# D5W1 A1 Assignment Exercise 6 rnn_backward need help

in the D5W1 A1 exercise for “Building_a_Recurrent_Neural_Network_Step_by_Step”

Exercise 6 rnn_backward need help I run into a strange problem and need help understanding.

when I run rnn_backward I got this error:

ValueError Traceback (most recent call last)

in

11 a_tmp, y_tmp, caches_tmp = rnn_forward(x_tmp, a0_tmp, parameters_tmp)

12 da_tmp = np.random.randn(5, 10, 4)

—> 13 gradients_tmp = rnn_backward(da_tmp, caches_tmp)

14

15 print(“gradients[“dx”][1][2] =”, gradients_tmp[“dx”][1][2])

in rnn_backward(da, caches)

39 for t in reversed(range(T_x)):

40 # Compute gradients at time step t. Choose wisely the “da_next” and the “cache” to use in the backward propagation step. (≈1 line)

—> 41 gradients = rnn_cell_backward(da_prevt, caches[t])

42 # Retrieve derivatives from gradients (≈ 1 line)

43 dxt, da_prevt, dWaxt, dWaat, dbat = gradients[“dxt”], gradients[“da_prev”], gradients[“dWax”], gradients[“dWaa”], gradients[“dba”]

in rnn_cell_backward(da_next, cache)

30 ### START CODE HERE ###

31 # compute the gradient of dtanh term using a_next and da_next (≈1 line)

—> 32 dtanh = da_next * (1 - np.tanh(np.dot(Wax, xt) + np.dot(Waa, a_prev) + ba)**2)

33

34 # compute the gradient of the loss with respect to Wax (≈2 lines)

ValueError: operands could not be broadcast together with shapes (10,4) (5,10)

However a step before when I worked on rnn_cell_backward I got the function working perfectly.

gradients[“dxt”][1][2] = -1.3872130506020925

gradients[“dxt”].shape = (3, 10)

gradients[“da_prev”][2][3] = -0.15239949377395495

gradients[“da_prev”].shape = (5, 10)

gradients[“dWax”][3][1] = 0.4107728249354584

gradients[“dWax”].shape = (5, 3)

gradients[“dWaa”][1][2] = 1.1503450668497135

gradients[“dWaa”].shape = (5, 5)

gradients[“dba”][4] = [0.20023491]

gradients[“dba”].shape = (5, 1)

So I am not sure why the aggregation stage ended up with mismatched shape (usually a result of the underlying operation say in rnn_cell_backward). need help in understanding what I may have done wrong here.

My lab ID is pahlbtkn