1 files changed, 91 insertions, 0 deletions
diff --git a/train.py b/train.py
new file mode 100755
index 0000000..ab973a1
--- /dev/null
+++ b/train.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+
+import logging
+import numpy
+import sys
+import importlib
+
+from blocks.dump import load_parameter_values
+from blocks.dump import MainLoopDumpManager
+from blocks.extensions import Printing
+from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
+from blocks.extensions.plot import Plot
+from blocks.graph import ComputationGraph
+from blocks.main_loop import MainLoop
+from blocks.model import Model
+from blocks.algorithms import GradientDescent
+from theano import tensor
+
+import datastream
+# from apply_model import Apply
+
+logging.basicConfig(level='INFO')
+logger = logging.getLogger(__name__)
+
+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)
+
+
+def train_model(m, train_stream, load_location=None, save_location=None):
+
+    # Define the model
+    model = Model(m.cost)
+
+    # Load the parameters from a dumped model
+    if load_location is not None:
+        logger.info('Loading parameters...')
+        model.set_param_values(load_parameter_values(load_location))
+
+    cg = ComputationGraph(m.cost_reg)
+    algorithm = GradientDescent(cost=m.cost_reg,
+                                step_rule=config.step_rule,
+                                params=cg.parameters)
+    main_loop = MainLoop(
+        model=model,
+        data_stream=train_stream,
+        algorithm=algorithm,
+        extensions=[
+            TrainingDataMonitoring(
+                [m.cost_reg, m.error_rate_reg, m.cost, m.error_rate],
+                prefix='train', every_n_epochs=1*config.pt_freq),
+            Printing(every_n_epochs=1*config.pt_freq, after_epoch=False),
+            Plot(document='tr_'+model_name+'_'+config.param_desc,
+                 channels=[['train_cost', 'train_cost_reg'],
+                           ['train_error_rate', 'train_error_rate_reg']],
+                 every_n_epochs=1*config.pt_freq, after_epoch=False)
+        ]
+    )
+    main_loop.run()
+
+    # Save the main loop
+    if save_location is not None:
+        logger.info('Saving the main loop...')
+        dump_manager = MainLoopDumpManager(save_location)
+        dump_manager.dump(main_loop)
+        logger.info('Saved')
+
+
+if __name__ == "__main__":
+    # Build datastream
+    train_stream = datastream.setup_datastream('data/logcompil.txt',
+                                               config.chars_per_seq,
+                                               config.seqs_per_epoch)
+
+    # 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' % model_name
+    train_model(m, train_stream,
+                load_location=None,
+                save_location=None)
+