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author | Étienne Simon <esimon@esimon.eu> | 2015-06-21 17:01:59 -0400 |
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committer | Étienne Simon <esimon@esimon.eu> | 2015-06-21 17:01:59 -0400 |
commit | 2a6980fdac3f6c3987d92882368bf413b50dee36 (patch) | |
tree | f74a175969e720fe9a19544bb6e52c7337df2793 /config/memory_network_1.py | |
parent | 0fd3b1497ffa1bb625bf593c845e28901bc640b7 (diff) | |
download | taxi-2a6980fdac3f6c3987d92882368bf413b50dee36.tar.gz taxi-2a6980fdac3f6c3987d92882368bf413b50dee36.zip |
Add bugged memory networks
Diffstat (limited to 'config/memory_network_1.py')
-rw-r--r-- | config/memory_network_1.py | 43 |
1 files changed, 43 insertions, 0 deletions
diff --git a/config/memory_network_1.py b/config/memory_network_1.py new file mode 100644 index 0000000..00fc958 --- /dev/null +++ b/config/memory_network_1.py @@ -0,0 +1,43 @@ +from blocks.initialization import IsotropicGaussian, Constant + +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 = [50] +prefix_encoder.weights_init = IsotropicGaussian(0.01) +prefix_encoder.biases_init = Constant(0.001) + +candidate_encoder = MLPConfig() +candidate_encoder.dim_input = n_begin_end_pts * 2 + sum(x for (_, _, x) in dim_embeddings) +candidate_encoder.dim_hidden = [50] +candidate_encoder.weights_init = IsotropicGaussian(0.01) +candidate_encoder.biases_init = Constant(0.001) + + +embed_weights_init = IsotropicGaussian(0.001) + +batch_size = 32 + +valid_set = 'cuts/test_times_0' +max_splits = 1 + +train_candidate_size = 1000 +valid_candidate_size = 10000 |