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1 files changed, 112 insertions, 0 deletions

diff --git a/train.py b/train.py
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+++ b/train.py
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+#!/usr/bin/env python
+
+import logging
+import numpy
+import sys
+import os
+import importlib
+
+import theano
+from theano import tensor
+
+from blocks.extensions import Printing, SimpleExtension, FinishAfter, ProgressBar
+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 = True
+except ImportError:
+    plot_avail = False
+    print "No plotting extension available."
+
+import data
+from paramsaveload import SaveLoadParams
+
+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, 'config')
+
+    # Build datastream
+    path = os.path.join(os.getenv("DATAPATH"), "deepmind-qa/cnn/questions/training")
+    valid_path = os.path.join(os.getenv("DATAPATH"), "deepmind-qa/cnn/questions/validation")
+    vocab_path = os.path.join(os.getenv("DATAPATH"), "deepmind-qa/cnn/stats/training/vocab.txt")
+
+    ds, train_stream = data.setup_datastream(path, vocab_path, config)
+    _, valid_stream = data.setup_datastream(valid_path, vocab_path, config)
+
+    dump_path = os.path.join("model_params", model_name+".pkl")
+
+    # Build model
+    m = config.Model(config, ds.vocab_size)
+
+    # Build the Blocks stuff for training
+    model = Model(m.sgd_cost)
+
+    algorithm = GradientDescent(cost=m.sgd_cost,
+                                step_rule=config.step_rule,
+                                parameters=model.parameters)
+
+    extensions = [
+            TrainingDataMonitoring(
+                [v for l in m.monitor_vars for v in l],
+                prefix='train',
+                every_n_batches=config.print_freq)
+    ]
+    if config.save_freq is not None and dump_path is not None:
+        extensions += [
+            SaveLoadParams(path=dump_path,
+                           model=model,
+                           before_training=True,
+                           after_training=True,
+                           after_epoch=True,
+                           every_n_batches=config.save_freq)
+        ]
+    if valid_stream is not None and config.valid_freq != -1:
+        extensions += [
+            DataStreamMonitoring(
+                [v for l in m.monitor_vars_valid for v in l],
+                valid_stream,
+                prefix='valid',
+                every_n_batches=config.valid_freq),
+        ]
+    if plot_avail:
+        plot_channels = [['train_' + v.name for v in lt] + ['valid_' + v.name for v in lv]
+                         for lt, lv in zip(m.monitor_vars, m.monitor_vars_valid)]
+        extensions += [
+            Plot(document='deepmind_qa_'+model_name,
+                 channels=plot_channels,
+                 # server_url='http://localhost:5006/', # If you need, change this
+                 every_n_batches=config.print_freq)
+        ]
+    extensions += [
+            Printing(every_n_batches=config.print_freq,
+                     after_epoch=True),
+            ProgressBar()
+    ]
+
+    main_loop = MainLoop(
+        model=model,
+        data_stream=train_stream,
+        algorithm=algorithm,
+        extensions=extensions
+    )
+
+    # Run the model !
+    main_loop.run()
+    main_loop.profile.report()
+
+
+
+#  vim: set sts=4 ts=4 sw=4 tw=0 et :