#!/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)
