def test_compute_weights_with_zero_mask():
state_dim = 2
attended_dim = 3
match_dim = 4
attended_length = 5
batch_size = 6
attention = SequenceContentAttention(
state_names=["states"], state_dims=[state_dim],
attended_dim=attended_dim, match_dim=match_dim,
weights_init=IsotropicGaussian(0.5),
biases_init=Constant(0))
attention.initialize()
energies = tensor.as_tensor_variable(
numpy.random.rand(attended_length, batch_size))
mask = tensor.as_tensor_variable(
numpy.zeros((attended_length, batch_size)))
weights = attention.compute_weights(energies, mask).eval()
assert numpy.all(numpy.isfinite(weights))
def test_compute_weights_with_zero_mask():
state_dim = 2
attended_dim = 3
match_dim = 4
attended_length = 5
batch_size = 6
attention = SequenceContentAttention(
state_names=["states"], state_dims=[state_dim],
attended_dim=attended_dim, match_dim=match_dim,
weights_init=IsotropicGaussian(0.5),
biases_init=Constant(0))
attention.initialize()
energies = tensor.as_tensor_variable(
numpy.random.rand(attended_length, batch_size))
mask = tensor.as_tensor_variable(
numpy.zeros((attended_length, batch_size)))
weights = attention.compute_weights(energies, mask).eval()
assert numpy.all(numpy.isfinite(weights))
def test_stable_attention_weights():
state_dim = 2
attended_dim = 3
match_dim = 4
attended_length = 5
batch_size = 6
attention = SequenceContentAttention(state_names=["states"],
state_dims=[state_dim],
attended_dim=attended_dim,
match_dim=match_dim,
weights_init=IsotropicGaussian(0.5),
biases_init=Constant(0))
attention.initialize()
# Random high energies with mu=800, sigma=50
energies_val = (50. * numpy.random.randn(attended_length, batch_size) +
800).astype(theano.config.floatX)
energies = tensor.as_tensor_variable(energies_val)
mask = tensor.as_tensor_variable(numpy.ones((attended_length, batch_size)))
weights = attention.compute_weights(energies, mask).eval()
assert numpy.all(numpy.isfinite(weights))
def test_stable_attention_weights():
state_dim = 2
attended_dim = 3
match_dim = 4
attended_length = 5
batch_size = 6
attention = SequenceContentAttention(
state_names=["states"], state_dims=[state_dim],
attended_dim=attended_dim, match_dim=match_dim,
weights_init=IsotropicGaussian(0.5),
biases_init=Constant(0))
attention.initialize()
# Random high energies with mu=800, sigma=50
energies_val = (
50. * numpy.random.randn(attended_length, batch_size) + 800
).astype(theano.config.floatX)
energies = tensor.as_tensor_variable(energies_val)
mask = tensor.as_tensor_variable(
numpy.ones((attended_length, batch_size)))
weights = attention.compute_weights(energies, mask).eval()
assert numpy.all(numpy.isfinite(weights))
