Neural Network From Scratch

initialize_NN.py

# 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
        self.input = x

        # learning rate of gradient descent value
        self.alpha = learning_rate

        # number of layers of NN (not including input layer)
        self.num_layers = num_layers

        # creating array that hold the number of nodes in each layer
        self.num_nodes = np.random.randint(
            2, high=10, size=num_layers-1).tolist()
        
        self.num_nodes.insert(0, 1)
        self.num_nodes.append(1)

        # setting weights of all layers
        for i in range(len(self.num_nodes)-1):
            # dynamically creating weights
            cmd = "self.w{} = np.random.randn(self.num_nodes[i+1], self.num_nodes[i])".format(i+1)
            exec(cmd)

        # setting biases of all layers
        for i in range(len(self.num_nodes)-1):
            # dynamically creating biases
            cmd = "self.b{} = np.random.randn(self.num_nodes[i+1], 1)".format(i+1)
            exec(cmd)

        # output array and it's shape
        self.y = y
        self.y_hat = np.random.rand(y.shape[0], y.shape[1])

        self.loss = 0