Example of MLP architecture

[pplonski]

Thank you for this package. I'm looking for some example on how to implement simple MLP (Multi Layer Perceptron) with this package. Any code snippets or tutorials are welcome.

Below is some code that I glue, but I have no idea on how to do backpropagation, I would like to have fit() method implemented.

Thank you!

from numpy_ml.neural_nets.losses import CrossEntropy, SquaredError
from numpy_ml.neural_nets.utils import minibatch
from numpy_ml.neural_nets.activations import ReLU, Sigmoid
from numpy_ml.neural_nets.layers import FullyConnected
from numpy_ml.neural_nets.optimizers.optimizers import SGD

optimizer = SGD()
loss = SquaredError()

class MLP:

    def __init__(self):
        self.nn = OrderedDict()
        self.nn["L1"] = FullyConnected(
            10, act_fn="ReLU", optimizer=optimizer
        )
        self.nn["L2"] = FullyConnected(
            1, act_fn="Sigmoid", optimizer=optimizer
        )

    def forward(self, X, retain_derived=True):
        Xs = {}
        out, rd = X, retain_derived
        for k, v in self.nn.items():
            Xs[k] = out
            out = v.forward(out, retain_derived=rd)
        return out, Xs
        
    def backward(self, grad, retain_grads=True):
        dXs = {}
        out, rg = grad, retain_grads
        for k, v in reversed(list(self.nn.items())):
            dXs[k] = out
            out = v.backward(out, retain_grads=rg)
        return out, dXs