Fix IRC mode

1 files changed, 0 insertions, 173 deletions

diff --git a/irc.py b/irc.py
deleted file mode 100644
index f8ca125..0000000
--- a/irc.py
+++ /dev/null
@@ -1,173 +0,0 @@
-#!/usr/bin/env python2
-
-import logging
-import sys
-import importlib
-
-import theano
-
-from blocks.extensions import Printing, SimpleExtension, FinishAfter
-from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
-
-from blocks.graph import ComputationGraph
-from blocks.main_loop import MainLoop
-from blocks.model import Model
-from blocks.algorithms import GradientDescent
-
-try:
-    from blocks.extras.extensions.plot import Plot
-    plot_avail = False
-except ImportError:
-    plot_avail = False
-
-
-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)
-
-
-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()
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print >> sys.stderr, 'Usage: %s [options] config' % sys.argv[0]
-        sys.exit(1)
-    model_name = sys.argv[-1]
-    config = importlib.import_module('%s' % model_name)
-
-
-    # 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.pred.name = 'pred'
-
-    # Train the model
-    saveloc = 'model_data/%s-%s' % (model_name, config.param_desc)
-    train_model(m, train_stream, dump_path=saveloc)
-
-
-    # Define the model
-    model = Model(m.sgd_cost)
-
-    # IRC mode : just load the parameters and run an IRC server
-    if '--irc' in sys.argv:
-        try:
-            extensions.append(FinishAfter(before_training=True, after_n_batches=1))
-            print "Initializing main loop"
-            main_loop.run()
-            print "Jumping into IRC"
-            irc.run_forever()
-        except KeyboardInterrupt:
-            pass
-        sys.exit(0)
-
-    # Train the model
-
-    cg = ComputationGraph(m.sgd_cost)
-    algorithm = GradientDescent(cost=m.sgd_cost,
-                                step_rule=config.step_rule,
-                                parameters=cg.parameters)
-
-    algorithm.add_updates(m.states)
-
-    monitor_vars = [v for p in m.monitor_vars for v in p]
-    extensions = [
-            TrainingDataMonitoring(
-                monitor_vars,
-                prefix='train', every_n_epochs=1),
-            Printing(every_n_epochs=1, after_epoch=False),
-
-            ResetStates([v for v, _ in m.states], after_epoch=True)
-    ]
-    if plot_avail:
-        plot_channels = [['train_' + v.name for v in p] for p in m.monitor_vars]
-        extensions.append(
-            Plot(document='text_'+model_name,
-                 channels=plot_channels,
-                 server_url='http://localhost:5006',
-                 every_n_epochs=1, after_epoch=False)
-        )
-    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:
-        irc = IRCClientExt(m, config.sample_temperature,
-                           server='clipper.ens.fr',
-                           port=6667,
-                           nick='frigo',
-                           channels=['#frigotest', '#courssysteme'],
-                           after_batch=True)
-        irc.do('before_training')
-        extensions.append(irc)
-
-    if config.on_irc:
-        irc = IRCClientExt(m, config.sample_temperature,
-                           server='clipper.ens.fr',
-                           port=6667,
-                           nick='frigo',
-                           channels=['#frigotest', '#courssysteme'],
-                           after_batch=True)
-        irc.do('before_training')
-        extensions.append(irc)
-
-	main_loop = MainLoop(
-		model=model,
-		data_stream=train_stream,
-		algorithm=algorithm,
-		extensions=extensions
-	)
-	main_loop.run()
-
-    # IRC mode : just load the parameters and run an IRC server
-    if '--irc' in sys.argv:
-        try:
-            extensions.append(FinishAfter(before_training=True, after_n_batches=1))
-            print "Initializing main loop"
-            main_loop.run()
-            print "Jumping into IRC"
-            irc.run_forever()
-        except KeyboardInterrupt:
-            pass
-        sys.exit(0)
-
-
-
-
-
-
-
-
-#  vim: set sts=4 ts=4 sw=4 tw=0 et :