This is autogradient implememted by myself just for fun!

To ask for grad, just use A.grad(B) , which can give you partial A partial B

Philosophy:Efficiency is the last thing I will consider.

core

core

example

• Linear Regression
• Logistic Regression
• Neural Network
• Softmax Regression

Other usages

>>> mat1 = Mat([[1, 2], [2, 3], [2, math.e]])
>>> format(op.log(mat1), '.3f')
['0.000', '0.693']
['0.693', '1.099']
['0.693', '1.000']

Log

Nov 5

• refactor project structure
• add unittest

Nov 6

• .T() -> .T
• add MatMulInternalError
• add NN for exclusive-or problem

Nov 7

• remove children of Mat and Node
• optimize zero_grad to simplify computation graph
• add node scalar operation, make Mat scalar operation depend on it
• overload > < of Node
• add softmax
• fix zero_grad error

ISSUE

Since Node and Mat both implement __roper__ method, we will face error when we do Mat + Node or Node + Mat

TODO

• singledispatch
• combine Node.zero_grad and Mat.zero_grad
• fix some awful feature
• add flag require_grad to simplify computation graph
• fix format
• random

Thought

• Node-based gradient -> Mat-based gradient

Mat-Based implement

• use numpy avoid overloading
• use require_grad
• calculate gradient in forward step for require_grad = 1