def test_sequence_content_attention():
# Disclaimer: only check dimensions, not values
rng = numpy.random.RandomState([2014, 12, 2])
seq_len = 5
batch_size = 6
state_dim = 2
sequence_dim = 3
match_dim = 4
attention = SequenceContentAttention(state_names=["states"],
state_dims={"states": state_dim},
sequence_dim=sequence_dim,
match_dim=match_dim)
attention.allocate()
sequences = tensor.tensor3('sequences')
states = tensor.matrix('states')
mask = tensor.matrix('mask')
glimpses, weights = attention.take_look(sequences,
states=states,
mask=mask)
assert glimpses.ndim == 2
assert weights.ndim == 2
seq_values = numpy.zeros((seq_len, batch_size, sequence_dim), dtype=floatX)
states_values = numpy.zeros((batch_size, state_dim), dtype=floatX)
mask_values = numpy.zeros((seq_len, batch_size), dtype=floatX)
# randomly generate a sensible mask
for sed_idx in range(batch_size):
mask_values[:rng.randint(1, seq_len), sed_idx] = 1
glimpses_values, weight_values = theano.function(
[sequences, states, mask],
[glimpses, weights])(seq_values, states_values, mask_values)
assert glimpses_values.shape == (batch_size, sequence_dim)
assert weight_values.shape == (batch_size, seq_len)
assert numpy.all(weight_values >= 0)
assert numpy.all(weight_values <= 1)
assert numpy.all(weight_values.sum(axis=1) == 1)
assert numpy.all((weight_values.T == 0) == (mask_values == 0))
def test_sequence_content_attention():
# Disclaimer: only check dimensions, not values
rng = numpy.random.RandomState([2014, 12, 2])
seq_len = 5
batch_size = 6
state_dim = 2
sequence_dim = 3
match_dim = 4
attention = SequenceContentAttention(
state_names=["states"], state_dims={"states": state_dim},
sequence_dim=sequence_dim, match_dim=match_dim)
attention.allocate()
sequences = tensor.tensor3('sequences')
states = tensor.matrix('states')
mask = tensor.matrix('mask')
glimpses, weights = attention.take_look(sequences, states=states,
mask=mask)
assert glimpses.ndim == 2
assert weights.ndim == 2
seq_values = numpy.zeros((seq_len, batch_size, sequence_dim), dtype=floatX)
states_values = numpy.zeros((batch_size, state_dim), dtype=floatX)
mask_values = numpy.zeros((seq_len, batch_size), dtype=floatX)
# randomly generate a sensible mask
for sed_idx in range(batch_size):
mask_values[:rng.randint(1, seq_len), sed_idx] = 1
glimpses_values, weight_values = theano.function(
[sequences, states, mask], [glimpses, weights])(
seq_values, states_values, mask_values)
assert glimpses_values.shape == (batch_size, sequence_dim)
assert weight_values.shape == (batch_size, seq_len)
assert numpy.all(weight_values >= 0)
assert numpy.all(weight_values <= 1)
assert numpy.all(weight_values.sum(axis=1) == 1)
assert numpy.all((weight_values.T == 0) == (mask_values == 0))
