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#!/usr/bin/env python
import logging
import numpy
import sys
import importlib
from contextlib import closing
import theano
from theano import tensor
from theano.tensor.shared_randomstreams import RandomStreams
from blocks.serialization import load_parameter_values, secure_dump, BRICK_DELIMITER
from blocks.extensions import Printing, SimpleExtension
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
# from blocks.extras.extensions.plot import Plot
from blocks.extensions.saveload import Checkpoint, Load
from blocks.graph import ComputationGraph
from blocks.main_loop import MainLoop
from blocks.model import Model
from blocks.algorithms import GradientDescent
import datastream
from paramsaveload import SaveLoadParams
from gentext import GenText
from ircext import IRCClientExt
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)
class ResetStates(SimpleExtension):
def __init__(self, state_vars, **kwargs):
super(ResetStates, self).__init__(**kwargs)
self.f = theano.function(
inputs=[], outputs=[],
updates=[(v, v.zeros_like()) for v in state_vars])
def do(self, which_callback, *args):
self.f()
def train_model(m, train_stream, dump_path=None):
# Define the model
model = Model(m.cost)
cg = ComputationGraph(m.cost_reg)
algorithm = GradientDescent(cost=m.cost_reg,
step_rule=config.step_rule,
params=cg.parameters)
algorithm.add_updates(m.states)
extensions = []
if config.save_freq is not None and dump_path is not None:
extensions.append(
SaveLoadParams(path=dump_path,
model=model,
before_training=True,
after_epoch=False,
every_n_epochs=config.save_freq)
)
if config.sample_freq is not None:
extensions.append(
GenText(m, '\nalex\ttu crois ?\n',
config.sample_len, config.sample_temperature,
every_n_epochs=config.sample_freq,
after_epoch=False, before_training=True)
)
if config.on_irc:
extensions.append(
IRCClientExt(m, config.sample_temperature,
server='irc.ulminfo.fr',
port=6667,
nick='frigo',
channels=['#frigotest', '#courssysteme'],
after_batch=True)
)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions + [
TrainingDataMonitoring(
[m.cost_reg, m.error_rate_reg, m.cost, m.error_rate],
prefix='train', every_n_epochs=1),
Printing(every_n_epochs=1, after_epoch=False),
# Plot(document='text_'+model_name+'_'+config.param_desc,
# channels=[['train_cost', 'train_cost_reg'],
# ['train_error_rate', 'train_error_rate_reg']],
# server_url='http://eos21:4201/',
# every_n_epochs=1, after_epoch=False),
ResetStates([v for v, _ in m.states], after_epoch=True)
]
)
main_loop.run()
if __name__ == "__main__":
# Build datastream
train_stream = datastream.setup_datastream('data/logcompil.txt',
config.num_seqs,
config.seq_len,
config.seq_div_size)
# Build model
m = config.Model()
m.cost.name = 'cost'
m.cost_reg.name = 'cost_reg'
m.error_rate.name = 'error_rate'
m.error_rate_reg.name = 'error_rate_reg'
m.pred.name = 'pred'
# Train the model
saveloc = 'model_data/%s-%s' % (model_name, config.param_desc)
train_model(m, train_stream, dump_path=saveloc)
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