In forward propagation, we aim to calculate the activations and cost.
To calculate activations, we need to follow 2 steps for that:
Calculate the dot product of X and w.T, add then b.
Pass the above-obtained result to the sigmoid function.
To compute the cost, we:
Note:
np.log calculates the natural logarithm of all the elements in the array.
np.dot(a,b) calculates the dot product of the two vectors a and b.
np.sum(x) calculates the sum of elements in the input array.
Create a list x with elements 1,2,3.
x = << your code comes here >>
Calculate the natural logarithm of all the elements in x, using np.log function, store it in x_log.
x_log = << your code comes here >>(x)
Calculate the dot product of vectors a and b using np.dot() function, store the result in c.
a = np.array([[1,2],[3,4]])
b = np.array([[10,20],[30,40]])
c = << your code comes here >>(a,b)
print(c)
Use np.ones() to create a NumPy array containing 1 as all of its elements, and shape (4,4).
ones_array = << your code comes here >>(shape=(4,4))
Calculate the sum of the elements of ones_array using np.sum() function.
sum_of_ones_array = << your code comes here >>(ones_array)
The forward_prop function below, calculates the activations A and the cross-entropy cost cost. Copy-paste the following function.
def forward_prop(w, b, X, Y):
# calculate activations
z = np.dot(w.T, X) + b
A = sigmoid(z)
# calculate cost
m = X.shape[1]
cost = (-1/m) * np.sum(Y * np.log(A) + (1-Y) * (np.log(1-A)))
cost = np.squeeze(cost)
return A, cost
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