Deep-Learning framework from scratch
Home Page: https://ictxiangxin.github.io/paradox/
用python3和numpy实现一个简单的深度学习框架。
| Name | Version |
|---|---|
| Python | 3.5.0+ |
| Numpy | 1.13.0+ |
使用Paradox对进行梯度计算，并输出
x的梯度。
整个过程由图计算和自动求导完成。
import paradox as pd
k = pd.Constant([[2, 3], [1, 1]], name='k')
b = pd.Constant([[7], [3]], name='b')
x = pd.Variable([[0], [0]], name='x')
y = k @ x + b
print(pd.Engine(y).gradient(x).value)运行结果
[[ 3.]
[ 4.]]
| Author | ict |
| 405340537 | |
| [email protected] |
看paradox的代码感觉好轻量级，思路和TensorFlow很像但非常简便，特别适合学习了解框架的构建。
相信对我们这些初学者来说非常有用。
这里想请求作者是否可以分享些关于这些框架的思路或者相关的教程呢，直接看源代码感觉有点摸不着头脑，尤其是engin和symbol两个类。
使用了MNIST的CNN demo,速度有些缓慢。cpu占用率不高，或许可以采用用多核来解决，（https://docs.python.org/2/library/multiprocessing.html）。从paradox代码受益匪浅。期待更新。
好久没看写得这么规范的代码了，竟然IDE上几乎一个格式or拼写warning都没有，看得心情超好
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