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
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
|
#!/usr/bin/env python2
import importlib
import logging
import operator
import os
import sys
from functools import reduce
from theano import tensor
import blocks
import fuel
from blocks import roles
from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule, Momentum
from blocks.extensions import Printing, FinishAfter
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
blocks.config.default_seed = 123
fuel.config.default_seed = 123
try:
from blocks.extras.extensions.plot import Plot
use_plot = True
except ImportError:
use_plot = False
from blocks.filter import VariableFilter
from blocks.graph import ComputationGraph, apply_dropout, apply_noise
from blocks.main_loop import MainLoop
from blocks.model import Model
from ext_saveload import SaveLoadParams
from ext_test import RunOnTest
logger = logging.getLogger(__name__)
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')
logger.info('# Configuration: %s' % config.__name__)
for key in dir(config):
if not key.startswith('__') and isinstance(getattr(config, key), (int, str, list, tuple)):
logger.info(' %20s %s' % (key, str(getattr(config, key))))
model = config.Model(config)
model.initialize()
stream = config.Stream(config)
inputs = stream.inputs()
req_vars = model.cost.inputs
train_stream = stream.train(req_vars)
valid_stream = stream.valid(req_vars)
cost = model.cost(**inputs)
cg = ComputationGraph(cost)
monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables))
valid_monitored = monitored
if hasattr(model, 'valid_cost'):
valid_cost = model.valid_cost(**inputs)
valid_cg = ComputationGraph(valid_cost)
valid_monitored = set([valid_cost] + VariableFilter(roles=[roles.COST])(valid_cg.variables))
if hasattr(config, 'dropout') and config.dropout < 1.0:
cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
if hasattr(config, 'noise') and config.noise > 0.0:
cg = apply_noise(cg, config.noise_inputs(cg), config.noise)
cost = cg.outputs[0]
cg = Model(cost)
logger.info('# Parameter shapes:')
parameters_size = 0
for value in cg.parameters:
logger.info(' %20s %s' % (value.get_value().shape, value.name))
parameters_size += reduce(operator.mul, value.get_value().shape, 1)
logger.info('Total number of parameters: %d in %d matrices' % (parameters_size, len(cg.parameters)))
if hasattr(config, 'step_rule'):
step_rule = config.step_rule
else:
step_rule = AdaDelta()
logger.info("Fuel seed: %d" % fuel.config.default_seed)
logger.info("Blocks seed: %d" % blocks.config.default_seed)
params = cg.parameters
algorithm = GradientDescent(
cost=cost,
step_rule=CompositeRule([
RemoveNotFinite(),
step_rule
]),
parameters=params)
plot_vars = [['valid_' + x.name for x in valid_monitored] +
['train_' + x.name for x in valid_monitored]]
logger.info('Plotted variables: %s' % str(plot_vars))
dump_path = os.path.join('model_data', model_name) + '.pkl'
logger.info('Dump path: %s' % dump_path)
extensions=[TrainingDataMonitoring(monitored, prefix='train', every_n_batches=1000),
DataStreamMonitoring(valid_monitored, valid_stream,
prefix='valid',
every_n_batches=1000),
Printing(every_n_batches=1000),
FinishAfter(every_n_batches=10000000),
SaveLoadParams(dump_path, cg,
before_training=True, # before training -> load params
every_n_batches=1000, # every N batches -> save params
after_epoch=True, # after epoch -> save params
after_training=True, # after training -> save params
),
RunOnTest(model_name,
model,
stream,
every_n_batches=1000),
]
if use_plot:
extensions.append(Plot(model_name,
channels=plot_vars,
every_n_batches=500,
server_url='http://eos6:5006/'))
main_loop = MainLoop(
model=cg,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
main_loop.profile.report()
|
