From 20a1a01cef9d61ce9dd09995f2c811ab5aca2a9d Mon Sep 17 00:00:00 2001 From: Alex Auvolat Date: Fri, 8 May 2015 14:59:44 -0400 Subject: Add model for a network that predicts both time and destination. --- config/joint_simple_mlp_tgtcls_1_cswdtx.py | 52 ++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 config/joint_simple_mlp_tgtcls_1_cswdtx.py (limited to 'config/joint_simple_mlp_tgtcls_1_cswdtx.py') diff --git a/config/joint_simple_mlp_tgtcls_1_cswdtx.py b/config/joint_simple_mlp_tgtcls_1_cswdtx.py new file mode 100644 index 0000000..f3de40b --- /dev/null +++ b/config/joint_simple_mlp_tgtcls_1_cswdtx.py @@ -0,0 +1,52 @@ +import cPickle + +import model.joint_simple_mlp_tgtcls as model + +from blocks.initialization import IsotropicGaussian, Constant + +import data + +n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory +n_end_pts = 5 + +n_valid = 1000 + +with open("%s/arrival-clusters.pkl" % data.path) 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(22): + time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2]) + +dim_embeddings = [ + ('origin_call', data.origin_call_size+1, 10), + ('origin_stand', data.stands_size+1, 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 = [500] + +# Destination prediction part +dim_hidden_dest = [] +dim_output_dest = len(dest_tgtcls) + +# Time prediction part +dim_hidden_time = [] +dim_output_time = len(time_tgtcls) + +embed_weights_init = IsotropicGaussian(0.001) +mlp_weights_init = IsotropicGaussian(0.01) +mlp_biases_init = Constant(0.001) + +learning_rate = 0.0001 +momentum = 0.99 +batch_size = 200 + +valid_set = 'cuts/test_times_0' -- cgit v1.2.3