1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
|
#!/usr/bin/env python
import logging
import numpy
import sys
import os
import importlib
import theano
from theano import tensor
from blocks.extensions import Printing, SimpleExtension, FinishAfter, ProgressBar
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
from blocks.graph import ComputationGraph
from blocks.main_loop import MainLoop
from blocks.model import Model
from blocks.algorithms import GradientDescent
try:
from blocks.extras.extensions.plot import Plot
plot_avail = True
except ImportError:
plot_avail = False
print "No plotting extension available."
import data
from paramsaveload import SaveLoadParams
logging.basicConfig(level='INFO')
logger = logging.getLogger(__name__)
sys.setrecursionlimit(500000)
if __name__ == "__main__":
if len(sys.argv) != 2:
print >> sys.stderr, 'Usage: %s config' % sys.argv[0]
sys.exit(1)
model_name = sys.argv[1]
config = importlib.import_module('.%s' % model_name, 'config')
# Build datastream
path = os.path.join(os.getenv("DATAPATH"), "deepmind-qa/cnn/questions/training")
valid_path = os.path.join(os.getenv("DATAPATH"), "deepmind-qa/cnn/questions/validation")
vocab_path = os.path.join(os.getenv("DATAPATH"), "deepmind-qa/cnn/stats/training/vocab.txt")
ds, train_stream = data.setup_datastream(path, vocab_path, config)
_, valid_stream = data.setup_datastream(valid_path, vocab_path, config)
dump_path = os.path.join("model_params", model_name+".pkl")
# Build model
m = config.Model(config, ds.vocab_size)
# Build the Blocks stuff for training
model = Model(m.sgd_cost)
algorithm = GradientDescent(cost=m.sgd_cost,
step_rule=config.step_rule,
parameters=model.parameters)
extensions = [
TrainingDataMonitoring(
[v for l in m.monitor_vars for v in l],
prefix='train',
every_n_batches=config.print_freq)
]
if config.save_freq is not None and dump_path is not None:
extensions += [
SaveLoadParams(path=dump_path,
model=model,
before_training=True,
after_training=True,
after_epoch=True,
every_n_batches=config.save_freq)
]
if valid_stream is not None and config.valid_freq != -1:
extensions += [
DataStreamMonitoring(
[v for l in m.monitor_vars_valid for v in l],
valid_stream,
prefix='valid',
every_n_batches=config.valid_freq),
]
if plot_avail:
plot_channels = [['train_' + v.name for v in lt] + ['valid_' + v.name for v in lv]
for lt, lv in zip(m.monitor_vars, m.monitor_vars_valid)]
extensions += [
Plot(document='deepmind_qa_'+model_name,
channels=plot_channels,
# server_url='http://localhost:5006/', # If you need, change this
every_n_batches=config.print_freq)
]
extensions += [
Printing(every_n_batches=config.print_freq,
after_epoch=True),
ProgressBar()
]
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions
)
# Run the model !
main_loop.run()
main_loop.profile.report()
# vim: set sts=4 ts=4 sw=4 tw=0 et :
|
