- Deep Learning with PyTorch
- Vishnu Subramanian
- 131字
- 2021-06-24 19:16:23
Matrix (2-D tensors)
Most of the structured data is represented in the form of tables or matrices. We will use a dataset called Boston House Prices, which is readily available in the Python scikit-learn machine learning library. The dataset is a numpy array consisting of 506 samples or rows and 13 features representing each sample. Torch provides a utility function called from_numpy(), which converts a numpy array into a torch tensor. The shape of the resulting tensor is 506 rows x 13 columns:
boston_tensor = torch.from_numpy(boston.data)
boston_tensor.size()
Output: torch.Size([506, 13])
boston_tensor[:2]
Output:
Columns 0 to 7
0.0063 18.0000 2.3100 0.0000 0.5380 6.5750 65.2000 4.0900
0.0273 0.0000 7.0700 0.0000 0.4690 6.4210 78.9000 4.9671
Columns 8 to 12
1.0000 296.0000 15.3000 396.9000 4.9800
2.0000 242.0000 17.8000 396.9000 9.1400
[torch.DoubleTensor of size 2x13]
推薦閱讀
- Arduino入門基礎教程
- 新型電腦主板關鍵電路維修圖冊
- 數字道路技術架構與建設指南
- 硬件產品經理成長手記(全彩)
- 計算機應用與維護基礎教程
- Unity 5.x Game Development Blueprints
- Large Scale Machine Learning with Python
- CC2530單片機技術與應用
- Rapid BeagleBoard Prototyping with MATLAB and Simulink
- Machine Learning with Go Quick Start Guide
- 數字媒體專業英語(第2版)
- 電腦橫機使用與維修
- MicroPython Cookbook
- 微服務架構實戰:基于Spring Boot、Spring Cloud、Docker
- Practical Artificial Intelligence and Blockchain