Creating the network

Suppose your dataset consists of inputs of size 3, and outputs of size 2 (just as in the example before). You can create a Neural Network compatible with this dataset by typing:

# Create a network with input size equal to 3
#  output layer with 2 neurons and a hidden
#  layer with 2 neurons and a bias unit

julia> net = FFNNet(3,2,2)
2 Layers Feedforward Neural Network:
  Input Size: 3
  Layer 1: 2 neurons + bias unit, tanh
  Layer 2: 2 neurons, tanh

By default, all layers have tanh as it's activation function. To change this, simply type:

# Change the last layer activation to `softmax`

julia> net.layers[end].activation = softmax

Notice that you can access the network's layers by indexing the vector net.layers. This is the vector of layers of this network and each of it's elements is of the type FFNNLayer.

A variable of type FFNNLayer has a field called activation, that specifies the activation function associated with that layer. To change the activation of a layer, we simply change that field.