#!/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 import paramsaveload import gentext import ircext 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.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( ircext.IRCClientExt(m, config.sample_temperature, server='irc.ulminfo.fr', port=6667, nick='frigo', channels=['#frigotest', '#courssysteme'], after_batch=True) ) extensions = [] if config.save_freq is not None: extensions.append( Checkpoint(path=dump_path, after_epoch=False, use_cpickle=True, every_n_epochs=config.save_freq), ) if config.sample_freq is not None: extensions.append( gentext.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( ircext.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)