代码拉取完成,页面将自动刷新
In the Jupyter notebook for this repository, I begin by calculating the bottleneck features for the CIFAR-10 dataset.
These features are then visualized with a Barnes-Hut implementation of t-SNE, which is the fastest t-SNE implementation to date.
For Python >= 3.5, this implementation can be installed by running
pip install git+https://github.com/alexisbcook/tsne.git
The bottleneck features are then fed to a shallow CNN for image classification. The model attains a test accuracy of 82.68%.
This repository is discussed in the blog post here.
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。