Refactor architecture so that embedding sizes can be easily changed.

1 files changed, 29 insertions, 0 deletions

diff --git a/config/simple_mlp_tgtcls_0_cs.py b/config/simple_mlp_tgtcls_0_cs.py
new file mode 100644
index 0000000..b174517
--- /dev/null
+++ b/config/simple_mlp_tgtcls_0_cs.py
@@ -0,0 +1,29 @@
+import cPickle
+
+import data
+
+import model.simple_mlp_tgtcls as model
+
+n_dow = 7       # number of division for dayofweek/dayofmonth/hourofday
+n_dom = 31
+n_hour = 24
+
+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)
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = []
+dim_output = tgtcls.shape[0]
+
+learning_rate = 0.0001
+momentum = 0.99
+batch_size = 32