# Course 1: Week 2 Logistic_Regression_with_a_Neural_Network_mindset

why are we creating deep copy of w and b and saving them into themselves in optimize function?
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)
grads, cost =propagate(w, b, X, Y)

# update rule (≈ 2 lines of code)
# w =
# b = ...
w=w-learning_rate*dw
b=b-learning_rate*db

# Record the costs
if i % 100 == 0:
costs.append(cost)

# Print the cost every 100 training iterations
if print_cost and i % 100 == 0:
print ("Cost after iteration %i: %f" %(i, cost))

params = {"w": w,
"b": b}