In the Assignment 2 in week 2 , I completed many parts. But in one part there was an error in the code that they gave me . Please tell me what can I do for that?

What does the error say?

in optimize(w, b, X, Y, num_iterations, learning_rate, print_cost)

40

41 # Retrieve derivatives from grads

—> 42 dw = grads[“dw”]

43 db = grads[“db”]

44

IndexError: only integers, slices (`:`

), ellipsis (`...`

), numpy.newaxis (`None`

) and integer or boolean arrays are valid indices

grads which is one of the returns of propagate functions needs to be a dictionary! And the keys of the dictionary should be “dw” and “db”.

Also you need to use deepcopy for w and b if you are not doing so already!

it is not working

Please share your full error.

IndexError Traceback (most recent call

last) in ----> 1 params, grads,

costs = optimize(w, b, X, Y, num_iterations=100, learning_rate=0.009,

print_cost=False) 2 3 print ("w = " + str(params[“w”]))

4 print ("b = " + str(params[“b”])) 5 print ("dw = " +

str(grads[“dw”]))

in optimize(w, b, X, Y,

num_iterations, learning_rate, print_cost) 40 41 #

Retrieve derivatives from grads—> 42 dw = grads[“dw”] 43

db = grads[“db”] 44

IndexError: only integers, slices (`:`

), ellipsis (`...`

),

numpy.newaxis (`None`

) and integer or boolean arrays are valid indices

I think you have changed some of the pre-written code. The original code is:

```
# GRADED FUNCTION: optimize
def optimize(w, b, X, Y, num_iterations=100, learning_rate=0.009, print_cost=False):
"""
This function optimizes w and b by running a gradient descent algorithm
Arguments:
w -- weights, a numpy array of size (num_px * num_px * 3, 1)
b -- bias, a scalar
X -- data of shape (num_px * num_px * 3, number of examples)
Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)
num_iterations -- number of iterations of the optimization loop
learning_rate -- learning rate of the gradient descent update rule
print_cost -- True to print the loss every 100 steps
Returns:
params -- dictionary containing the weights w and bias b
grads -- dictionary containing the gradients of the weights and bias with respect to the cost function
costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.
Tips:
You basically need to write down two steps and iterate through them:
1) Calculate the cost and the gradient for the current parameters. Use propagate().
2) Update the parameters using gradient descent rule for w and b.
"""
w = copy.deepcopy(w)
b = copy.deepcopy(b)
costs = []
for i in range(num_iterations):
# (≈ 1 lines of code)
# Cost and gradient calculation
grads, cost = ...
# Retrieve derivatives from grads
dw = grads["dw"]
db = grads["db"]
# update rule (≈ 2 lines of code)
w = ...
b = ...
# Record the costs
if i % 100 == 0:
costs.append(cost)
# Print the cost every 100 training iterations
if print_cost:
print ("Cost after iteration %i: %f" %(i, cost))
params = {"w": w,
"b": b}
grads = {"dw": dw,
"db": db}
return params, grads, costs
```