myreli/hello-pca-mlp

Messing around with Databases Dimensionality Reduction and classification using Multi Layers Perceptron. (simple academic research)

Hello PCA & MLP

Messing around with Databases Dimensionality Reduction and classification using Multi Layers Perceptron.

(simple academic research)

set up and run

git clone https://github.com/myreli/hello-pca-mlp.git

cd hello-pca-mlp

conda create -n pcamlp

conda activate pcamlp

conda install python=3.6.5

pip install -r requirements.txt

python script.py

requirements

Python > 3.6.x

Anaconda

about this sample

• Using mnist dataset tutorial from tensorflow library

the MNIST database is a large database of handwritten digits, commonly used for ML.

• Using matplotlib for graphic plotation

to graphically understand the MNIST dataset

• Using PCA from sklearn.decomposition

to try it out dimensionality reduction

• Using Multi Layer Perceptron from mlxtend.classifier

to understand dimensionality reduction, each reducted db has been exposed agains 3 MLPs, A (10 hidden layers and 0.005 learning curve), B (30 hidden layers and 0.05 learning curve) and C (60 hidden layers and 0.5 learning curve).

output

reading MNIST dataset from tensorflow sample...
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

ploting random MNIST sample to prove that dataset loaded properly and understand data...

applying PCA 1 to dataset...

applying PCA 2 to dataset...

apply MLPs and display accuracy to each...

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---
[RES] Full: 

[RES] MLP 1: 93.10%
[RES] MLP 2: 87.84%
[RES] MLP 3: 10.09%

---
[RES] PCA 1: 

[RES] MLP 1: 93.25%
[RES] MLP 2: 99.68%
[RES] MLP 3: 92.83%

---
[RES] PCA 2: 

[RES] MLP 1: 93.01%
[RES] MLP 2: 19.13%
[RES] MLP 3: 9.82%

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Execution time:  81.14863263649603