Hi, having trouble with exercises 2 and 6.

**Here’s my challenge in 2 …**

# GRADED FUNCTION: layer_sizes

def layer_sizes(X, Y):

“”"

Arguments:

X – input dataset of shape (input size, number of examples)

Y – labels of shape (output size, number of examples)

```
Returns:
n_x -- the size of the input layer
n_y -- the size of the output layer
"""
### START CODE HERE ### (~ 2 lines of code)
# Size of input layer.
n_x = np.size(X[0][0])
# Size of output layer.
n_y = np.size(Y[0][0])
### END CODE HERE ###
return (n_x, n_y)
```

**Here’s my graded result:**

Wrong size of the input layer n_x for the test case, where array X has a shape (5, 100).

Expected: 5.

Got: 1.

Wrong size of the output layer n_y for the test case, where array Y has a shape (3, 100).

Expected: 3.

Got: 1.

2 Tests passed

2 Tests failed

**Here’s my challenge in 6 …**

**I’e input this code as required:**

### START CODE HERE ### (~ 1 line of code)

parameters_multi = nn_model(X_multi_norm, Y_multi_norm, num_iterations=100, print_cost=True)

### END CODE HERE

print("W = " + str(parameters_multi[“W”]))

print("b = " + str(parameters_multi[“b”]))

W_multi = parameters_multi[“W”]

b_multi = parameters_multi[“b”]

##
**But this is the error I;m getting:**

ValueError Traceback (most recent call last)

in

1 ### START CODE HERE ### (~ 1 line of code)

----> 2 parameters_multi = nn_model(X_multi_norm, Y_multi_norm, num_iterations=100, print_cost=True)

3 ### END CODE HERE ###

4

5 print("W = " + str(parameters_multi[“W”]))

in nn_model(X, Y, num_iterations, print_cost)

27 ### START CODE HERE ### (~ 2 lines of code)

28 # Forward propagation. Inputs: “X, parameters”. Outputs: “Y_hat”.

—> 29 Y_hat = forward_propagation(X, parameters)

30

31 # Cost function. Inputs: “Y_hat, Y”. Outputs: “cost”.

in forward_propagation(X, parameters)

18 # Implement Forward Propagation to calculate Z.

19 ### START CODE HERE ### (~ 2 lines of code)

—> 20 Z = W @ X + b

21 Y_hat = Z

22 ### END CODE HERE ###

ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 1)

Any help on how I can get around this problem??