Fake Notes Detection

In this project we'll be using Keras with a TensorFlow backend to perform our deep learning operations for predicting Fake Notes.

This means we should get familiar with some Keras fundamentals and basics!

Imports

import numpy as np
import jovian

Dataset

We will use the Bank Authentication Data Set to start off with. This data set consists of various image features derived from images that had 400 x 400 pixels. You should note the data itself that we will be using ARE NOT ACTUAL IMAGES, they are features of images. In the next lecture we will cover grabbing and working with image data with Keras. This notebook focuses on learning the basics of building a neural network with Keras.

More info on the data set:

https://archive.ics.uci.edu/ml/datasets/banknote+authentication

Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images.

Attribute Information:

variance of Wavelet Transformed image (continuous)
skewness of Wavelet Transformed image (continuous)
curtosis of Wavelet Transformed image (continuous)
entropy of image (continuous)
class (integer)
Reading in the Data Set
We've already downloaded the dataset, its in the DATA folder. So let's open it up.

from numpy import genfromtxt
data = genfromtxt('../DATA/bank_note_data.txt', delimiter=',')

data

array([[  3.6216 ,   8.6661 ,  -2.8073 ,  -0.44699,   0.     ],
       [  4.5459 ,   8.1674 ,  -2.4586 ,  -1.4621 ,   0.     ],
       [  3.866  ,  -2.6383 ,   1.9242 ,   0.10645,   0.     ],
       ...,
       [ -3.7503 , -13.4586 ,  17.5932 ,  -2.7771 ,   1.     ],
       [ -3.5637 ,  -8.3827 ,  12.393  ,  -1.2823 ,   1.     ],
       [ -2.5419 ,  -0.65804,   2.6842 ,   1.1952 ,   1.     ]])