def test_path_transition_layer():
init_rng()
sample_num = 5
class_num = 10
X = theano.tensor.nnet.softmax(np.random.random((sample_num, class_num)))
y = (9, 9, 9, 9, 9)
layer = PathTransitionLayer(class_num)
cost = layer.cost(X, y).eval()
y_pred = layer.predict(X).eval()
y_pred_cost = layer.cost(X, y_pred).eval()
print "optimized cost = ", cost
print "y_pred = ", y_pred
print "y_pred_cost", y_pred_cost
assert y_pred_cost < cost
y_score = 1000
logadd_score = 0.0
trans_mat = theano.tensor.nnet.softmax(layer.tag_trans_matrix).eval()
X = X.eval()
for path in itertools.product(range(class_num), repeat=sample_num):
score = trans_mat[0, path[0]] + X[0, path[0]]
for idx in range(1, sample_num):
score += trans_mat[path[idx - 1] + 1, path[idx]] + X[idx, path[idx]]
score = score # .eval()
logadd_score += math.exp(score)
if path == y:
y_score = score
logadd_score = math.log(logadd_score)
bruteforce_cost = logadd_score - y_score
print "bruteforce cost = {0} with logadd = {1} and selected_path_score = {2}".format(
bruteforce_cost, logadd_score, y_score
)
bruteforce_y_pred = np.argmax(X, axis=1) # because trans_mat is const matrix
print "brueforce y_pred = ", bruteforce_y_pred
assert math.fabs(bruteforce_cost - cost) < 1e-6
assert not np.any(y_pred - bruteforce_y_pred)
def test_path_transition_layer2():
init_rng()
sample_num = 5
class_num = 10
y1 = (9, 9, 9, 9, 9)
X1 = np.zeros((sample_num, class_num))
X1[range(sample_num), y1] = 1
y2 = (0, 1, 2, 3, 4)
X2 = np.zeros((sample_num, class_num))
X2[range(sample_num), y2] = 1
layer = PathTransitionLayer(class_num)
cost1 = layer.cost(X1, y1).eval()
cost2 = layer.cost(X2, y2).eval()
cost1_2 = layer.cost(X1, y2).eval()
cost2_1 = layer.cost(X2, y1).eval()
y_pred1 = layer.predict(X1).eval()
y_pred2 = layer.predict(X2).eval()
print "X1 = ", X1
print "X2 = ", X2
print "y1 = ", y1
print "y2 = ", y2
print "cost1 = ", cost1
print "cost2 = ", cost2
print "cost1_2 = ", cost1_2
print "cost2_1 = ", cost2_1
print "y_pred1 = ", y_pred1
print "y_pred2 = ", y_pred2
