How to plot cost function against iterations? I am new to coding in machine learning. I am trying to plot a graph for the gradient descent of a univariate function.
def linear_regression(x, y, m_current=0, b_current=0, epochs=1000, learning_rate=0.0001):
     N = float(len(y))
     for i in range(epochs):
          y_current = (m_current * x) + b_current
          cost = sum([data**2 for data in (y-y_current)]) / N
          m_gradient = -(2/N) * sum(x * (y - y_current))
          b_gradient = -(2/N) * sum(y - y_current)
          m_current = m_current - (learning_rate * m_gradient)
          b_current = b_current - (learning_rate * b_gradient)
     return m_current, b_current, cost

in order to plot the cost function with the iterations, i am using matlplotlib.pyplot. However, I am confused about how I should plot it against the iterations. Here 'i' is defined within the linear regression function, so I cannot use 'i' as an input for the plot function. How will I be able to plot the cost function with the iterations outside the linear regression function?
 A: import matplotlib.pyplot as plt   //import 
def linear_regression(x, y, m_current=0, b_current=0, epochs=1000, learning_rate=0.0001):
    cost_list = []   // list to store the cost in each iteration
    N = float(len(y))
    for i in range(epochs):
        y_current = (m_current * x) + b_current
        cost = sum([data**2 for data in (y-y_current)]) / N
        cost_list.append(cost)     // append the cost 
        m_gradient = -(2/N) * sum(x * (y - y_current))
        b_gradient = -(2/N) * sum(y - y_current)
        m_current = m_current - (learning_rate * m_gradient)
        b_current = b_current - (learning_rate * b_gradient)
    return m_current, b_current, cost, cost_list  //return cost_list 

m_current, b_current, cost, cost_list = linear_regression(.......)  // call the function

plt.plot(list(range(epochs)), cost_list, '-r') //plot the cost function. 

I have add comments where i have modified your code. What i have done is, 


*

*Created an empty list inside the linear_regression function to store the cost value in each iteration

*Then i append the cost to the list (cost_list) in each iteration

*Finally return the list(cost_list) containing all my cost values

*Call the linear_regression function and get the cost list

*Plot the cost list against the epochs

