How can I implement a basic feedforward neural network in Julia using Flux jl

Question

Can you explain How we can implement a basic feedforward neural network in Julia using Flux.jl?

Answer 1

To implement a basic feedforward neural network in Julia using Flux.jl, Here is the code snippet you can refer to:

In the above code, we are using the following:

• Define the Model: Use Chain to define layers. Dense represents fully connected layers with specified input, output dimensions, and activation functions.
• Define Loss: Use a loss function like Flux.crossentropy for classification tasks.
• Optimizer: Use an optimizer (e.g., Adam or SGD) for training.
• Training: Use Flux.train!() to update the model's parameters based on input data and target labels.

Hence, this is a simple setup for a feedforward neural network for tasks like image classification. You can extend it by adding more layers, dropout, etc.