字幕列表 影片播放 列印英文字幕 [MUSIC PLAYING] SPEAKER: In this video, you'll see how you can get TensorBoard working with Keras-based TensorFlow code. In this example, I'm using the Fashion MNIST dataset to do some basic computer vision, where I can train a Keras neural network to classify items of clothing. It's set to train for five epochs, and you can see the progress, including the loss and the accuracy, in the output window. We can see that it finishes training with an accuracy of about 86%, and we output some sample predictions. But how do we visualize this with TensorBoard? Let's start by importing the time library and TensorBoard itself. It can be found in tensorflow.pytho n.kera.callbacks. Next, after the model is defined, we want to instantiate TensorBoard. Note that we specify a log directory where stuff will get written. Finally, as the model is training in the model.fit function, we need to tell Keras to call back to TensorBoard. We simply do this by specifying the callback's parameter and tell it to use whatever we call the TensorBoard instance. In this case, it's all lowercase tensorboard. Now, in your terminal, you can execute the TensorBoard command, pointing at the log directory that you just specified. You'll see that it executes, and it gives me a TensorBoard at HTTP Machine Name colon 6,006. Now, if I retrain again, when it's done, I can take a look in TensorBoard. TensorBoard will launch, and I can start investigating things like the loss and the accuracy. I can also look at the graph that was built for the training. And that's just how to get it up and running. There's lots of great things that you can do further with TensorBoard, and you can see them at tensorflow.org/tensorboard. To learn more about TensorFlow, visit tensorflow.org. For more videos about TensorFlow, click the Subscribe button, and if you've any questions about this video, please leave them in the comments below. Thank you. [MUSIC PLAYING]
B1 中級 用Keras使用TensorBoard(TensorFlow一週小技巧)。 (Using TensorBoard with Keras (TensorFlow Tip of the Week)) 3 0 林宜悉 發佈於 2021 年 01 月 14 日 更多分享 分享 收藏 回報 影片單字