dest_mlp_tgtcls_1_cs.py (788B)
1 import os 2 import cPickle 3 4 from blocks.initialization import IsotropicGaussian, Constant 5 6 import data 7 from model.dest_mlp_tgtcls import Model, Stream 8 9 10 n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory 11 12 with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f) 13 14 dim_embeddings = [ 15 ('origin_call', data.origin_call_train_size, 10), 16 ('origin_stand', data.stands_size, 10) 17 ] 18 19 dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) 20 dim_hidden = [500] 21 dim_output = tgtcls.shape[0] 22 23 embed_weights_init = IsotropicGaussian(0.001) 24 mlp_weights_init = IsotropicGaussian(0.01) 25 mlp_biases_init = Constant(0.001) 26 27 learning_rate = 0.0001 28 momentum = 0.99 29 batch_size = 32 30 31 max_splits = 100