def test_sample_dropout_mask(self):
for _ in range(20):
height = 1000
width = 10000
dropout_prob = np.random.rand()
X = sampler.gen_uniform((height, width), np.float32)
dropout_mask = sample_dropout_mask(X, dropout_prob)
dropout_rate = 1. - dropout_mask.get().mean()
self.assertLess(np.abs(dropout_prob - dropout_rate), self.TOL)
self.assertTrue(np.all((X.get() != 0.) == dropout_mask.get()))
def test_sample_dropout_mask(self):
for _ in range(20):
height = 1000
width = 10000
dropout_prob = np.random.rand()
X = sampler.gen_uniform((height, width), np.float32)
dropout_mask = sample_dropout_mask(X, dropout_prob)
dropout_rate = 1. - dropout_mask.get().mean()
self.assertLess(np.abs(dropout_prob - dropout_rate), self.TOL)
self.assertTrue(np.all((X.get() != 0.) == dropout_mask.get()))
def test_sample_dropout_mask_columns(self):
for _ in range(20):
height = 10000
width = 10000
dropout_prob = np.random.rand()
X = sampler.gen_uniform((height, width), np.float32)
start = np.random.randint(0, width - 1000)
end = start + 1000
columns = (start, end)
dropout_mask = sample_dropout_mask(X, dropout_prob, columns)
dropout_rate = 1. - dropout_mask.get().mean()
self.assertEqual(dropout_mask.shape, (X.shape[0], end - start))
self.assertLess(np.abs(dropout_prob - dropout_rate), self.TOL)
self.assertTrue(
np.all((X.get()[:, start:end] != 0.) == dropout_mask.get()))
def test_sample_dropout_mask_columns(self):
for _ in range(20):
height = 10000
width = 10000
dropout_prob = np.random.rand()
X = sampler.gen_uniform((height, width), np.float32)
start = np.random.randint(0, width - 1000)
end = start + 1000
columns = (start, end)
dropout_mask = sample_dropout_mask(X, dropout_prob, columns)
dropout_rate = 1. - dropout_mask.get().mean()
self.assertEqual(dropout_mask.shape, (X.shape[0], end - start))
self.assertLess(np.abs(dropout_prob - dropout_rate),
self.TOL)
self.assertTrue(np.all((X.get()[:, start:end] != 0.)
== dropout_mask.get()))