def test_in_top_k(self):
batch_size = 20
num_classes = 10
# Random prediction test case
predictions = np.random.random(
(batch_size, num_classes)).astype('float32')
targets = np.random.randint(num_classes,
size=batch_size,
dtype='int32')
predictions_th = KTH.variable(predictions, dtype='float32')
targets_th = KTH.variable(targets, dtype='int32')
predictions_tf = KTF.variable(predictions, dtype='float32')
targets_tf = KTF.variable(targets, dtype='int32')
for k in range(1, num_classes + 1):
res_th = KTH.eval(KTH.in_top_k(predictions_th, targets_th, k))
res_tf = KTF.eval(KTF.in_top_k(predictions_tf, targets_tf, k))
assert res_th.shape == res_tf.shape
assert_allclose(res_th, res_tf, atol=1e-05)
# Identical prediction test case:
# randomly set half of the predictions to an identical value
num_identical = num_classes // 2
for i in range(batch_size):
idx_identical = np.random.choice(num_classes,
size=num_identical,
replace=False)
predictions[i, idx_identical] = predictions[i, 0]
targets = np.zeros(batch_size, dtype='int32')
predictions_th = KTH.variable(predictions, dtype='float32')
targets_th = KTH.variable(targets, dtype='int32')
predictions_tf = KTF.variable(predictions, dtype='float32')
targets_tf = KTF.variable(targets, dtype='int32')
for k in range(1, num_classes + 1):
res_th = KTH.eval(KTH.in_top_k(predictions_th, targets_th, k))
res_tf = KTF.eval(KTF.in_top_k(predictions_tf, targets_tf, k))
assert res_th.shape == res_tf.shape
assert_allclose(res_th, res_tf, atol=1e-05)
def test_in_top_k(self):
batch_size = 20
num_classes = 10
# Random prediction test case
predictions = np.random.random((batch_size, num_classes)).astype('float32')
targets = np.random.randint(num_classes, size=batch_size, dtype='int32')
predictions_th = KTH.variable(predictions, dtype='float32')
targets_th = KTH.variable(targets, dtype='int32')
predictions_tf = KTF.variable(predictions, dtype='float32')
targets_tf = KTF.variable(targets, dtype='int32')
for k in range(1, num_classes + 1):
res_th = KTH.eval(KTH.in_top_k(predictions_th, targets_th, k))
res_tf = KTF.eval(KTF.in_top_k(predictions_tf, targets_tf, k))
assert res_th.shape == res_tf.shape
assert_allclose(res_th, res_tf, atol=1e-05)
# Identical prediction test case:
# randomly set half of the predictions to an identical value
num_identical = num_classes // 2
for i in range(batch_size):
idx_identical = np.random.choice(num_classes, size=num_identical, replace=False)
predictions[i, idx_identical] = predictions[i, 0]
targets = np.zeros(batch_size, dtype='int32')
predictions_th = KTH.variable(predictions, dtype='float32')
targets_th = KTH.variable(targets, dtype='int32')
predictions_tf = KTF.variable(predictions, dtype='float32')
targets_tf = KTF.variable(targets, dtype='int32')
for k in range(1, num_classes + 1):
res_th = KTH.eval(KTH.in_top_k(predictions_th, targets_th, k))
res_tf = KTF.eval(KTF.in_top_k(predictions_tf, targets_tf, k))
assert res_th.shape == res_tf.shape
assert_allclose(res_th, res_tf, atol=1e-05)