Deep Learning Framework from scratch, only using numpy

Project

Gradient Descent: This article describes how to implement a gradient descent using the differential approach (2D example implementation), then, using the perturbation approach (3D example implementation).

FCN implementation: This article shows how to create a set of non-linearly separable data and how to implement a FCN from scratch using numpy: linear layers, activation functions, loss and training function.

Python Deep Learning Framework implementation: This article shows the implementation of a Deep Learning Framework with only numpy. It implements all the layers listed in the next section. It also explains how to implement some good features provided by a Deep Learning Framework such as: saving and loading a model to deploy it somewhere, getting its number of parameters, drawing learning curves, printing its description, getting its confusion matrix, etc.

Network deployment: This article explains how the Deep Learning Framework can help to create and train a CNN for hand signal recognition for UAV piloting. It also shows how to build a dataset for a particular task and how to deploy a trained model to perform the task.

C++ Deep Learning Framework implementation: This article explains how to create a C++ library that implements a simple Deep Learning Framework: Linear layer, MSE loss, ReLU and Softmax functions, a feature/label generator and a training loop. The main goal of this article is to show how to develop a project in C++ by explaining key concepts of the language.

Layer implementation:

Linear layer
Convolution layer
Flatten layer
Max pooling layer
Average pooling layer
Batch Normalization
Activation functions: Sigmoid, ReLU, LeakyReLU, Softmax
Loss functions (MSE: Mean Squared Error, Cross Entropy)

Example to build a neural network model:

To build a model, the Sequential module has to be used. The code below creates the following network: Convolution -> LeakyReLU -> Max Pooling -> Convolution -> LeakyReLU -> Flatten -> Batch Normalization -> Linear layer -> Linear layer -> Softmax -> Cross Entropy Loss

cnn_model = NN.Sequential([
	NN.Convolution(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size),
	NN.LeakyReLU(),
	NN.MaxPooling2D(2),
	NN.Convolution(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size),
	NN.LeakyReLU(),
	NN.Flatten(),
	NN.BatchNorm(),
	NN.Linear((out_first_conv**2)*out_channels, hidden_size),
	NN.LeakyReLU(),
	NN.BatchNorm(),
	NN.Linear(hidden_size, num_class),
	NN.Softmax()],
	NN.LossCrossEntropy())

Useful features of the framework:

One-hot encoding conversion
Train function (epoch and mini-batch)
Save and load a model to deploy it somewhere
Get the number of parameters of a created model
Get the confusion matrix
Draw learning curves
Print model's description

Example of use

The cnn-fcn_example.ipynb notebooks contain an implementation and training of a CNN model and a FCN model. They also show how to save and load a model with its weights to deploy it somewhere.