akarsh-saxena

import numpy as np

def forward_prop(X, params):

  """Performs forward propagation and calculates output value

    Arguments
    ---------
    X: array_like
      Data

akarsh-saxena

import numpy as np

def initialize_nn(X):

  """Initializes random weights and bias

  Arguments
  ---------
  X: array-like
    Train Dataset

akarsh-saxena

import numpy as np

def sigmoid(Z):
  
  """Applies sigmoid function to an array/value
    Arguments
    ---------
    Z: float/int/array_like
      Original Value
    Returns

akarsh-saxena

from scipy.special import xlogy
import numpy as np

def calculate_loss(cache, y):

  """Calculate the entropy loss

    Arguments
    ---------
    cache: dict

akarsh-saxena

import numpy as np

def backward_prop(X, y, cache):

  """Performs backward propagation and calculates dw and db

    Arguments
    ---------
    X: array_like
      Data

akarsh-saxena

import numpy as np

def update_weights(params, changes, learning_rate=0.01):

  """Updates weights of the layers

    Arguments
    ---------
    params: dict
      Dictionary containing 'w' and 'b'

akarsh-saxena

epochs = 1000
learning_rate = 5e-3

params = initialize_nn(X_train)

for i in range(epochs):
  cache = forward_prop(X_train, params)
  loss = calculate_loss(cache, y_train)
  updates = backward_prop(X_train, y_train, cache)
  params = update_weights(params, updates, learning_rate=learning_rate)