def test_f(args, y, output):
correct = [0] * args.seq_length
total = [0] * args.seq_length
if args.label_type == 'one_hot':
y_decode = one_hot_decode(y)
output_decode = one_hot_decode(output)
elif args.label_type == 'five_hot':
y_decode = five_hot_decode(y)
output_decode = five_hot_decode(output)
for i in range(np.shape(y)[0]):
y_i = y_decode[i]
output_i = output_decode[i]
# print(y_i)
# print(output_i)
class_count = {}
for j in range(args.seq_length):
if y_i[j] not in class_count:
class_count[y_i[j]] = 0
class_count[y_i[j]] += 1
total[class_count[y_i[j]]] += 1
if y_i[j] == output_i[j]:
correct[class_count[y_i[j]]] += 1
# set_trace()
# return [float(correct[i]) / total[i] if total[i] > 0. else 0. for i in range(1, int(args.seq_length/args.n_classes))]
return [
float(correct[i]) / total[i] if total[i] > 0. else 0.
for i in range(1, 11)
], total[1:11]
def test_f(args, y, output):
correct = [0] * args.seq_length #correctly predicted
total = [0] * args.seq_length #total predicted
if args.label_type == 'one_hot':
y_decode = one_hot_decode(y) #getting the index of the arg max
output_decode = one_hot_decode(output) #getting the index of argmax
elif args.label_type == 'five_hot':
y_decode = five_hot_decode(y)
output_decode = five_hot_decode(output)
for i in range(
np.shape(y)[0]
): #this is iterating through the each predicted example in the batch
y_i = y_decode[i] #one y_i have 50 elementns
print("Printing the correct classes of the sequence", y_i)
output_i = output_decode[i]
# print(y_i)
# print(output_i)
class_count = {}
for j in range(args.seq_length): #now for each time step we iterate.
print(j)
if y_i[j] not in class_count: #get the first class in the starting time step. Check whether the sequence saw it before
class_count[y_i[j]] = 0 #start for the that class with sero
class_count[y_i[
j]] += 1 #add one for the class #each time when this sees a class it will up the counts
print("Printing the class counts", class_count)
print(
"printing the class cout of the current correct-class in the sequence",
class_count[y_i[j]])
total[class_count[y_i[j]]] += 1
print(total)
if y_i[j] == output_i[
j]: #if corerctly predicted the current time step one
correct[class_count[y_i[
j]]] += 1 #This is to basically find how many times networks see a class and how many times network correctly predicted a class.
print("Printing the correctness thing", correct)
#basically here we calculate end of each time step how many times I have seen this examples and how many times my network predicted correctly.
#here total is a [0,8,2,3,......49] of there 8 in second position is in the batch the network has seen same class for twice while and .
return [
float(correct[i]) / total[i] if total[i] > 0. else 0.
for i in range(1, 11)
] # accuracy is get by how many time steps in a back has seen sa