diff options
Diffstat (limited to 'model/memory_network.py')
| -rw-r--r-- | model/memory_network.py | 12 |
1 files changed, 8 insertions, 4 deletions
diff --git a/model/memory_network.py b/model/memory_network.py index 5acbfe3..1afc9cb 100644 --- a/model/memory_network.py +++ b/model/memory_network.py @@ -21,11 +21,11 @@ class Model(Initializable): self.context_embedder = ContextEmbedder(config) - self.prefix_encoder = MLP(activations=[Rectifier() for _ in config.prefix_encoder.dim_hidden], - dims=[config.prefix_encoder.dim_input] + config.prefix_encoder.dim_hidden, + self.prefix_encoder = MLP(activations=[Rectifier() for _ in config.prefix_encoder.dim_hidden] + [config.representation_activation()], + dims=[config.prefix_encoder.dim_input] + config.prefix_encoder.dim_hidden + [config.representation_size], name='prefix_encoder') - self.candidate_encoder = MLP(activations=[Rectifier() for _ in config.candidate_encoder.dim_hidden], - dims=[config.candidate_encoder.dim_input] + config.candidate_encoder.dim_hidden, + self.candidate_encoder = MLP(activations=[Rectifier() for _ in config.candidate_encoder.dim_hidden] + [config.representation_activation()], + dims=[config.candidate_encoder.dim_input] + config.candidate_encoder.dim_hidden + [config.representation_size], name='candidate_encoder') self.softmax = Softmax() @@ -46,11 +46,15 @@ class Model(Initializable): prefix_extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v] for k, v in self.prefix_extremities.items()) prefix_inputs = tensor.concatenate(prefix_extremities + prefix_embeddings, axis=1) prefix_representation = self.prefix_encoder.apply(prefix_inputs) + if self.config.normalize_representation: + prefix_representation = prefix_representation / tensor.sqrt((prefix_representation ** 2).sum(axis=1, keepdims=True)) candidate_embeddings = tuple(self.context_embedder.apply(**{k: kwargs['candidate_%s'%k] for k in self.context_embedder.inputs })) candidate_extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v] for k, v in self.candidate_extremities.items()) candidate_inputs = tensor.concatenate(candidate_extremities + candidate_embeddings, axis=1) candidate_representation = self.candidate_encoder.apply(candidate_inputs) + if self.config.normalize_representation: + candidate_representation = candidate_representation / tensor.sqrt((candidate_representation ** 2).sum(axis=1, keepdims=True)) similarity_score = tensor.dot(prefix_representation, candidate_representation.T) similarity = self.softmax.apply(similarity_score) |