硬质与火炬
哎哎哎:# t0]https://www.geeksforgeeks.orgas-vs-pytorch/
Keras 和 PyTorch 是两个最强大的开源机器学习库。
Keras 是一个基于 python 的开源库，用于深度学习(用于神经网络)。它可以运行在 TensorFlow、微软 CNTK 或 antano 之上。理解和使用非常简单，适合快速实验。Keras 模型既可以在中央处理器上运行，也可以在图形处理器上运行。
PyTorch 是一个开源的机器学习库，由脸书的 AI 研究小组开发。它可以与 Python 和 C++集成。它之所以受欢迎，是因为它高效的内存使用和轻松调试神经网络的能力。
让我们看看 Keras 和 PyTorch 的区别。
| s。不 | 硬 | PyTorch |
|---|---|---|
| 1。 | Keras was released in March 2015. | and PyTorch was released in October 2016. |
| 2。 | Keras has a high-level API. | The air pollution index of PyTorch is lower. |
| 3。 | The speed of Karas is relatively slow. | The speed of PyTorch is higher than that of Keras, which is suitable for high performance. |
| 4。 | Keras is simple in structure, more readable and more convenient to use. | However, because of its complex architecture, PyTorch has very low readability. |
| 5。 | Keras' community support is weak. | While PyTorch has stronger community support. |
| 6。 | Keras is mostly used in small data sets because of its slow speed. | While PyTorch is the first choice for large data sets and high performance. |
| 7。 | It is difficult to debug in Keras due to the existence of calculation garbage. | Debugging in PyTorch is easier and faster. |
| 8。 | Keras provides static calculation diagram. | And PyTorch provides dynamic calculation chart. |
| 9。 | The backend of Keras includes TensorFlow, antano and Microsoft CNTK backend. | But PyTorch has no back-end implementation. |
