path: root/train.py

#!/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+'.pkl',
                           model=model,
                           before_training=True,
                           after_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)