a-mhamdi / neural-network-from-scratch-in-julia

Without using any deep learning frameworks, we construct and train a neural network architecture in Julia from the ground up.

Neural Network From Scratch In Julia

Without using any deep learning frameworks, we construct and train a neural network architecture in Julia from the ground up.

Architecture's Design

The neural network (NN) is built from scratch and trained on some data. Here is a possible representation of the NN architecture:

model = [ # MLP
    Layer(num_features, num_neurons_1, relu; distribution='n'),
    Layer(num_neurons_1, num_neurons_2, relu; distribution='n'), 
    Layer(num_neurons_2, num_targets, softmax; distribution='n')
    ]

where num_features, num_targets and num_neurons_\d+ denote, respectively, the numbers of input features, output targets, and neurons in the hidden layers. The choice of distribution for the weights initialization is either a normal distribution (n) or uniform distribution (u). Both Xavier and He methods were implemented.

Some of the hyperparameters are configured as follows:

Settings(epochs, batch_size)

Model's Training

We can define a struct for the regularization as follows:

reg = Regularization(method, λ, r, dropout)

method can be symbol or string of one of the following: l1, l2, elasticnet, or none. The λ parameter is the regularization parameter. The r parameter determines the mix of penalties in case of elasticnet method. The dropout parameter is the dropout rate. loss and optimizer are accessed through:

Solver(loss, optimizer, learning_rate, reg)

loss can be :mae, :mse, :rmse, :binarycrossentropy or :crossentropy. :sgd is the default optimizer. The model is trained using the following method:

TrainNN(model, data_in, data_out, x_val, y_val; solver)

Under the hood, the TrainNN method calls the FeedForward and BackProp functions. The FeedForward method returns the pre-activations z and the post-activations a, bundled into data_cache. The method signature is:

data_cache = FeedForward(model, data_in; solver::Solver)

The BackProp method allows to return the loss and the gradients of the weights and biases: ∇W and ∇b, as follows:

loss, ∇W, ∇b = BackProp(model, data_cache, data_out; solver::Solver)

Detailed steps of the backpropagation algorithm are shown in the images below (source).

The code is written in Julia. The main.jl file contains the primary code to setup the simulation. The required modules are located in the src folder.

Simulation Outcomes

Julia version and status of used packages are shown below:

The figure hereafter displays the model's loss for both the training and test sets at the end of each epoch.

The following provides specifics about confusion matrix, accuracy, precision, recall and f1-score metrics.

Note: The code is not optimized for performance. It is written for educational purposes. There is always room for improvement.

TODO: Implement the following features:

• parallelization of backprop on the batch of data instead of using a for loop;
• optimizers: SGD+Momentum, SGD+Nesterov, Adam, RMSprop, Adagrad, Adadelta.