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authorAlex Auvolat <alex.auvolat@ens.fr>2015-07-02 15:00:51 -0400
committerAlex Auvolat <alex.auvolat@ens.fr>2015-07-02 15:00:51 -0400
commit66d2717188e189fde5422576740903ca8e488f63 (patch)
tree555f94f67fe061b1d6fb9977b4c9559d6a16898a /config/dest_mlp_tgtcls_1_cswdtx_small.py
parent6cf80aff3e4dc57d13a33c2946bc0ae57cfae6b8 (diff)
downloadtaxi-66d2717188e189fde5422576740903ca8e488f63.tar.gz
taxi-66d2717188e189fde5422576740903ca8e488f63.zip
Add small models
Diffstat (limited to 'config/dest_mlp_tgtcls_1_cswdtx_small.py')
-rw-r--r--config/dest_mlp_tgtcls_1_cswdtx_small.py38
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diff --git a/config/dest_mlp_tgtcls_1_cswdtx_small.py b/config/dest_mlp_tgtcls_1_cswdtx_small.py
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+import os
+import cPickle
+
+from blocks.initialization import IsotropicGaussian, Constant
+from blocks.algorithms import Momentum
+
+import data
+from model.dest_mlp_tgtcls import Model, Stream
+
+
+n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
+
+with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f)
+
+dim_embeddings = [
+ ('origin_call', data.origin_call_train_size, 10),
+ ('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),
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [100]
+dim_output = tgtcls.shape[0]
+
+embed_weights_init = IsotropicGaussian(0.01)
+mlp_weights_init = IsotropicGaussian(0.1)
+mlp_biases_init = Constant(0.01)
+
+step_rule = Momentum(learning_rate=0.01, momentum=0.9)
+
+batch_size = 200
+
+valid_set = 'cuts/test_times_0'
+max_splits = 100