Genericize RNNs

1 files changed, 49 insertions, 0 deletions

diff --git a/config/rnn_lag_tgtcls_1.py b/config/rnn_lag_tgtcls_1.py
new file mode 100644
index 0000000..7a41b70
--- /dev/null
+++ b/config/rnn_lag_tgtcls_1.py
@@ -0,0 +1,49 @@
+import os
+import cPickle
+
+from blocks import roles
+from blocks.bricks import Rectifier
+from blocks.filter import VariableFilter
+from blocks.initialization import IsotropicGaussian, Constant
+
+import data
+from model.rnn_lag_tgtcls import Model, Stream
+
+class EmbedderConfig(object):
+    __slots__ = ('dim_embeddings', 'embed_weights_init')
+
+pre_embedder = EmbedderConfig()
+pre_embedder.embed_weights_init = IsotropicGaussian(0.001)
+pre_embedder.dim_embeddings = [ 
+    ('week_of_year', 52, 10),
+    ('day_of_week', 7, 10),
+    ('qhour_of_day', 24 * 4, 10),
+    ('day_type', 3, 10),
+    ('taxi_id', 448, 10),
+]
+
+post_embedder = EmbedderConfig()
+post_embedder.embed_weights_init = IsotropicGaussian(0.001)
+post_embedder.dim_embeddings = [ 
+    ('origin_call', data.origin_call_train_size, 10), 
+    ('origin_stand', data.stands_size, 10),
+]
+
+with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f)
+
+hidden_state_dim = 100 
+weights_init = IsotropicGaussian(0.01)
+biases_init = Constant(0.001)
+
+rec_to_out_dims = [200, 1000]
+in_to_rec_dims = [200]
+
+dropout = 0.5
+dropout_inputs = VariableFilter(bricks=[Rectifier], name='output')
+
+noise = 0.01
+noise_inputs = VariableFilter(roles=[roles.PARAMETER])
+
+batch_size = 10
+batch_sort_size = 10
+valid_set = 'cuts/test_times_0'