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authorAdeB <adbrebs@gmail.com>2015-05-05 22:15:22 -0400
committerAdeB <adbrebs@gmail.com>2015-05-05 22:15:22 -0400
commitf4d3ee6449217535bdbe19ac9c5fdd825d71b0d3 (patch)
treeb2dfd7f6f914f5f9e4521634b9ffc4a2b0171fdd /config
parent54613c1f9cf510ca7a71d6619418f2247515aec6 (diff)
downloadtaxi-f4d3ee6449217535bdbe19ac9c5fdd825d71b0d3.tar.gz
taxi-f4d3ee6449217535bdbe19ac9c5fdd825d71b0d3.zip
New hyperparameters. Training error is monitored.
Diffstat (limited to 'config')
-rw-r--r--config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py30
1 files changed, 30 insertions, 0 deletions
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
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+++ b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
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+import cPickle
+
+import data
+
+import model.dest_simple_mlp_tgtcls_alexandre as model
+
+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(data.DATA_PATH + "/arrival-clusters.pkl") as f: tgtcls = cPickle.load(f)
+
+dim_embeddings = [
+ ('origin_call', data.n_train_clients+1, 10),
+ ('origin_stand', data.n_stands+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),
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [500]
+dim_output = tgtcls.shape[0]
+
+learning_rate = 0.01
+momentum = 0.9
+batch_size = 200