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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:

- Calculate the product of Y and np.log(A)
- Calculate the product of (1-Y) and np.log(1-A)
- Add the results of the above 2 steps.
- Divide the negative of the result obtained in the third step with m, the total samples of the trainset.

**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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