angadsinghsandhu

# training neural net
def train(self):

    # dynamically calculating layers and their respective z
    for i in range(len(self.input)):

        self.z0 = self.input[i].reshape([-1, 1])
        
        # forward step
        output = self.forwardprop()

angadsinghsandhu

# activation function
def sigmoid(self, x):
    return 1 / (1 + np.exp(-x))

angadsinghsandhu

# imports
import numpy as np

# nn class
class NeuralNetwork:

    # initializing variables
    def __init__(self, x, y, learning_rate=0.06, num_layers=2):
      
        # input array