mnist_3d

• Plot mnist data in 3d space.
• Visualization Method
  • LeNet
  • TruncatedSVD -> T-SNE

Example

• LeNet_mod(2D)

• LeNet_mod(3D)

• TruncatedSVD -> T-SNE(2D)

• TruncatedSVD -> T-SNE(3D)

Requirements

python version >= 3.6

pip install -r requirements.txt

Run

(1) Save mnist data as png

python save_mnist.py

(2) Plot mnist data

python plot.py

Arguments

Arguments	Default	Example	Description
sample_number	100	1000	Number of samples extracted from the whole mnist data.
phase	train	test	Phase of samples.
neural_net	False	N/A	Network to use or not to use
mid_dimension	100	500	Output dimension of TruncatedSVD or LeNet
out_dimension	2	3	Output dimension of t-SNE
count_sampled	False	N/A	Count the number of each handwriting nubmers.

python plot.py -sn 1000 -p test -sd 500 -od 3 -sc

What does it do?

LeNet

paper

Truncated SVD

t-SNE reduces the dimension of the input tensor quite well, but takes a long time when its dimension is high too much. Truncated SVD is recommended to use in that case for speedy processing.

t-SNE

t-SNE is a tool to visualize high-dimensional data. See here for more information.

Train LeNet by yourself

modifed: https://github.com/pytorch/examples/blob/master/mnist/main.py

python train_lenet.py