from utils.sigmoid import sigmoid
import numpy as np
from scipy.special import softmax

def forward_pass(w1, b1, w2, b2, data):
    # compute the forward pass of the MLP with sigmoid activations for the hidden layer and softmax for the output layer
    z1 = np.matmul(data, w1) + b1
    a1 = sigmoid(z1)
    z2 = np.matmul(a1, w2) + b2
    a2 = softmax(z2, axis=1)
    return a1, a2