I’m not getting expected cost value for the Gradient descent function in Exercise 05, the cost doesn’t seems to be changing much:

Call gradient_descent

iters: 10 cost: 9.105433

iters: 20 cost: 8.422988

iters: 30 cost: 8.758872

iters: 40 cost: 8.333231

iters: 50 cost: 9.127138

iters: 60 cost: 8.514464

iters: 70 cost: 8.935792

iters: 80 cost: 8.442537

iters: 90 cost: 8.672371

iters: 100 cost: 8.554414

iters: 110 cost: 8.016827

iters: 120 cost: 8.074106

iters: 130 cost: 8.337460

iters: 140 cost: 8.211384

iters: 150 cost: 7.975324

Assuming you passed the other unit tests and `forward_prop`

, `compute_cost`

, and `back_prop`

all produce expected results. That leaves the implementation of *#update weights and biases*

Maybe compare your code to the equations in the lecture/reading *Training a CBOW Model: Backpropagation and Gradient Descent*.

W_1 := W_1 - \alpha \frac{\partial J_{batch}}{\partial W_1}

W_2 := W_2 - \alpha \frac{\partial J_{batch}}{\partial W_2}

b_1 := b_1 - \alpha \frac{\partial J_{batch}}{\partial b_1}

b_2 := b_2 - \alpha \frac{\partial J_{batch}}{\partial b_2}

HINT: the partial derivative elements are computed in `back_prop()`