def compute_dz1_db1(h):
'''
Compute local gradient of the logits function z2 w.r.t. the biases b2.
Input:
h: the number of output activations in the first layer, an integer.
Output:
dz1_db1: the partial gradient of the logits z1 w.r.t. the biases b1, a float vector of shape h by 1.
Each element represents the partial gradient of the i-th logit z1[i] w.r.t. the i-th bias b1[i]: d_z1[i] / d_b1[i]
'''
#########################################
# INSERT YOUR CODE HERE
dz1_db1 = sr.compute_dz_db(h)
#########################################
return dz1_db1
def compute_dz2_db2(c):
'''
Compute local gradient of the logits function z2 w.r.t. the biases b2.
Input:
c: the number of classes, an integer.
Output:
dz2_db2: the partial gradient of the logits z2 w.r.t. the biases b2, a float vector of shape c by 1.
Each element represents the partial gradient of the i-th logit z2[i] w.r.t. the i-th bias b2[i]: d_z2[i] / d_b2[i]
'''
#########################################
# INSERT YOUR CODE HERE
dz2_db2 = sr.compute_dz_db(c)
#########################################
return dz2_db2
