Printing(after_epoch=True)
])
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__":
train_ex = 100
r_dim = 50
# Build model
cost, error_rate = construct_model(Tanh(), r_dim, 30, 2)
# Build datastream
train_stream = prepare_data(
"ARCENE", "train",
LogregOrderTransposeIt(10, True, 'model_param/logreg_param.pkl', 500))
# Train the model
train_model(cost,
error_rate,
train_stream,
load_location=None,
save_location=None)
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', 'valid_cost'],
['train_ber', 'train_ber_reg', 'valid_ber']],
server_url='http://eos21:4201',
every_n_epochs=1*config.pt_freq, after_epoch=False),
FinishAfter(every_n_epochs=10000)
]
)
main_loop.run()
if __name__ == "__main__":
# Build datastream
ref_data, train_stream, valid_stream, test_stream = prepare_data(config)
# Build model
m = config.Model(ref_data, 2)
m.cost.name = 'cost'
m.cost_reg.name = 'cost_reg'
m.ber.name = 'ber'
m.ber_reg.name = 'ber_reg'
m.pred.name = 'pred'
m.confidence.name = 'confidence'
# Train the model
saveloc = 'model_data/%s-%s' % (model_name, config.param_desc)
train_model(m, train_stream, valid_stream,
load_location=None, save_location=None)
DataStreamMonitoring([cost], train_stream, prefix='train',
after_epoch=True),
Printing(after_epoch=True)
]
)
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__":
train_ex = 100
r_dim = 50
# Build model
cost, error_rate = construct_model(Tanh(), r_dim, 30, 2)
# Build datastream
train_stream = prepare_data("ARCENE", "train",
LogregOrderTransposeIt(10, True,
'model_param/logreg_param.pkl', 500))
# Train the model
train_model(cost, error_rate, train_stream, load_location=None,
save_location=None)
every_n_epochs=5 * 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', 'valid_cost'],
['train_ber', 'train_ber_reg', 'valid_ber']],
server_url='http://eos21:4201',
every_n_epochs=1 * config.pt_freq,
after_epoch=False),
FinishAfter(every_n_epochs=10000)
])
main_loop.run()
if __name__ == "__main__":
# Build datastream
ref_data, train_stream, valid_stream, test_stream = prepare_data(config)
# Build model
m = config.Model(ref_data, 2)
m.cost.name = 'cost'
m.cost_reg.name = 'cost_reg'
m.ber.name = 'ber'
m.ber_reg.name = 'ber_reg'
m.pred.name = 'pred'
m.confidence.name = 'confidence'
# Train the model
saveloc = 'model_data/%s-%s' % (model_name, config.param_desc)
train_model(m,
train_stream,
valid_stream,