From ff1502ff1b6a4192974f73347b365a5d3a0e1f20 Mon Sep 17 00:00:00 2001
From: Alex Auvolat <alex.auvolat@ens.fr>
Date: Mon, 27 Jul 2015 15:02:27 -0400
Subject: Bidir RNN with window

---
 config/bidirectional_window_1.py          | 41 +++++++++++++++++++++++++++++++
 config/bidirectional_window_1_momentum.py | 41 +++++++++++++++++++++++++++++++
 config/dest_mlp_1_cswdtx_alexandre.py     |  2 +-
 3 files changed, 83 insertions(+), 1 deletion(-)
 create mode 100644 config/bidirectional_window_1.py
 create mode 100644 config/bidirectional_window_1_momentum.py

(limited to 'config')

diff --git a/config/bidirectional_window_1.py b/config/bidirectional_window_1.py
new file mode 100644
index 0000000..8dbf3c1
--- /dev/null
+++ b/config/bidirectional_window_1.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)
+
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)
+
diff --git a/config/dest_mlp_1_cswdtx_alexandre.py b/config/dest_mlp_1_cswdtx_alexandre.py
index 3c013e7..510c16e 100644
--- a/config/dest_mlp_1_cswdtx_alexandre.py
+++ b/config/dest_mlp_1_cswdtx_alexandre.py
@@ -28,7 +28,7 @@ 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)
+step_rule = Momentum(learning_rate=0.001, momentum=0.9)
 
 batch_size = 200
 
-- 
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