diff options
Diffstat (limited to 'model/memory_network_mlp.py')
| -rw-r--r-- | model/memory_network_mlp.py | 133 |
1 files changed, 49 insertions, 84 deletions
diff --git a/model/memory_network_mlp.py b/model/memory_network_mlp.py index cb8de2a..de07e60 100644 --- a/model/memory_network_mlp.py +++ b/model/memory_network_mlp.py @@ -16,91 +16,56 @@ from model import ContextEmbedder from memory_network import StreamSimple as Stream from memory_network import MemoryNetworkBase +class MLPEncoder(Initializable): + def __init__(self, config, output_dim, activation, **kwargs): + super(RecurrentEncoder, self).__init__(**kwargs) + + self.config = config + self.context_embedder = ContextEmbedder(self.config) + + self.encoder_mlp = 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.extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis + for side in ['first', 'last'] for axis in [0, 1]} -class Model(MemoryNetworkBase): - def __init__(self, **kwargs): - super(Model, self).__init__(**kwargs) - - 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] - + [config.representation_activation()], - dims=[config.candidate_encoder.dim_input] - + config.candidate_encoder.dim_hidden - + [config.representation_size], - name='candidate_encoder') - self.softmax = Softmax() - - self.prefix_extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis - for side in ['first', 'last'] for axis in [0, 1]} - self.candidate_extremities = {'candidate_%s_k_%s' % (side, axname): axis - for side in ['first', 'last'] - for axis, axname in enumerate(['latitude', 'longitude'])} - - self.inputs = self.context_embedder.inputs \ - + ['candidate_%s'%k for k in self.context_embedder.inputs] \ - + self.prefix_extremities.keys() + self.candidate_extremities.keys() self.children = [ self.context_embedder, - self.prefix_encoder, - self.candidate_encoder, - self.softmax ] + self.encoder_mlp ] def _push_initialization_config(self): - for (mlp, config) in [[self.prefix_encoder, self.config.prefix_encoder], - [self.candidate_encoder, self.config.candidate_encoder]]: - mlp.weights_init = config.weights_init - mlp.biases_init = config.biases_init - - @application(outputs=['destination']) - def predict(self, **kwargs): - prefix_embeddings = tuple(self.context_embedder.apply( - **{k: kwargs[k] for k in self.context_embedder.inputs })) - 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) - - candidate_destination = tensor.concatenate( - (tensor.shape_padright(kwargs['candidate_last_k_latitude'][:,-1]), - tensor.shape_padright(kwargs['candidate_last_k_longitude'][:,-1])), - axis=1) - - return tensor.dot(similarity, candidate_destination) - - @predict.property('inputs') - def predict_inputs(self): - return self.inputs - - @application(outputs=['cost']) - def cost(self, **kwargs): - y_hat = self.predict(**kwargs) - y = tensor.concatenate((kwargs['destination_latitude'][:, None], - kwargs['destination_longitude'][:, None]), axis=1) - - return error.erdist(y_hat, y).mean() - - @cost.property('inputs') - def cost_inputs(self): - return self.inputs + ['destination_latitude', 'destination_longitude'] + for brick in [self.contex_encoder, self.encoder_mlp]: + brick.weights_init = self.config.weights_init + brick.biases_init = self.config.biases_init + + @application + def apply(self, **kwargs): + embeddings = tuple(self.context_embedder.apply( + **{k: kwargs[k] for k in self.context_embedder.inputs })) + extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v] + for k, v in self.prefix_extremities.items()) + inputs = tensor.concatenate(extremities + embeddings, axis=1) + + return self.encoder_mlp.apply(inputs) + + @apply.property('inputs') + def apply_inputs(self): + return self.context_embedder.inputs + self.extremities.keys() + + +class Model(MemoryNetworkBase): + def __init__(self, config, **kwargs): + prefix_encoder = MLPEncoder(config.prefix_encoder, + config.representation_size, + config.representation_activation()) + + candidate_encoer = MLPEncoder(config.candidate_encoder, + config.representation_size, + config.representation_activation()) + + super(Model, self).__init__(config, prefix_encoder, candidate_encoder, **kwargs) + + |