def logits_and_state(self):
"""Creates a block that goes from tokens to (logits, state) tuples."""
unknown_idx = len(self.vocab)
def lookup_word(word):
return self.vocab.get(word, unknown_idx)
#(GetItem(key) >> block).eval(inp) => block.eval(inp[key])
# InputTransform(funk): A Python function, lifted to a block.
# Scalar - input to scalar
word2vec = (td.GetItem(0) >> td.InputTransform(lookup_word) >>
td.Scalar('int32') >> self.word_embedding)
#
pair2vec = (self.embed_subtree(), self.embed_subtree())
# Trees are binary, so the tree layer takes two states as its
# input_state.
zero_state = td.Zeros((self.tree_lstm_cell.state_size, ) * 2)
# Input is a word vector.
zero_inp = td.Zeros(self.word_embedding.output_type.shape[0])
# AllOf(a, b, c).eval(inp) => (a.eval(inp), b.eval(inp), c.eval(inp))
word_case = td.AllOf(word2vec, zero_state)
pair_case = td.AllOf(zero_inp, pair2vec)
# OneOf(func, [(key, block),(key,block)])) where funk(input) => key and
# OneOf returns one of blocks
tree2vec = td.OneOf(len, [(1, word_case), (2, pair_case)])
return tree2vec >> self.tree_lstm_cell
def logits_and_state():
"""Creates a block that goes from tokens to (logits, state) tuples."""
unknown_idx = len(word_idx)
lookup_word = lambda word: word_idx.get(
word) # unknown_idx is the default return value
word2vec = (
td.GetItem(0) >> td.GetItem(0) >> td.InputTransform(lookup_word) >>
td.Scalar('int32') >> word_embedding
) # <td.Pipe>: None -> TensorType((200,), 'float32')
context2vec1 = td.GetItem(1) >> td.InputTransform(
makeContextMat) >> td.Vector(10)
context2vec2 = td.GetItem(1) >> td.InputTransform(
makeContextMat) >> td.Vector(10)
ent1posit1 = td.GetItem(2) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
ent1posit2 = td.GetItem(2) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
ent2posit1 = td.GetItem(3) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
ent2posit2 = td.GetItem(3) >> td.InputTransform(
makeEntPositMat) >> td.Vector(10)
pairs2vec = td.GetItem(0) >> (embed_subtree(), embed_subtree())
# our binary Tree can have two child nodes, therefore, we assume the zero state have two child nodes.
zero_state = td.Zeros((tree_lstm.state_size, ) * 2)
# Input is a word vector.
zero_inp = td.Zeros(word_embedding.output_type.shape[0]
) # word_embedding.output_type.shape[0] == 200
word_case = td.AllOf(word2vec, zero_state, context2vec1, ent1posit1,
ent2posit1)
children_case = td.AllOf(zero_inp, pairs2vec, context2vec2, ent1posit2,
ent2posit2)
# if leaf case, go to word case...
tree2vec = td.OneOf(lambda x: 1
if len(x[0]) == 1 else 2, [(1, word_case),
(2, children_case)])
# tree2vec = td.OneOf(lambda pair: len(pair[0]), [(1, word_case), (2, children_case)])
# logits and lstm states
return tree2vec >> tree_lstm >> (output_layer, td.Identity())
def logits_and_state():
"""Creates a block that goes from tokens to (logits, state) tuples."""
unknown_idx = len(word_idx)
lookup_word = lambda word: word_idx.get(word, unknown_idx)
word2vec = (td.GetItem(0) >> td.InputTransform(lookup_word) >>
td.Scalar('int32') >> word_embedding)
pair2vec = (embed_subtree(), embed_subtree())
# Trees are binary, so the tree layer takes two states as its input_state.
zero_state = td.Zeros((tree_lstm.state_size, ) * 2)
# Input is a word vector.
zero_inp = td.Zeros(word_embedding.output_type.shape[0])
word_case = td.AllOf(word2vec, zero_state)
pair_case = td.AllOf(zero_inp, pair2vec)
tree2vec = td.OneOf(len, [(1, word_case), (2, pair_case)])
return tree2vec >> tree_lstm >> (output_layer, td.Identity())