Example of MLP architecture
Author: pplonskiCreated Dec 15, 2023Updated Aug 16, 2024
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, dXsSource: ddbourgin/numpy-ml