import os import cPickle from blocks.initialization import IsotropicGaussian, Constant import data from model.joint_simple_mlp_tgtcls import Model, Stream n_begin_end_pts = 7 # how many points we consider at the beginning and end of the known trajectory n_valid = 1000 with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: dest_tgtcls = cPickle.load(f) # generate target classes for time prediction as a Fibonacci sequence time_tgtcls = [1, 2] for i in range(21): time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2]) dim_embeddings = [ ('origin_call', data.origin_call_size, 15), ('origin_stand', data.stands_size, 10), ('week_of_year', 52, 10), ('day_of_week', 7, 10), ('qhour_of_day', 24 * 4, 10), ('day_type', 3, 10), ('taxi_id', 448, 10), ] # Common network part dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) dim_hidden = [5000] # Destination prediction part dim_hidden_dest = [] dim_output_dest = dest_tgtcls.shape[0] # Time prediction part dim_hidden_time = [] dim_output_time = len(time_tgtcls) # Cost ratio between distance cost and time cost time_cost_factor = 4 embed_weights_init = IsotropicGaussian(0.01) mlp_weights_init = IsotropicGaussian(0.1) mlp_biases_init = Constant(0.01) learning_rate = 0.0001 momentum = 0.99 batch_size = 200 valid_set = 'cuts/test_times_0'