taxi

commit 88cdc3f8047a05bc5971eaa915ca6626f89a3e78
parent bd08e452093bba68fe2d79b1e9da76488b203720
Author: AdeB <adbrebs@gmail.com>
Date:   Wed, 24 Jun 2015 15:12:15 -0400

New configs. training step rule out of train.py

Diffstat:
M
config/dest_simple_mlp_emb_only.py
 | 
22
+++++++++++-----------
A
config/memory_network_adeb.py
 | 
46
++++++++++++++++++++++++++++++++++++++++++++++
M
test.py
 | 
5
+++--
M
train.py
 | 
5
++---

4 files changed, 62 insertions(+), 16 deletions(-)
diff --git a/config/dest_simple_mlp_emb_only.py b/config/dest_simple_mlp_emb_only.py
@@ -6,26 +6,26 @@ from model.mlp_emb import Model, Stream
 use_cuts_for_training = True
 
 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),
+    # ('origin_call', data.origin_call_train_size, 100),
+    # ('origin_stand', data.stands_size, 100),
+    # ('week_of_year', 52, 100),
+    # ('day_of_week', 7, 100),
     ('qhour_of_day', 24 * 4, 10),
-    ('day_type', 3, 10),
+    ('day_type', 3, 1),
 ]
 
 dim_input = sum(x for (_, _, x) in dim_embeddings)
-dim_hidden = [200, 100]
+dim_hidden = [10, 10]
 output_mode = "destination"
 dim_output = 2
 
-embed_weights_init = IsotropicGaussian(0.001)
+embed_weights_init = IsotropicGaussian(0.01)
 mlp_weights_init = IsotropicGaussian(0.01)
-mlp_biases_init = Constant(0.001)
+mlp_biases_init = IsotropicGaussian(0.001)
 
-learning_rate = 0.0001
-momentum = 0.99
-batch_size = 32
+learning_rate = 0.001
+momentum = 0.9
+batch_size = 100
 
 valid_set = 'cuts/test_times_0'
 max_splits = 100
diff --git a/config/memory_network_adeb.py b/config/memory_network_adeb.py
@@ -0,0 +1,46 @@
+from blocks.initialization import IsotropicGaussian, Constant
+from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule, Momentum
+
+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 = [100, 100]
+prefix_encoder.weights_init = IsotropicGaussian(0.001)
+prefix_encoder.biases_init = Constant(0.0001)
+
+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(0.001)
+candidate_encoder.biases_init = Constant(0.0001)
+
+
+embed_weights_init = IsotropicGaussian(0.001)
+
+step_rule = Momentum(learning_rate=0.001, momentum=0.9)
+batch_size = 32
+
+valid_set = 'cuts/test_times_0'
+max_splits = 1
+num_cuts = 1000
+
+train_candidate_size = 1000
+valid_candidate_size = 10000
diff --git a/test.py b/test.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python
 
+import cPickle
 import sys
 import os
 import importlib
 import csv
 
-from blocks.dump import load_parameter_values
 from blocks.model import Model
 
 
@@ -24,7 +24,8 @@ if __name__ == "__main__":
     test_stream = stream.test(req_vars_test)
 
     model = Model(model_config.predict(**inputs))
-    parameters = load_parameter_values(os.path.join('model_data', model_name, 'params.npz'))
+    with open(os.path.join('model_data', "{}.pkl".format(model_name))) as f:
+        parameters = cPickle.load(f)
     model.set_param_values(parameters)
 
     if 'destination' in outputs:
diff --git a/train.py b/train.py
@@ -11,7 +11,7 @@ from functools import reduce
 from theano import tensor
 
 from blocks import roles
-from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule
+from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule, Momentum
 from blocks.extensions import Printing, FinishAfter, SimpleExtension
 from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
 
@@ -136,8 +136,7 @@ if __name__ == "__main__":
         cost=cost,
         step_rule=CompositeRule([
                 ElementwiseRemoveNotFinite(),
-                AdaDelta(),
-                #Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
+                config.step_rule,
             ]),
         params=params)