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authorÉtienne Simon <esimon@esimon.eu>2015-07-24 16:09:48 -0400
committerÉtienne Simon <esimon@esimon.eu>2015-07-24 16:09:48 -0400
commit7dab7e47ce0e8c5ae996821794450a9ad3186cd3 (patch)
treee0babcc305696a6e6a67a52acecd300bfdf22cf0 /config/memory_network_mlp.py
parent60e6bc64d8e3c6679a6e2a960513c656d481f0ed (diff)
downloadtaxi-7dab7e47ce0e8c5ae996821794450a9ad3186cd3.tar.gz
taxi-7dab7e47ce0e8c5ae996821794450a9ad3186cd3.zip
Fix memory network
Diffstat (limited to 'config/memory_network_mlp.py')
-rw-r--r--config/memory_network_mlp.py54
1 files changed, 54 insertions, 0 deletions
diff --git a/config/memory_network_mlp.py b/config/memory_network_mlp.py
new file mode 100644
index 0000000..8c7b445
--- /dev/null
+++ b/config/memory_network_mlp.py
@@ -0,0 +1,54 @@
+from blocks.initialization import IsotropicGaussian, Constant
+
+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, 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, 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 = True
+
+
+batch_size = 32
+batch_sort_size = 20
+
+max_splits = 100
+num_cuts = 1000
+
+train_candidate_size = 100
+valid_candidate_size = 100
+test_candidate_size = 100