Neural Network (Week 2) : Python Basics

import numpy as np
def sigmoid(x):
    s = 1/(1+np.exp(-x))
    return s

def sigmoid_derivative(x):
    s = 1/(1+np.exp(-x))
    ds = s*(1-s)
    return ds

def image2vector(image):
    v = image.reshape(image.shape[0]*
  image.shape[1]*image.shape[2], 1)
    return v

def normalizeRows(x):
    x_norm = np.linalg.norm(
  x, ord=2, axis=1, keepdims=True)
    x = x/x_norm
    return x

def softmax(x):
    x_exp = np.exp(x)
    x_sum = np.sum(x_exp, axis=1,
keepdims=True)
    s = x_exp / x_sum
    return s

def L1(yhat, y):
    loss = np.sum(abs(y-yhat))
    return loss

def L2(yhat, y):
loss = np.dot((y-yhat),(y-yhat))
    return loss