吴恩达Coursera, 机器学习专项课程, Machine Learning：Advanced Learning Algorithms第一周编程作业

吴恩达Coursera, 机器学习专项课程, Machine Learning：Advanced Learning Algorithms第一周所有jupyter notebook文件：

吴恩达,机器学习专项课程, Advanced Learning Algorithms第一周所有Python编程文件

本次作业

Exercise 1

# UNQ_C1
# GRADED CELL: Sequential model
 
model = Sequential(
    [               
        tf.keras.Input(shape=(400,)),    #specify input size
        ### START CODE HERE ### 
        Dense(25, activation='sigmoid', name = 'layer1'),
        Dense(15, activation='sigmoid', name = 'layer2'),
        Dense(1, activation='sigmoid', name = 'layer3')
      
        
        ### END CODE HERE ### 
    ], name = "my_model" 
)                            

Exercise 2

# UNQ_C2
# GRADED FUNCTION: my_dense
 
def my_dense(a_in, W, b, g):
    """
    Computes dense layer
    Args:
      a_in (ndarray (n, )) : Data, 1 example 
      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units
      b    (ndarray (j, )) : bias vector, j units  
      g    activation function (e.g. sigmoid, relu..)
    Returns
      a_out (ndarray (j,))  : j units
    """
    units = W.shape[1]
    a_out = np.zeros(units)
### START CODE HERE ### 
    for j in range(units):               
        w = W[:,j]                                    
        z = np.dot(w, a_in) + b[j]         
        a_out[j] = g(z)          
        
### END CODE HERE ### 
    return(a_out)

Exercise 3

# UNQ_C3
# GRADED FUNCTION: my_dense_v
 
def my_dense_v(A_in, W, b, g):
    """
    Computes dense layer
    Args:
      A_in (ndarray (m,n)) : Data, m examples, n features each
      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units
      b    (ndarray (1,j)) : bias vector, j units  
      g    activation function (e.g. sigmoid, relu..)
    Returns
      A_out (ndarray (m,j)) : m examples, j units
    """
### START CODE HERE ### 
#     units = W.shape[1]
#     a_out = np.zeros(units)
#     for j in range(units):               
#         w = W[:,j]                                    
#         z = np.dot(w, a_in) + b[j]         
#         a_out[j] = g(z)
    z = np.matmul(A_in,W) + b     
    A_out = g(z)    
### END CODE HERE ### 
    return(A_out)