datasets, rnn = constructModels(parameters, seed, verboseOutputter)
### From here the experiment should be the same every time
# Start experiment clock
start = time.clock()
# Train on all datasets in succession
train(rnn,
datasets,
parameters,
name,
start,
saveModels=saveModels,
targets=not parameters['single_digit'],
verboseOutputter=verboseOutputter)
print("Training all datasets finished!")
# Final test on last dataset
test(rnn,
datasets[-1],
parameters,
start,
show_prediction_conf_matrix=False)
# Save weights to pickles
if (saveModels):
saveVars = rnn.getVars()
save_to_pickle('saved_models/%s.model' % name, saveVars)
# Report on error
print("Train: %d" % k)
printF("Total error: %.2f" % total_error, experimentId,
currentIteration)
printF("Mean error: %.8f" % (total_error / float(k)), experimentId,
currentIteration)
# Intermediate testing if this was not the last iteration of training
# and we have passed the testing threshold
sampleSize = parameters['sample_testing_size']
if (r == parameters['repetitions'] - 1):
sampleSize = False
test(model,
dataset,
parameters,
model.n_max_digits,
print_samples=parameters['debug'],
sample_size=sampleSize)
# Save weights to pickles
save_modulo = 50
if (saveModels and (r + 1) % save_modulo == 0):
saveVars = model.getVars()
save_to_pickle('saved_models/%s_%d.model' % (name, r),
saveVars,
settings=parameters)
printF("Training finished!", experimentId, currentIteration)
trackerreporter.experimentDone(experimentId)
import os, sys
from tools.file import load_from_pickle_with_filename, save_to_pickle
from tools.model import constructModels
import theano
if __name__ == '__main__':
theano.config.floatX = 'float32'
name = sys.argv[1]
filepath = "./saved_models/%s.floats" % name
if (os.path.isfile(filepath)):
modelName = name
result = load_from_pickle_with_filename(filepath)
if (result is not False):
savedVars, settings = result
dataset, rnn = constructModels(settings, 0, None)
modelSet = rnn.loadVars(savedVars, floats=True)
if (modelSet):
saveVars = rnn.getVars()
save_to_pickle('saved_models/%s_from_floats.model' % (name),
saveVars,
settings=settings)
print("Success!")
else:
print("Error (3)")
else:
print("Error (2)")
else:
print("Error (1)")