2080 Ti GPU (12 GB Ram).

The server used is configured with Intel(R) Xeon(R) Gold 6240R CPU and NVIDIA RTX 2080 Ti GPU (12GB ram), and the system is CentOS Linux release 7.8.2003. As for the hyperparameters, the network parameters are iterated for up to 10000 epochs using the Adam optimizer. The mini-batch size and the learning rate are set as 32 and 10⁻³, respectively. We select the model with the best performance on the validation set and then deployed to obtain the diagnosis results on the test set. Finally, the number of selected neighbors is set to be 3, and we will test the influence of varying numbers of neighbors in the following experiments.

The models were trained on a desktop workstation with the following specifications: 64 GB of memory; an Intel Xeon^® W-214 CPU; and an NVIDIA RTX 2080Ti GPU with 11 GB of video memory. The workstation operated on Windows 11 (64-bit), and the training was conducted using Python 3.9 with the deep learning platform CUDA 11.6 and the Pytorch framework.
The quality of the training model is significantly influenced by the difference in training parameters, and hyperparameters such as the learning rate, batch size, and number of iterations must be set manually during the training process. Among them, the learning rate is crucial in deep learning optimizers as it determines the speed at which weights are updated. If the learning rate is too high, the training results will exceed the optimal value, while if it is too low, the model will converge too slowly. The batch size depends on the size of the computer memory, with larger batches providing better model training results. The number of iterations determines the number of training rounds, with more iterations taking longer to complete. The iteration typically ends when the loss value has fully converged. After several parameter adjustments, the parameters in the model were set according to the values provided in Table 2.