def _gumbel_softmax(self, logits, tau=0.67, eps=1e-10):
u = layers.uniform_random_batch_size_like(
logits, shape=[-1, self.latent_type_size], min=0.0, max=1.0)
u.stop_gradient = True
gumbel = 0.0 - layers.log(eps - layers.log(u + eps))
y = logits + gumbel
return layers.softmax(y / tau)
def test_uniform_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.uniform_random_batch_size_like(input, [-1, 11])
self.assertIsNotNone(out)
print(str(program))
def gumbel_softmax(logits, tau=0.67, eps=1e-10):
"""Gumbel softmax."""
u = layers.uniform_random_batch_size_like(
logits, shape=[-1, logits.shape[1]], min=0.0, max=1.0)
u.stop_gradient = True
gumbel = 0.0 - layers.log(eps - layers.log(u + eps))
y = logits + gumbel
return layers.softmax(y / tau)
def dropout2d(input, prob, is_train=False):
if not is_train:
return input
channels = input.shape[1]
keep_prob = 1.0 - prob
random_tensor = keep_prob + layers.uniform_random_batch_size_like(
input, [-1, channels, 1, 1], min=0., max=1.)
binary_tensor = layers.floor(random_tensor)
output = input / keep_prob * binary_tensor
return output