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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
|
#!/usr/bin/env python2
import cPickle
import importlib
import logging
import operator
import os
import sys
from functools import reduce
from theano import tensor
from blocks import roles
from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule, Momentum
from blocks.extensions import Printing, FinishAfter, SimpleExtension
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
try:
from blocks.extras.extensions.plotting 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
logger = logging.getLogger(__name__)
class ElementwiseRemoveNotFinite(StepRule):
"""A step rule that replaces non-finite coefficients by zeros.
Replaces non-finite elements (such as ``inf`` or ``NaN``) in a step
(the parameter update of a single shared variable)
with a scaled version of the parameters being updated instead.
Parameters
----------
scaler : float, optional
The scaling applied to the parameter in case the step contains
non-finite elements. Defaults to 0.1.
Notes
-----
This trick was originally used in the GroundHog_ framework.
.. _GroundHog: https://github.com/lisa-groundhog/GroundHog
"""
def __init__(self, scaler=0.1):
self.scaler = scaler
def compute_step(self, param, previous_step):
not_finite = tensor.isnan(previous_step) + tensor.isinf(previous_step)
step = tensor.switch(not_finite, self.scaler * param, previous_step)
return step, []
class SaveLoadParams(SimpleExtension):
def __init__(self, path, model, **kwargs):
super(SaveLoadParams, self).__init__(**kwargs)
self.path = path
self.model = model
def do_save(self):
with open(self.path, 'w') as f:
logger.info('Saving parameters to %s...'%self.path)
cPickle.dump(self.model.get_param_values(), f, protocol=cPickle.HIGHEST_PROTOCOL)
logger.info('Done saving.')
def do_load(self):
try:
with open(self.path, 'r') as f:
logger.info('Loading parameters from %s...'%self.path)
self.model.set_param_values(cPickle.load(f))
except IOError:
pass
def do(self, which_callback, *args):
if which_callback == 'before_training':
self.do_load()
else:
self.do_save()
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 key, value in cg.get_params().iteritems():
logger.info(' %20s %s' % (value.get_value().shape, key))
parameters_size += reduce(operator.mul, value.get_value().shape, 1)
logger.info('Total number of parameters: %d in %d matrices' % (parameters_size, len(cg.get_params())))
if hasattr(config, 'step_rule'):
step_rule = config.step_rule
else:
step_rule = AdaDelta()
params = cg.parameters
algorithm = GradientDescent(
cost=cost,
step_rule=CompositeRule([
ElementwiseRemoveNotFinite(),
step_rule
]),
params=params)
plot_vars = [['valid_' + 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),
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
),
]
if use_plot:
extensions.append(Plot(model_name, channels=plot_vars, every_n_batches=500))
main_loop = MainLoop(
model=cg,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
main_loop.profile.report()
|
