Add bidirectional with recurrent ReLu

2 files changed, 38 insertions, 1 deletions

diff --git a/config/bidirectional_tgtcls_relu_1.py b/config/bidirectional_tgtcls_relu_1.py
new file mode 100644
index 0000000..918c8e2
--- /dev/null
+++ b/config/bidirectional_tgtcls_relu_1.py
@@ -0,0 +1,36 @@
+import os
+import cPickle
+
+from blocks.bricks import Rectifier
+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),
+]
+
+rec_activation = Rectifier
+
+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 = 100
+batch_sort_size = 20
+
+max_splits = 100
+
diff --git a/model/bidirectional.py b/model/bidirectional.py
index 4c4ffb0..5779752 100644
--- a/model/bidirectional.py
+++ b/model/bidirectional.py
@@ -37,7 +37,8 @@ class BidiRNN(Initializable):
 
         self.context_embedder = ContextEmbedder(config)
         
-        self.rec = SegregatedBidirectional(LSTM(dim=config.hidden_state_dim, name='recurrent'))
+        act = config.rec_activation() if hasattr(config, 'rec_activation') else None
+        self.rec = SegregatedBidirectional(LSTM(dim=config.hidden_state_dim, activation=act, name='recurrent'))
 
         self.fwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
                              prototype=Linear(), name='fwd_fork')