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author | Étienne Simon <esimon@esimon.eu> | 2015-07-24 16:09:48 -0400 |
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committer | Étienne Simon <esimon@esimon.eu> | 2015-07-24 16:09:48 -0400 |
commit | 7dab7e47ce0e8c5ae996821794450a9ad3186cd3 (patch) | |
tree | e0babcc305696a6e6a67a52acecd300bfdf22cf0 /config | |
parent | 60e6bc64d8e3c6679a6e2a960513c656d481f0ed (diff) | |
download | taxi-7dab7e47ce0e8c5ae996821794450a9ad3186cd3.tar.gz taxi-7dab7e47ce0e8c5ae996821794450a9ad3186cd3.zip |
Fix memory network
Diffstat (limited to 'config')
-rw-r--r-- | config/memory_network_mlp.py | 54 |
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 |