Revive project

1 files changed, 173 insertions, 0 deletions

diff --git a/irc.py b/irc.py
new file mode 100644
index 0000000..f8ca125
--- /dev/null
+++ b/irc.py
@@ -0,0 +1,173 @@
+#!/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 :