Bidir RNN with window

1 files changed, 41 insertions, 0 deletions

diff --git a/config/bidirectional_window_1_momentum.py b/config/bidirectional_window_1_momentum.py
new file mode 100644
index 0000000..9925db1
--- /dev/null
+++ b/config/bidirectional_window_1_momentum.py
@@ -0,0 +1,41 @@
+import os
+import cPickle
+
+from blocks.algorithms import Momentum
+from blocks.initialization import IsotropicGaussian, Constant
+
+import data
+from model.bidirectional_tgtcls import Model, Stream
+
+
+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),
+    ('taxi_id', data.taxi_id_size, 10),
+]
+
+hidden_state_dim = 100
+
+dim_hidden = [500, 500]
+
+embed_weights_init = IsotropicGaussian(0.01)
+weights_init = IsotropicGaussian(0.1) 
+biases_init = Constant(0.01)
+
+batch_size = 400
+batch_sort_size = 20
+
+max_splits = 100
+train_max_len = 500
+
+window_size = 5
+
+# monitor_freq = 10000 # temporary, for finding good learning rate
+
+step_rule= Momentum(learning_rate=0.001, momentum=0.9)
+