Digit Recognising using Neural Net of an 28*28 pixel images by MNIST.
Everything was built from scratch upto the activation function.
- It is designed to show how far we're from the 'ideal' solution.
- Closer the
accuracy
value to1
, the better it is.
- It shows how many images are correctly identified
- Matrix is normalised so, values will be b/w [0, 1].
- Ideally principal diagonal values should be 1, other values should be 0
- We assume that
Anaconda
is already installed. - Anaconda comes with important libraries like
numpy
andmatplotlib
Update and install
PyPl's keyboard
library from terminal
py -version -m pip install keyboard
- download MNIST dataset from Kaggle.
- provide path to the dataset in main.py file
#Train Test split
X3d = idx2numpy.convert_from_file (
"E:\Path\to\trainingset images\\train-images-idx3-ubyte"
)
y = idx2numpy.convert_from_file (
"E:\Path\to\trainingset labels\\train-labels-idx1-ubyte"
)
X3d_test = idx2numpy.convert_from_file (
"E:\Path\to\testset images\\t10k-images-idx3-ubyte"
)
y_test = idx2numpy.convert_from_file (
"E:\Path\to\testset labels\\t10k-labels-idx1-ubyte"
)
- Thanks to the
google
andstackoverflow
. They were always there for my 101 questions ;) - Thank you for reading to the end :)