#!/usr/bin/env python
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
from blocks.extensions import Printing, FinishAfter
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
from blocks.extensions.plot import Plot
from blocks.extensions.saveload import Dump, LoadFromDump
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, []
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)
unmonitor = set()
if hasattr(config, 'dropout') and config.dropout < 1.0:
unmonitor.update(VariableFilter(roles=[roles.COST])(cg.variables))
cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
if hasattr(config, 'noise') and config.noise > 0.0:
unmonitor.update(VariableFilter(roles=[roles.COST])(cg.variables))
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())))
params = cg.parameters
algorithm = GradientDescent(
cost=cost,
step_rule=CompositeRule([
ElementwiseRemoveNotFinite(),
AdaDelta(),
#Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
]),
params=params)
monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables)) - unmonitor
plot_vars = [['valid_' + x.name for x in monitored]]
logger.info('Plotted variables: %s' % str(plot_vars))
dump_path = os.path.join('model_data', model_name)
logger.info('Dump path: %s' % dump_path)
extensions=[TrainingDataMonitoring(monitored, prefix='train', every_n_batches=1000),
DataStreamMonitoring(monitored, valid_stream,
prefix='valid',
every_n_batches=1000),
Printing(every_n_batches=1000),
Plot(model_name, channels=plot_vars, every_n_batches=500),
Dump(dump_path, every_n_batches=5000),
LoadFromDump(dump_path),
#FinishAfter(after_n_batches=2),
]
main_loop = MainLoop(
model=cg,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
main_loop.profile.report()
