def initialize_graph(self):
if not self.model_settings["static_entity_embeddings"]:
self.entity_embedding = VectorEmbedding(self.entity_indexer,
self.variables,
variable_prefix="entity")
self.add_component(self.entity_embedding)
else:
self.entity_embedding = StaticVectorEmbedding(
self.entity_indexer, self.variables, variable_prefix="entity")
self.add_component(self.entity_embedding)
self.word_embedding = SequenceEmbedding(self.word_indexer,
self.variables,
variable_prefix="word")
self.add_component(self.word_embedding)
self.lstms = [
BiLstm(self.variables,
self.model_settings["word_embedding_dimension"],
variable_prefix="lstm_" + str(i))
for i in range(self.model_settings["n_lstms"])
]
for lstm in self.lstms:
self.add_component(lstm)
self.target_comparator = TargetComparator(
self.variables, variable_prefix="comparison_to_sentence")
self.add_component(self.target_comparator)
self.decoder = SoftmaxDecoder(self.variables)
self.add_component(self.decoder)
self.sentence_to_graph_mapper = EmbeddingRetriever(
self.variables, duplicate_policy="sum", variable_prefix="mapper")
self.add_component(self.sentence_to_graph_mapper)
if self.model_settings["use_transformation"]:
self.transformation = MultilayerPerceptron(
[
self.model_settings["word_embedding_dimension"],
self.model_settings["entity_embedding_dimension"]
],
self.variables,
variable_prefix="transformation",
l2_scale=self.model_settings["regularization_scale"])
self.centroid_transformation = MultilayerPerceptron(
[
self.model_settings["entity_embedding_dimension"],
self.model_settings["word_embedding_dimension"]
],
self.variables,
variable_prefix="centroid_transformation",
l2_scale=self.model_settings["regularization_scale"])
self.add_component(self.centroid_transformation)
self.add_component(self.transformation)
def initialize_graph(self):
if not self.model_settings["static_entity_embeddings"]:
self.entity_embedding = VectorEmbedding(self.entity_indexer,
self.variables,
variable_prefix="entity")
self.add_component(self.entity_embedding)
else:
self.entity_embedding = StaticVectorEmbedding(
self.entity_indexer, self.variables, variable_prefix="entity")
self.add_component(self.entity_embedding)
self.hypergraph = TensorflowHypergraphRepresentation(self.variables)
self.add_component(self.hypergraph)
#self.question_sentence = TensorflowSentenceRepresentation(self.variables)
#self.add_component(self.question_sentence)
self.word_embedding = SequenceEmbedding(self.word_indexer,
self.variables,
variable_prefix="word")
self.add_component(self.word_embedding)
self.target_comparator = TargetComparator(
self.variables, variable_prefix="comparison_to_sentence")
self.add_component(self.target_comparator)
self.decoder = SoftmaxDecoder(self.variables)
self.add_component(self.decoder)
self.sentence_to_graph_mapper = EmbeddingRetriever(
self.variables, duplicate_policy="sum", variable_prefix="mapper")
self.add_component(self.sentence_to_graph_mapper)
if self.model_settings["use_transformation"]:
self.transformation = MultilayerPerceptron(
[
self.model_settings["word_embedding_dimension"],
self.model_settings["entity_embedding_dimension"]
],
self.variables,
variable_prefix="transformation",
l2_scale=self.model_settings["regularization_scale"])
self.centroid_transformation = MultilayerPerceptron(
[
self.model_settings["entity_embedding_dimension"],
self.model_settings["word_embedding_dimension"]
],
self.variables,
variable_prefix="centroid_transformation",
l2_scale=self.model_settings["regularization_scale"])
self.add_component(self.centroid_transformation)
self.add_component(self.transformation)
def initialize_graph(self):
self.hypergraph = TensorflowHypergraphRepresentation(self.variables, edge_dropout_rate=self.model_settings["edge_dropout"])
self.add_component(self.hypergraph)
self.lstms = [BiLstm(self.variables, self.model_settings["word_embedding_dimension"]+1, self.model_settings["lstm_hidden_state_dimension"], variable_prefix="lstm_" + str(i)) for i in
range(self.model_settings["n_lstms"])]
for lstm in self.lstms:
self.add_component(lstm)
self.word_embedding = SequenceEmbedding(self.word_indexer, self.variables, variable_prefix="word", word_dropout_rate=self.model_settings["word_dropout"], is_static=self.model_settings["static_word_embeddings"])
self.add_component(self.word_embedding)
#self.attention = Attention(self.model_settings["word_embedding_dimension"], self.variables, variable_prefix="attention", strategy="constant_query")
self.attention = MultiheadAttention(self.model_settings["lstm_hidden_state_dimension"], self.variables, attention_heads=4,
variable_prefix="attention", strategy="constant_query", attention_dropout=self.model_settings["attention_dropout"])
self.add_component(self.attention)
self.target_comparator = TargetComparator(self.variables, variable_prefix="comparison_to_sentence", comparison="concat")
self.add_component(self.target_comparator)
self.decoder = SoftmaxDecoder(self.variables, self.model_settings["loss"])
self.add_component(self.decoder)
self.candidate_scorer = NeuralNetworkOrFactorizationScorer(self.model_settings, self.variables, variable_prefix="scorer")
self.add_component(self.candidate_scorer)
self.hypergraph_gcn_propagation_units = [None] * self.model_settings["n_layers"]
for layer in range(self.model_settings["n_layers"]):
self.hypergraph_gcn_propagation_units[layer] = HypergraphGcnPropagationUnit("layer_" + str(layer),
self.facts,
self.variables,
self.model_settings["entity_embedding_dimension"],
self.hypergraph,
weights="identity",
biases="relation_specific",
self_weight="identity",
self_bias="zero",
add_inverse_relations=True)
self.add_component(self.hypergraph_gcn_propagation_units[layer])
self.sentence_to_graph_mapper = EmbeddingRetriever(self.variables, duplicate_policy="sum", variable_prefix="mapper")
self.add_component(self.sentence_to_graph_mapper)
self.final_transformation = MultilayerPerceptron([int(self.model_settings["lstm_hidden_state_dimension"]/2) + self.model_settings["entity_embedding_dimension"],
self.model_settings["nn_hidden_state_dimension"],
1],
self.variables,
variable_prefix="transformation",
l2_scale=self.model_settings["regularization_scale"])
self.add_component(self.final_transformation)
def initialize_graph(self):
self.hypergraph = TensorflowHypergraphRepresentation(
self.variables,
edge_dropout_rate=self.model_settings["edge_dropout"])
self.add_component(self.hypergraph)
self.lstms = [
BiLstm(self.variables,
self.model_settings["word_embedding_dimension"] + 1,
self.model_settings["lstm_hidden_state_dimension"],
variable_prefix="lstm_" + str(i))
for i in range(self.model_settings["n_lstms"])
]
for lstm in self.lstms:
self.add_component(lstm)
self.word_embedding = SequenceEmbedding(
self.word_indexer,
self.variables,
variable_prefix="word",
word_dropout_rate=self.model_settings["word_dropout"],
is_static=self.model_settings["static_word_embeddings"])
self.add_component(self.word_embedding)
#self.attention = Attention(self.model_settings["word_embedding_dimension"], self.variables, variable_prefix="attention", strategy="constant_query")
self.attention = MultiheadAttention(
self.model_settings["lstm_hidden_state_dimension"],
self.variables,
attention_heads=self.model_settings["n_attention_heads"],
variable_prefix="attention",
strategy="constant_query",
attention_dropout=self.model_settings["attention_dropout"])
self.add_component(self.attention)
self.target_comparator = TargetComparator(
self.variables,
variable_prefix="comparison_to_sentence",
comparison="concat")
self.add_component(self.target_comparator)
self.decoder = SoftmaxDecoder(self.variables,
self.model_settings["loss"])
self.add_component(self.decoder)
self.candidate_scorer = NeuralNetworkOrFactorizationScorer(
self.model_settings, self.variables, variable_prefix="scorer")
self.add_component(self.candidate_scorer)
gcn_factory = GcnFactory()
gcn_settings = {
"n_layers":
self.model_settings["n_layers"],
"embedding_dimension":
self.model_settings["entity_embedding_dimension"],
"n_relation_types":
self.facts.number_of_relation_types
}
self.hypergraph_gcn_propagation_units = gcn_factory.get_gated_type_only_gcn(
self.hypergraph, self.variables, gcn_settings)
for layer in self.hypergraph_gcn_propagation_units:
self.add_component(layer)
self.sentence_to_graph_mapper = EmbeddingRetriever(
self.variables, duplicate_policy="sum", variable_prefix="mapper")
self.add_component(self.sentence_to_graph_mapper)
self.final_transformation = MultilayerPerceptron(
[
int(self.model_settings["lstm_hidden_state_dimension"] / 2) +
self.model_settings["entity_embedding_dimension"],
self.model_settings["nn_hidden_state_dimension"], 1
],
self.variables,
variable_prefix="transformation",
l2_scale=self.model_settings["regularization_scale"])
self.add_component(self.final_transformation)
def initialize_graph(self):
if not self.model_settings["static_entity_embeddings"]:
self.entity_embedding = VectorEmbedding(self.entity_indexer,
self.variables,
variable_prefix="entity")
self.add_component(self.entity_embedding)
else:
self.entity_embedding = StaticVectorEmbedding(
self.entity_indexer, self.variables, variable_prefix="entity")
self.add_component(self.entity_embedding)
self.hypergraph = TensorflowHypergraphRepresentation(self.variables)
self.add_component(self.hypergraph)
self.word_embedding = SequenceEmbedding(self.word_indexer,
self.variables,
variable_prefix="word")
self.add_component(self.word_embedding)
self.target_comparator = TargetComparator(
self.variables,
variable_prefix="comparison_to_sentence",
comparison="concat")
self.add_component(self.target_comparator)
self.decoder = SoftmaxDecoder(self.variables)
self.add_component(self.decoder)
self.hypergraph_gcn_propagation_units = [
None
] * self.model_settings["n_layers"]
for layer in range(self.model_settings["n_layers"]):
self.hypergraph_gcn_propagation_units[
layer] = HypergraphGcnPropagationUnit(
"layer_" + str(layer),
self.facts,
self.variables,
self.model_settings["entity_embedding_dimension"],
self.hypergraph,
weights="identity",
biases="relation_specific",
self_weight="identity",
self_bias="zero",
add_inverse_relations=False)
self.add_component(self.hypergraph_gcn_propagation_units[layer])
self.sentence_to_graph_mapper = EmbeddingRetriever(
self.variables, duplicate_policy="sum", variable_prefix="mapper")
self.add_component(self.sentence_to_graph_mapper)
if False: #self.model_settings["use_transformation"]:
self.transformation = MultilayerPerceptron(
[
self.model_settings["word_embedding_dimension"],
self.model_settings["entity_embedding_dimension"]
],
self.variables,
variable_prefix="transformation",
l2_scale=self.model_settings["regularization_scale"])
self.centroid_transformation = MultilayerPerceptron(
[
self.model_settings["entity_embedding_dimension"],
self.model_settings["word_embedding_dimension"]
],
self.variables,
variable_prefix="centroid_transformation",
l2_scale=self.model_settings["regularization_scale"])
self.add_component(self.centroid_transformation)
self.add_component(self.transformation)
self.final_transformation = MultilayerPerceptron(
[
self.model_settings["word_embedding_dimension"] +
self.model_settings["entity_embedding_dimension"],
4 * self.model_settings["entity_embedding_dimension"], 1
],
self.variables,
variable_prefix="transformation",
l2_scale=self.model_settings["regularization_scale"])
self.add_component(self.final_transformation)