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import numpy as np
from neural_network.forwardprop import fp
from neural_network.backprop import bp
def get_args():
"""
returns a dictionary containing
the arguments to be passed to
the main function
"""
return {
"epochs": int(input("Enter the number of epochs: ")),
"hidden_size": int(input("Enter the number of hidden nodes: ")),
"learning_rate": float(input("Enter the learning rate: ")),
"activation_func": input("Enter the activation function: "),
}
def init(X: np.array, y: np.array, hidden_size: int) -> dict:
"""
returns a dictionary containing randomly initialized
weights and biases to start off the neural_network
"""
return {
"W1": np.random.randn(X.shape[1], hidden_size),
"b1": np.zeros((1, hidden_size)),
"W2": np.random.randn(hidden_size, 1),
"b2": np.zeros((1, 1)),
}
def main(
X: np.array,
y: np.array,
epochs: int,
hidden_size: int,
learning_rate: float,
activation_func: str,
) -> None:
wb = init(X, y, hidden_size)
for e in range(epochs):
fp()
bp()
# update weights and biases
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