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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"]
r = {}
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
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),
)
db2 = np.sum(error * func_prime(y_hat), axis=0)
db1 = np.sum(np.dot(error * func_prime(y_hat), w2.T) * func_prime(node1), axis=0)
# update weights & biases using gradient descent.
# this is -= and not += because if the gradient descent
# is positive, we want to go down.
w1 -= (lr * dw1)
w2 -= (lr * dw2)
b1 -= (lr * db1)
b2 -= (lr * db2)
r[e] = {
"W1": w1,
"W2": w2,
"b1": b1,
"b2": b2,
"dw1": dw1,
"dw2": dw2,
"db1": db1,
"db2": db2,
}
return r
def compute_node(X, w, b, func):
return func(np.dot(X, w) + b)
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