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：本次作业

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)