def test_sum():
def myfunc(a):
return a.sum()
adobj = AD(myfunc)
inp = np.array([n for n in range(5)])
truth = [[1, 1, 1, 1, 1]]
assert (adobj._reverse(inp) == truth).all()
def test_reverse_noinfl_in():
adobj = AD(lambda x, y: x**2 + x)
grad = adobj._reverse(1, 1)
truth = [[3, 0]]
assert (grad == truth).all()
def test_reverse_multidim_m():
adobj = AD(lambda x, y: [x + y, x**2])
grad = adobj._reverse(1, 1)
truth = [[1, 1], [2, 0]]
assert (grad == truth).all()
def test_reverse_multidim_n():
adobj = AD(lambda x, y: x**2 + 2 * y)
truth = [[4, 2]]
assert (adobj._reverse(2, 1) == truth).all()
return a.sum()
ad_object = AD(my_sum)
# showcase reverse better than forward when inputs increase
fwd_times = []
rev_times = []
ninputs = range(1, 10000, 100)
for n in range(1, 10000, 100):
inp = np.array([i for i in range(n)])
start_time = time.time()
ad_object._forward(inp)
runtime = time.time() - start_time
fwd_times.append(runtime)
start_time = time.time()
ad_object._reverse(inp)
runtime = time.time() - start_time
rev_times.append(runtime)
# plot results
plt.plot(ninputs, fwd_times, label='forward')
plt.plot(ninputs, rev_times, label='backward')
plt.legend()
plt.title('Comparing times under different AD modes')
plt.xlabel('Number of inputs')
plt.ylabel('Elapsed time in seconds')
plt.savefig('../img/fwd_rev_increasing_n.png')