taxi

commit ca40e5c81d385e1422cebe40e009d7e93b95bfbb
parent 3acb6f5ec0e3c0d2c6ceef6eacfea731ef18beb1
Author: Alex Auvolat <alex.auvolat@ens.fr>
Date:   Tue, 28 Jul 2015 18:00:18 -0400

Memory networks

Diffstat:
A
config/memory_network_mlp_3_momentum_normalization.py
 | 
55
+++++++++++++++++++++++++++++++++++++++++++++++++++++++
M
config/memory_network_mlp_4_momentum.py
 | 
2
+-
A
config/memory_network_mlp_5_momentum.py
 | 
57
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++

3 files changed, 113 insertions(+), 1 deletion(-)
diff --git a/config/memory_network_mlp_3_momentum_normalization.py b/config/memory_network_mlp_3_momentum_normalization.py
@@ -0,0 +1,55 @@
+from blocks.initialization import IsotropicGaussian, Constant
+from blocks.algorithms import Momentum
+
+from blocks.bricks import Tanh
+
+import data
+from model.memory_network_mlp import Model, Stream
+
+n_begin_end_pts = 5
+
+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),
+]
+
+embed_weights_init = IsotropicGaussian(0.001)
+
+class MLPConfig(object):
+    __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init', 'embed_weights_init', 'dim_embeddings')
+
+prefix_encoder = MLPConfig()
+prefix_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+prefix_encoder.dim_hidden = [500]
+prefix_encoder.weights_init = IsotropicGaussian(0.01)
+prefix_encoder.biases_init = Constant(0.001)
+prefix_encoder.embed_weights_init = embed_weights_init
+prefix_encoder.dim_embeddings = dim_embeddings
+
+candidate_encoder = MLPConfig()
+candidate_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+candidate_encoder.dim_hidden = [500]
+candidate_encoder.weights_init = IsotropicGaussian(0.01)
+candidate_encoder.biases_init = Constant(0.001)
+candidate_encoder.embed_weights_init = embed_weights_init
+candidate_encoder.dim_embeddings = dim_embeddings
+
+representation_size = 500
+representation_activation = Tanh
+
+normalize_representation = True
+
+step_rule = Momentum(learning_rate=0.001, momentum=0.9)
+
+batch_size = 5000
+# batch_sort_size = 20
+
+max_splits = 200
+
+train_candidate_size = 10000
+valid_candidate_size = 10000
+test_candidate_size = 10000
diff --git a/config/memory_network_mlp_4_momentum.py b/config/memory_network_mlp_4_momentum.py
@@ -48,7 +48,7 @@ step_rule = Momentum(learning_rate=0.1, momentum=0.9)
 batch_size = 10000
 # batch_sort_size = 20
 
-# monitor_freq = 2
+monitor_freq = 1000
 
 max_splits = 200
 
diff --git a/config/memory_network_mlp_5_momentum.py b/config/memory_network_mlp_5_momentum.py
@@ -0,0 +1,57 @@
+from blocks.initialization import IsotropicGaussian, Constant
+from blocks.algorithms import Momentum
+
+from blocks.bricks import Tanh
+
+import data
+from model.memory_network_mlp import Model, Stream
+
+n_begin_end_pts = 5
+
+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),
+]
+
+embed_weights_init = IsotropicGaussian(0.001)
+
+class MLPConfig(object):
+    __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init', 'embed_weights_init', 'dim_embeddings')
+
+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]
+prefix_encoder.weights_init = IsotropicGaussian(0.01)
+prefix_encoder.biases_init = Constant(0.001)
+prefix_encoder.embed_weights_init = embed_weights_init
+prefix_encoder.dim_embeddings = dim_embeddings
+
+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]
+candidate_encoder.weights_init = IsotropicGaussian(0.01)
+candidate_encoder.biases_init = Constant(0.001)
+candidate_encoder.embed_weights_init = embed_weights_init
+candidate_encoder.dim_embeddings = dim_embeddings
+
+representation_size = 100
+representation_activation = Tanh
+
+normalize_representation = False
+
+step_rule = Momentum(learning_rate=0.1, momentum=0.9)
+
+batch_size = 10000
+# batch_sort_size = 20
+
+monitor_freq = 1000
+
+max_splits = 200
+
+train_candidate_size = 20000
+valid_candidate_size = 20000
+test_candidate_size = 20000