def _make_stack(self, seq_length=4):
self.embedding_dim = embedding_dim = 3
self.vocab_size = vocab_size = 10
self.seq_length = seq_length
def compose_network(inp, inp_dim, outp_dim, vs, name="compose"):
# Just add the two embeddings!
W = T.concatenate([T.eye(outp_dim), T.eye(outp_dim)], axis=0)
return inp.dot(W)
X = T.imatrix("X")
transitions = T.imatrix("transitions")
apply_dropout = T.scalar("apply_dropout")
vs = VariableStore()
self.stack = HardStack(embedding_dim,
embedding_dim,
vocab_size,
seq_length,
compose_network,
IdentityLayer,
apply_dropout,
vs,
X=X,
transitions=transitions,
make_test_fn=True)
# Swap in our own dummy embeddings and weights.
embeddings = np.arange(vocab_size).reshape(
(vocab_size, 1)).repeat(embedding_dim, axis=1)
self.stack.embeddings.set_value(embeddings)
def _make_stack(self, seq_length=4):
self.embedding_dim = embedding_dim = 3
self.vocab_size = vocab_size = 10
self.seq_length = seq_length
def compose_network(inp, inp_dim, outp_dim, vs, name="compose"):
# Just add the two embeddings!
W = T.concatenate([T.eye(outp_dim), T.eye(outp_dim)], axis=0)
return inp.dot(W)
X = T.imatrix("X")
transitions = T.imatrix("transitions")
apply_dropout = T.scalar("apply_dropout")
vs = VariableStore()
self.stack = HardStack(
embedding_dim,
embedding_dim,
vocab_size,
seq_length,
compose_network,
IdentityLayer,
apply_dropout,
vs,
X=X,
transitions=transitions,
make_test_fn=True,
)
# Swap in our own dummy embeddings and weights.
embeddings = np.arange(vocab_size).reshape((vocab_size, 1)).repeat(embedding_dim, axis=1)
self.stack.embeddings.set_value(embeddings)
def _make_stack(self, seq_length=4):
self.embedding_dim = embedding_dim = 3
self.vocab_size = vocab_size = 10
self.seq_length = seq_length
def compose_network(inp, inp_dim, outp_dim, vs, name="compose"):
# Just add the two embeddings!
W = T.concatenate([T.eye(outp_dim), T.eye(outp_dim)], axis=0)
return inp.dot(W)
X = T.imatrix("X")
transitions = T.imatrix("transitions")
training_mode = T.scalar("training_mode")
ground_truth_transitions_visible = T.scalar(
"ground_truth_transitions_visible", dtype="int32")
vs = VariableStore()
# Swap in our own dummy embeddings and weights.
initial_embeddings = np.arange(vocab_size).reshape(
(vocab_size, 1)).repeat(embedding_dim, axis=1)
self.stack = HardStack(embedding_dim,
embedding_dim,
vocab_size,
seq_length,
compose_network,
IdentityLayer,
training_mode,
ground_truth_transitions_visible,
vs,
X=X,
transitions=transitions,
make_test_fn=True,
initial_embeddings=initial_embeddings,
use_input_batch_norm=False,
use_input_dropout=False)
def _make_stack(self, seq_length=4):
self.embedding_dim = embedding_dim = 3
self.vocab_size = vocab_size = 10
self.seq_length = seq_length
def compose_network(inp, inp_dim, outp_dim, vs, name="compose"):
# Just add the two embeddings!
W = T.concatenate([T.eye(outp_dim), T.eye(outp_dim)], axis=0)
return inp.dot(W)
X = T.imatrix("X")
transitions = T.imatrix("transitions")
training_mode = T.scalar("training_mode")
ground_truth_transitions_visible = T.scalar("ground_truth_transitions_visible", dtype="int32")
vs = VariableStore()
# Swap in our own dummy embeddings and weights.
initial_embeddings = np.arange(vocab_size).reshape((vocab_size, 1)).repeat(embedding_dim, axis=1)
self.stack = HardStack(
embedding_dim,
embedding_dim,
vocab_size,
seq_length,
compose_network,
IdentityLayer,
training_mode,
ground_truth_transitions_visible,
vs,
X=X,
transitions=transitions,
make_test_fn=True,
initial_embeddings=initial_embeddings,
use_input_batch_norm=False,
use_input_dropout=False,
)
class HardStackTestCase(unittest.TestCase):
"""Basic functional tests for HardStack with dummy data."""
def _make_stack(self, seq_length=4):
self.embedding_dim = embedding_dim = 3
self.vocab_size = vocab_size = 10
self.seq_length = seq_length
def compose_network(inp, inp_dim, outp_dim, vs, name="compose"):
# Just add the two embeddings!
W = T.concatenate([T.eye(outp_dim), T.eye(outp_dim)], axis=0)
return inp.dot(W)
X = T.imatrix("X")
transitions = T.imatrix("transitions")
training_mode = T.scalar("training_mode")
ground_truth_transitions_visible = T.scalar(
"ground_truth_transitions_visible", dtype="int32")
vs = VariableStore()
# Swap in our own dummy embeddings and weights.
initial_embeddings = np.arange(vocab_size).reshape(
(vocab_size, 1)).repeat(embedding_dim, axis=1)
self.stack = HardStack(embedding_dim,
embedding_dim,
vocab_size,
seq_length,
compose_network,
IdentityLayer,
training_mode,
ground_truth_transitions_visible,
vs,
X=X,
transitions=transitions,
make_test_fn=True,
initial_embeddings=initial_embeddings,
use_input_batch_norm=False,
use_input_dropout=False)
def test_basic_ff(self):
self._make_stack(4)
X = np.array([[3, 1, 2, 0], [3, 2, 4, 5]], dtype=np.int32)
transitions = np.array(
[
# First input: push a bunch onto the stack
[0, 0, 0, 0],
# Second input: push, then merge, then push more. (Leaves one item
# on the buffer.)
[0, 0, 1, 0]
],
dtype=np.int32)
expected = np.array([[[0, 0, 0], [2, 2, 2], [1, 1, 1], [3, 3, 3]],
[[4, 4, 4], [5, 5, 5], [0, 0, 0], [0, 0, 0]]])
ret = self.stack.scan_fn(X, transitions, 1.0, 1)
np.testing.assert_almost_equal(ret, expected)
def test_with_cropped_data(self):
dataset = [{
"tokens": [1, 2, 4, 5, 6, 2],
"transitions": [0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1]
}, {
"tokens": [6, 1],
"transitions": [0, 0, 1]
}, {
"tokens": [6, 1, 2, 3, 5, 1],
"transitions": [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
}]
seq_length = 5
self._make_stack(seq_length)
dataset = CropAndPad(dataset, seq_length)
X = np.array([example["tokens"] for example in dataset],
dtype=np.int32)
transitions = np.array([example["transitions"] for example in dataset],
dtype=np.int32)
expected = np.array([[[8, 8, 8], [0, 0, 0], [0, 0, 0], [0, 0, 0],
[0, 0, 0]],
[[7, 7, 7], [0, 0, 0], [0, 0, 0], [0, 0, 0],
[0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0],
[0, 0, 0]]])
ret = self.stack.scan_fn(X, transitions, 1.0, 1)
np.testing.assert_almost_equal(ret, expected)
class HardStackTestCase(unittest.TestCase):
"""Basic functional tests for HardStack with dummy data."""
def _make_stack(self, seq_length=4):
self.embedding_dim = embedding_dim = 3
self.vocab_size = vocab_size = 10
self.seq_length = seq_length
def compose_network(inp, inp_dim, outp_dim, vs, name="compose"):
# Just add the two embeddings!
W = T.concatenate([T.eye(outp_dim), T.eye(outp_dim)], axis=0)
return inp.dot(W)
X = T.imatrix("X")
transitions = T.imatrix("transitions")
apply_dropout = T.scalar("apply_dropout")
vs = VariableStore()
self.stack = HardStack(
embedding_dim,
embedding_dim,
vocab_size,
seq_length,
compose_network,
IdentityLayer,
apply_dropout,
vs,
X=X,
transitions=transitions,
make_test_fn=True,
)
# Swap in our own dummy embeddings and weights.
embeddings = np.arange(vocab_size).reshape((vocab_size, 1)).repeat(embedding_dim, axis=1)
self.stack.embeddings.set_value(embeddings)
def test_basic_ff(self):
self._make_stack(4)
X = np.array([[3, 1, 2, 0], [3, 2, 4, 5]], dtype=np.int32)
transitions = np.array(
[
# First input: push a bunch onto the stack
[0, 0, 0, 0],
# Second input: push, then merge, then push more. (Leaves one item
# on the buffer.)
[0, 0, 1, 0],
],
dtype=np.int32,
)
expected = np.array(
[[[0, 0, 0], [2, 2, 2], [1, 1, 1], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [0, 0, 0], [0, 0, 0]]]
)
ret = self.stack.scan_fn(X, transitions, 1.0)
np.testing.assert_almost_equal(ret, expected)
def test_with_cropped_data(self):
dataset = [
{"tokens": [1, 2, 4, 5, 6, 2], "transitions": [0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1]},
{"tokens": [6, 1], "transitions": [0, 0, 1]},
{"tokens": [6, 1, 2, 3, 5, 1], "transitions": [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]},
]
seq_length = 5
self._make_stack(seq_length)
dataset = CropAndPad(dataset, seq_length)
X = np.array([example["tokens"] for example in dataset], dtype=np.int32)
transitions = np.array([example["transitions"] for example in dataset], dtype=np.int32)
expected = np.array(
[
[[8, 8, 8], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[7, 7, 7], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
]
)
ret = self.stack.scan_fn(X, transitions, 1.0)
np.testing.assert_almost_equal(ret, expected)