memory_network_bidir.py (1486B)
1 from blocks.initialization import IsotropicGaussian, Constant 2 3 from blocks.bricks import Tanh 4 5 import data 6 from model.memory_network_bidir import Model, Stream 7 8 9 dim_embeddings = [ 10 ('origin_call', data.origin_call_train_size, 10), 11 ('origin_stand', data.stands_size, 10), 12 ('week_of_year', 52, 10), 13 ('day_of_week', 7, 10), 14 ('qhour_of_day', 24 * 4, 10), 15 ('day_type', 3, 10), 16 ] 17 18 embed_weights_init = IsotropicGaussian(0.001) 19 20 21 class RNNConfig(object): 22 __slots__ = ('rec_state_dim', 'dim_embeddings', 'embed_weights_init', 23 'dim_hidden', 'weights_init', 'biases_init') 24 25 prefix_encoder = RNNConfig() 26 prefix_encoder.dim_embeddings = dim_embeddings 27 prefix_encoder.embed_weights_init = embed_weights_init 28 prefix_encoder.rec_state_dim = 100 29 prefix_encoder.dim_hidden = [100, 100] 30 prefix_encoder.weights_init = IsotropicGaussian(0.01) 31 prefix_encoder.biases_init = Constant(0.001) 32 33 candidate_encoder = RNNConfig() 34 candidate_encoder.dim_embeddings = dim_embeddings 35 candidate_encoder.embed_weights_init = embed_weights_init 36 candidate_encoder.rec_state_dim = 100 37 candidate_encoder.dim_hidden = [100, 100] 38 candidate_encoder.weights_init = IsotropicGaussian(0.01) 39 candidate_encoder.biases_init = Constant(0.001) 40 41 representation_size = 100 42 representation_activation = Tanh 43 44 normalize_representation = True 45 46 47 batch_size = 32 48 batch_sort_size = 20 49 50 max_splits = 100 51 num_cuts = 1000 52 53 train_candidate_size = 100 54 valid_candidate_size = 100 55 test_candidate_size = 100