Tensorflow prototype Kernel and Advanced Visualization

This article mainly introduces "Tensorflow prototyping kernel and high-level visualization". In daily operation, I believe many people have doubts about Tensorflow prototyping kernel and high-level visualization. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "Tensorflow prototyping kernel and advanced visualization". Next, please follow the editor to study!

We all know that the efficiency of Python is very low. In order to improve the efficiency of computing, the kernel operations in Tensorflow are written entirely in C++. But writing a Tensorflow kernel in C++ is a very painful thing. So before we spend hours to implement the kernel, we should first prototype Python as soon as possible, although this is not efficient, but it will improve the efficiency of writing C++ version of Tensorflow kernel.

1. Build a prototype kernel with Python

I said earlier how to use tf.py_func (), which converts any piece of Python code into a Tensorflow operation. Now let's implement a simple ReLU nonlinear kernel as a Python operation in Tensorflow, as follows.

Of course, the efficiency of the above implementation is very inefficient and is only useful for prototypes. Because Python code cannot be parallelized and cannot be run on GPU. If we try a new idea, once we've validated it with Python, we can't wait to write it into a C++ kernel.

2. Tensorflow Advanced Visualization

In practice, we usually use Python ops for visualization on Tensorboard. For example, when we build an image classification model, we want to visualize the prediction of the model during training. Tensorflow allows you to visualize images with the tf.summary.image () function.

At this point, the study on "Tensorflow prototyping kernel and advanced visualization" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!