def test_softmax(t):
q = minitorch.softmax(t, 3)
x = q.sum(dim=3)
assert_close(x[0, 0, 0, 0], 1.0)
q = minitorch.softmax(t, 1)
x = q.sum(dim=1)
assert_close(x[0, 0, 0, 0], 1.0)
minitorch.grad_check(lambda a: minitorch.softmax(a, dim=2), t)
def test_log_softmax(t):
q = minitorch.softmax(t, 3)
q2 = minitorch.logsoftmax(t, 3).exp()
for i in q._tensor.indices():
assert_close(q[i], q2[i])
minitorch.grad_check(lambda a: minitorch.logsoftmax(a, dim=2), t)
def test_softmax(t):
t = minitorch.tensor_fromlist([
[
[
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00]]]])
q = minitorch.softmax(t, 2)
print('=====t')
print(t)
print('=====q')
print(q)
x = q.sum(dim=3)
print('=====x')
print(x)
assert_close(x[0, 0, 0, 0], 1.0)
q = minitorch.softmax(t, 1)
x = q.sum(dim=1)
assert_close(x[0, 0, 0, 0], 1.0)
minitorch.grad_check(lambda a: minitorch.softmax(a, dim=2), t)
def test_log_softmax(t):
q = minitorch.softmax(t, 2)
logmax_q = minitorch.logsoftmax(t, 2).exp()
assert_close(logmax_q[0, 0, 0], q[0, 0, 0])