My implementations of cs231n Spring 2019 Assignments.
Original official version of assignments without answers could be find here.
Most of the implementation ideas have been described in comments, and more notes will be updated one after another.
- pytorch in windows
If you cannot install pytorch in Windows by pip directly, you can find how to install it in the website of pytorch.
- c extension in windows
In assignment2, you need to compile some C extensions yourself before using faster_layers.It's quite troublesom for Python 3.x in Windows. It requires VC 14.x for compiling C extensions, so you need install Visual Studio 2017 or later, which is pretty big.
Of course, you can try to find other people's compiled version online. My compiled file for py3.6 in win64 in here.
- packages version of assignment3
The packages in requirements.txt of assignment3 are too old for python 3.7. Packages inrequirements.txt of assignment2 work well for assignment3.
The behavior depends on the arguments in the following way.
- If both arguments are 2-D they are multiplied like conventional matrices.
- If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
- If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
- If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
matmul differs from dot in two important ways:
-
Multiplication by scalars is not allowed, use
*instead. -
Stacks of matrices are broadcast together as if the matrices were elements, respecting the signature
(n,k),(k,m)->(n,m):
>>> a = np.ones([9, 5, 7, 4])
>>> c = np.ones([9, 5, 4, 3])
>>> np.dot(a, c).shape
(9, 5, 7, 9, 5, 3)
>>> np.matmul(a, c).shape
(9, 5, 7, 3)
>>> # n is 7, k is 4, m is 3
The matmul function implements the semantics of the @ operator introduced in Python 3.5 following PEP465.