Manoj Rao bio photo

Manoj Rao

Your Average Common Man

Email Twitter Github

MxNet crash course - Part 1

  • This is an attempt to follow the crash course listed here MxNet Crash Course
  • There are some gaps in the crash course page of the MXNet website while this is a tested/verified set of steps.
  • Jupyter notebooks make it convenient to share.
  • Ensure you have installed the latest version of mxnet (>=1.4.x) at the time of this post.
# !pip install -U mxnet
from mxnet import nd

  • Create a 2D array with 1,2,3 and 4,5,6
nd.array(((1,2,3), (5,6,7)))
[[1. 2. 3.]
 [5. 6. 7.]]
<NDArray 2x3 @cpu(0)>
  • create a 2x3 matrix with 1’s
x = nd.ones((2,3))
x
[[1. 1. 1.]
 [1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
  • Create arrays with random values in a range.
  • Ex: values between -1 and 1 in the shape of 2x3
y = nd.random_uniform(-1, 1, (2, 3))
y
[[0.09762704 0.18568921 0.43037868]
 [0.6885315  0.20552671 0.71589124]]
<NDArray 2x3 @cpu(0)>
  • you can also fill an array of a given shape with a give value such as 2.0
x = nd.full((2, 3), 2.0)
x
[[2. 2. 2.]
 [2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
  • As with numpy, the dimensions of each ND array are accessible by accessing the .shape attribute. We can also query its size, which is equal to the product of the components of the shape. In addition, .dtype tells the data type of the stored values.
(x.shape, x.size, x.dtype)
((2, 3), 6, numpy.float32)

Operations

x * y
[[0.19525409 0.37137842 0.86075735]
 [1.377063   0.41105342 1.4317825 ]]
<NDArray 2x3 @cpu(0)>
y.exp()
[[1.1025515 1.204048  1.5378398]
 [1.9907899 1.2281718 2.0460093]]
<NDArray 2x3 @cpu(0)>
  • matrix’s transpose to compute a proper matrix-matrix product
nd.dot(x, y.T)
[[1.4273899 3.219899 ]
 [1.4273899 3.219899 ]]
<NDArray 2x2 @cpu(0)>

Indexing

y[1,2]
[0.71589124]
<NDArray 1 @cpu(0)>
  • Reading the second and third columns from y
y[:, 1:3]
[[0.18568921 0.43037868]
 [0.20552671 0.71589124]]
<NDArray 2x2 @cpu(0)>
  • and setting them to a specific element
y[:, 1:3] = 2
y
[[0.09762704 2.         2.        ]
 [0.6885315  2.         2.        ]]
<NDArray 2x3 @cpu(0)>
  • Multi-dimensional slicing
y[1:2, 0:2] = 4
y
[[0.09762704 2.         2.        ]
 [4.         4.         2.        ]]
<NDArray 2x3 @cpu(0)>

Converting between MXNet NDArray and NumPy

a = x.asnumpy()
(type(a), a)
(numpy.ndarray, array([[2., 2., 2.],
        [2., 2., 2.]], dtype=float32))
nd.array(a)
[[2. 2. 2.]
 [2. 2. 2.]]
<NDArray 2x3 @cpu(0)>


My Podcast!

If you like topics such as this then please consider subscribing to my podcast. I talk to some of the stalwarts in tech and ask them what their favorite productivity hacks are:

Available on iTunes Podcast

Visit Void Star Podcast’s page on iTunes Podcast Portal. Please Click ‘Subscribe’, leave a comment.

Get it iTunes

Available on Google Play Music

Visit Void Star Podcast’s page on Google Play Music. Please Click ‘Subscribe’ and leave a comment.

Listen on Google Play Music
Available on Stitcher

Visit Void Star Podcast’s page on Sticher. Please Click ‘Subscribe’ and leave a comment.

Listen to Stitcher

Your app not listed here? Not an issue..

You should be able to search for ‘VoidStar Podcast’ on your favorite app. Most apps use one of the above sources for listing podcasts. This was tested on Podcast Addict (where you can also specify the search engine) and RatPoison on Android.