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import numpy as np
from neural_network.opts import activation
def bp(X_train: np.array, y_train: np.array, wb: dict, args: dict):
epochs = args["epochs"]
func = activation[args["activation_func"]]["main"]
func_prime = activation[args["activation_func"]]["prime"]
w1, w2 = wb["W1"], wb["W2"]
b1, b2 = wb["b1"], wb["b2"]
lr = args["learning_rate"]
for e in range(epochs):
# forward prop
node1 = compute_node(X_train, w1, b1, func)
y_hat = compute_node(node1, w2, b2, func)
error = y_hat - y_train
# backprop
# right now this is just the weights,
# we should also update the biases
dw2 = np.dot(
node1.T,
error * func_prime(y_hat),
)
dw1 = np.dot(
X_train.T,
np.dot(error * func_prime(y_hat), w2.T) * func_prime(node1),
)
# update weights & biases
w1 -= lr * dw1
w2 -= lr * dw2
def compute_node(X, w, b, func):
return func(np.dot(X, w) + b)
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