吴恩达机器学习week2实验答案Practice Lab Linear Regression【C1_W2_Linear

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Practice Lab: Linear Regression

Exercise 1

Complete the compute_cost below to:

Iterate over the training examples, and for each example, compute:
- The prediction of the model for that example
  $f_{wb}(x^{(i)}) = wx^{(i)} + b$
- The cost for that example $cost^{(i)} = (f_{wb} - y^{(i)})^2$
Return the total cost over all examples
$J(\mathbf{w},b) = \frac{1}{2m} \sum\limits_{i = 0}^{m-1} cost^{(i)}$
- Here, $m$ is the number of training examples and $\sum$ is the summation operator

If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.文章来源地址https://www.toymoban.com/news/detail-709262.html

# UNQ_C1
# GRADED FUNCTION: compute_cost

def compute_cost(x, y, w, b): 
    # number of training examples
    m = x.shape[0] 
    # You need to return this variable correctly
    total_cost = 0
    
    ### START CODE HERE ###  
    for i in range(m):
        total_cost+=((x[i]*w+b)-y[i])**2
    total_cost=total_cost/(2*m)
    ### END CODE HERE ### 
    
    return total_cost

Exercise 2

Please complete the compute_gradient function to:

Iterate over the training examples, and for each example, compute:
- The prediction of the model for that example
  $f_{wb}(x^{(i)}) = wx^{(i)} + b$
- The gradient for the parameters $w, b$ from that example
  $\frac{\partial J(w,b)}{\partial b}^{(i)} = (f_{w,b}(x^{(i)}) - y^{(i)})$
  $\frac{\partial J(w,b)}{\partial w}^{(i)} = (f_{w,b}(x^{(i)}) -y^{(i)})x^{(i)}$
Return the total gradient update from all the examples
$\frac{\partial J(w,b)}{\partial b} = \frac{1}{m} \sum\limits_{i = 0}^{m-1} \frac{\partial J(w,b)}{\partial b}^{(i)}$

$\frac{\partial J(w,b)}{\partial w} = \frac{1}{m} \sum\limits_{i = 0}^{m-1} \frac{\partial J(w,b)}{\partial w}^{(i)}$
- Here, $m$ is the number of training examples and $\sum$ is the summation operator

If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.

# UNQ_C2
# GRADED FUNCTION: compute_gradient
def compute_gradient(x, y, w, b):   
    # Number of training examples
    m = x.shape[0]
    # You need to return the following variables correctly
    dj_dw = 0
    dj_db = 0
    
    ### START CODE HERE ### 
    for i in range(m):
        f_wb=w*x[i]+b
        dj_dw_i=(f_wb-y[i])*x[i]
        dj_db_i=f_wb-y[i]
        dj_dw+=dj_dw_i
        dj_db+=dj_db_i
    dj_dw=dj_dw/m
    dj_db=dj_db/m
    ### END CODE HERE ### 
        
    return dj_dw, dj_db