Sequential MLP implementation

[AndreiMoraru123]

Maybe not PR worthy, but I guess one can abstract the MLP implementation even more, making use of the layers instead of number of inputs and outputs yet again, since each individual layer already knows them.

As such, I wrote it as:

class MLP:

  def __init__(self, layers):
    self.layers = layers

  def __call__(self, x):
    for l in self.layers:
      x = l(x)
    return x

  def parameters(self):
      return [p for layer in self.layers for p in layer.parameters()]

by which you can define a network more intuitively, much like PyTorch's Sequential:

n = MLP([Layer(3, 6), Layer(6, 3), Layer(3, 1)])

To be even more rigorous, a dimension assertion can be added in the __init__:

class MLP:

  def __init__(self, layers):
    self.layers = layers
    for i in range(1, len(layers)):
      assert layers[i-1].nout == layers[i].nin

for which I would have to store the nin & nout for the layers in the as well:

class Layer:

  def __init__(self, nin, nout):
    self.nin = nin
    self.nout = nout
    self.neurons = [Neuron(nin) for _ in range(nout)]