Smart-squirrel

Convolutional neural network implementation for school project :

CNNs are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. The whole network still expresses a single differentiable score function: from the raw image pixels on one end to class scores at the other. And they still have a loss function (e.g. SVM/Softmax) on the last (fully-connected) layer and all the tips/tricks we developed for learning regular Neural Networks still apply.

Image classification from scratch

We propose to use an existing dataset of dogs and cats available on theKaggle platform: https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data

Used libairies :

• cv2 also calledOpenCV, is an image and video processing library available in Python and many other high level programming languages. It is used in reading and resizing Images.
• Numpy
• Pandas
• Matplotlib
• gc

Getting started

1. Start by downloading dataset available on kaggle and extract it like below :
   • data/train/
   • data/test/
2. Create a virtual environment using pipenv with pipenv install
3. Execute CNN_from_scratch.py with the command pipenv run python CNN_from_scratch.py

You should obtain the following results :