New configs. training step rule out of train.py

2 files changed, 57 insertions, 11 deletions

diff --git a/config/dest_simple_mlp_emb_only.py b/config/dest_simple_mlp_emb_only.py
index e5c91b8..76acdfa 100644
--- a/config/dest_simple_mlp_emb_only.py
+++ b/config/dest_simple_mlp_emb_only.py
@@ -6,26 +6,26 @@ from model.mlp_emb import Model, Stream
 use_cuts_for_training = True
 
 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),
+    # ('origin_call', data.origin_call_train_size, 100),
+    # ('origin_stand', data.stands_size, 100),
+    # ('week_of_year', 52, 100),
+    # ('day_of_week', 7, 100),
     ('qhour_of_day', 24 * 4, 10),
-    ('day_type', 3, 10),
+    ('day_type', 3, 1),
 ]
 
 dim_input = sum(x for (_, _, x) in dim_embeddings)
-dim_hidden = [200, 100]
+dim_hidden = [10, 10]
 output_mode = "destination"
 dim_output = 2
 
-embed_weights_init = IsotropicGaussian(0.001)
+embed_weights_init = IsotropicGaussian(0.01)
 mlp_weights_init = IsotropicGaussian(0.01)
-mlp_biases_init = Constant(0.001)
+mlp_biases_init = IsotropicGaussian(0.001)
 
-learning_rate = 0.0001
-momentum = 0.99
-batch_size = 32
+learning_rate = 0.001
+momentum = 0.9
+batch_size = 100
 
 valid_set = 'cuts/test_times_0'
 max_splits = 100
diff --git a/config/memory_network_adeb.py b/config/memory_network_adeb.py
new file mode 100644
index 0000000..1d7dc5d
--- /dev/null
+++ b/config/memory_network_adeb.py
@@ -0,0 +1,46 @@
+from blocks.initialization import IsotropicGaussian, Constant
+from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule, Momentum
+
+import data
+from model.memory_network import Model, Stream
+
+
+n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
+
+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),
+]
+
+
+class MLPConfig(object):
+    __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init')
+
+prefix_encoder = MLPConfig()
+prefix_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+prefix_encoder.dim_hidden = [100, 100]
+prefix_encoder.weights_init = IsotropicGaussian(0.001)
+prefix_encoder.biases_init = Constant(0.0001)
+
+candidate_encoder = MLPConfig()
+candidate_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+candidate_encoder.dim_hidden = [100, 100]
+candidate_encoder.weights_init = IsotropicGaussian(0.001)
+candidate_encoder.biases_init = Constant(0.0001)
+
+
+embed_weights_init = IsotropicGaussian(0.001)
+
+step_rule = Momentum(learning_rate=0.001, momentum=0.9)
+batch_size = 32
+
+valid_set = 'cuts/test_times_0'
+max_splits = 1
+num_cuts = 1000
+
+train_candidate_size = 1000
+valid_candidate_size = 10000