Tesla p100 pcie 16gb

The t-test and chi-square test were performed on four demographic variables to confirm the difference between the eyes in the pathologic myopia group and the normal group. Statistical significance was evaluated at p < 0.05 using R studio (version 1.3). The accuracy, specificity, sensitivity, and area under the receiver operating characteristic curve (AUROC) were calculated to analyze the performance of the model. A test dataset was used to evaluate the model. Tensorflow 2.5.0 [37 ] was used to train and evaluate the deep learning models and Pytorch 1.9.0 [38 ] with TorchIO [39 (link)] was used for image enhancement, along with the OpenCV [40 ] package in Python (version 3.8) for image processing. The learning of this model was trained on a GPU Nvidia Tesla P100-PCIE-16GB.

To show the correlation between increased prediction probabilities and text inputs, the t distributed stochastic neighbor embedding (SNE) was implemented by reducing the 768 dimensions of the language model’s pool output to 2 into a plane [29 ,30 ]. Thus, we showed the clustering of word embeddings using assorted colors for different predicted probabilities and different icons for observed mortalities. We randomly resampled 10,000 and 5000 patients who underwent surgeries in the training and testing sets, respectively, to construct this visualization. The language-model-predicted probabilities and observed mortalities for randomly selected text inputs were calculated and listed.
The study was implemented using Python 3.9, Scikit-learn 0.24 [31 ], imbalanced-learn 0.8.0 [23 (link)], PyTorch 1.8 [32 ], and transformers 4.9 (Hugging Face) [24 (link)]. Our models were trained and validated on the NVIDIA Tesla P100-PCIE-16GB graphics processing unit (GPU). The statistical significances of AUROCs and AUPRCs were calculated using MedCalc software (Ostend, Belgium).