def __init__(self):
print 'call me'
self.parameters = Parameters()
if LBL:
graph.output_weights = self.parameters.output_weights
graph.output_biases = self.parameters.output_biases
graph.score_biases = self.parameters.score_biases
else:
graph.hidden_weights = self.parameters.hidden_weights
graph.hidden_biases = self.parameters.hidden_biases
graph.output_weights = self.parameters.output_weights
graph.output_biases = self.parameters.output_biases
# (self.graph_train, self.graph_predict, self.graph_verbose_predict) = graph.functions(self.parameters)
import sets
self.train_loss = MovingAverage()
self.train_err = MovingAverage()
self.train_lossnonzero = MovingAverage()
self.train_squashloss = MovingAverage()
self.train_unpenalized_loss = MovingAverage()
self.train_l1penalty = MovingAverage()
self.train_unpenalized_lossnonzero = MovingAverage()
self.train_correct_score = MovingAverage()
self.train_noise_score = MovingAverage()
self.train_cnt = 0
def __init__(self,
modelname="",
window_size=HYPERPARAMETERS["WINDOW_SIZE"],
vocab_size=vocabulary.wordmap().len,
embedding_size=HYPERPARAMETERS["EMBEDDING_SIZE"],
hidden_size=HYPERPARAMETERS["HIDDEN_SIZE"],
seed=miscglobals.RANDOMSEED,
initial_embeddings=None,
two_hidden_layers=HYPERPARAMETERS["TWO_HIDDEN_LAYERS"]):
self.modelname = modelname
self.parameters = Parameters(window_size, vocab_size, embedding_size,
hidden_size, seed, initial_embeddings,
two_hidden_layers)
if LBL:
graph.output_weights = self.parameters.output_weights
graph.output_biases = self.parameters.output_biases
graph.score_biases = self.parameters.score_biases
else:
graph.hidden_weights = self.parameters.hidden_weights
graph.hidden_biases = self.parameters.hidden_biases
if self.parameters.two_hidden_layers:
graph.hidden2_weights = self.parameters.hidden2_weights
graph.hidden2_biases = self.parameters.hidden2_biases
graph.output_weights = self.parameters.output_weights
graph.output_biases = self.parameters.output_biases
# (self.graph_train, self.graph_predict, self.graph_verbose_predict) = graph.functions(self.parameters)
import sets
self.train_loss = MovingAverage()
self.train_err = MovingAverage()
self.train_lossnonzero = MovingAverage()
self.train_squashloss = MovingAverage()
self.train_unpenalized_loss = MovingAverage()
self.train_l1penalty = MovingAverage()
self.train_unpenalized_lossnonzero = MovingAverage()
self.train_correct_score = MovingAverage()
self.train_noise_score = MovingAverage()
self.train_cnt = 0
#RELOAD previous model
channel.save()
err = dict([(trainsize, {}) for trainsize in VALIDATION_TRAININGSIZE])
rebuildunsup(model, LR, NOISE_LVL, ACTIVATION_REGULARIZATION_COEFF,
WEIGHT_REGULARIZATION_COEFF, BATCHSIZE, train)
epoch = 0
if epoch in EPOCHSTEST:
svm_validation(err, epoch, model, train, datatrain, datatrainsave,
datatest, datatestsave, VALIDATION_TRAININGSIZE,
VALIDATION_RUNS_FOR_EACH_TRAININGSIZE, PATH_SAVE,
PATH_DATA, NAME_DATATEST)
channel.save()
train_reconstruction_error_mvgavg = MovingAverage()
for epoch in xrange(1, NEPOCHS + 1):
time1 = time.time()
state.currentepoch = epoch
for filenb in xrange(1, NB_FILES + 1):
print >> sys.stderr, "\t\tAbout to read file %s..." % percent(
filenb, NB_FILES)
print >> sys.stderr, "\t\t", stats()
# initial_file_time = time.time()
f = open(PATH_DATA + NAME_DATA + '_%s.pkl' % filenb, 'r')
object = numpy.asarray(cPickle.load(f), dtype=theano.config.floatX)
print >> sys.stderr, "\t\t...read file %s" % percent(
filenb, NB_FILES)
print >> sys.stderr, "\t\t", stats()
# The last training file is not of the same shape as the other training files.
# So, to avoid a GPU memory error, we want to make sure it is the same size.
import sys
# Restrict to a particular path.
class RequestHandler(SimpleXMLRPCRequestHandler):
rpc_paths = ('/RPC2', )
# Create server
server = SimpleXMLRPCServer(("0.0.0.0", jv_port + 1),
requestHandler=RequestHandler)
server.register_introspection_functions()
from common.movingaverage import MovingAverage
broke = MovingAverage()
def extractKeyphrases(txt):
if broke.cnt % 100 == 0:
print >> sys.stderr, "%s documents could NOT have keyphrase extracted" % broke
try:
kw = s.kea.extractKeyphrases(txt)
broke.add(0)
return kw
except:
print >> sys.stderr, "Oops! Couldn't extract keyphrases over:", repr(
txt)
broke.add(1)
return []
