def run():
import sys, getopt, os
load_from = None
opts, args = getopt.getopt(sys.argv[1:], "l:")
for opt, val in opts:
if opt == "-l": load_from = val
bg = QuestionsBatchGenerator(args[0], 1000)
trainig_set_size = bg.training_samples()
validation_data = bg.validateSet()
print "Validation set created"
#print validation_data
model = createModel(bg.rowSize, 2)
if load_from: model.load_weights(load_from)
for i in range(100):
pair_x, pair_truth = bg.loadPair(i)
q1, q2, dup_ = pair_truth
print "Question 1: ", q1
print "Question 2: ", q2
y = model.predict(pair_x)[0]
dup = y[1] > 0.5
correct = dup == dup_
print "Same: %s model: %.3f (%s)" % (dup_, y[1], "CORRECT"
if correct else "incorrect")
print
def run():
import sys, getopt, os
save_to = None
load_from = None
batch_size = 60
nworkers = 2
verbose = 1
opts, args = getopt.getopt(sys.argv[1:], "s:l:n:qw:")
for opt, val in opts:
if opt == "-s": save_to = val
if opt == "-l": load_from = val
if opt == "-w":
load_from = val
save_to = val
if opt == "-n": nworkers = int(val)
if opt == "-q": verbose = 0
os.system("rm logs/*")
bg = QuestionsBatchGenerator(args[0], 1000)
print "Row size=", bg.rowSize
trainig_set_size = bg.training_samples()
validation_data = bg.validateSet()
print "Validation set created"
#print validation_data
model = createModel(bg.rowSize, 2)
if load_from:
model.load_weights(load_from, by_name=True)
print
print "model weights loaded from %s" % (load_from, )
print
tb = TensorBoard(write_images=False, histogram_freq=1.0)
callbacks = [tb]
if save_to:
callbacks.append(
ModelCheckpoint(filepath=save_to,
verbose=1,
save_best_only=True,
save_weights_only=True,
monitor="val_loss"))
model.fit_generator(bg.batches_guargded(batch_size),
int(trainig_set_size / batch_size / 20),
epochs=1000,
verbose=verbose,
workers=nworkers,
callbacks=callbacks,
validation_data=validation_data)
def run():
import sys, getopt, os
save_to = None
load_from = None
batch_size = 30
opts, args = getopt.getopt(sys.argv[1:], "s:l:")
for opt, val in opts:
if opt == "-s": save_to = val
if opt == "-l": load_from = val
os.system("rm logs/*")
bg = QuestionsBatchGenerator(args[0], 1000)
trainig_set_size = bg.training_samples()
validation_data = bg.validateSet()
print "Validation set created"
#print validation_data
model = createModel(bg.rowSize, 2)
tb = TensorBoard(write_images=False, histogram_freq=1.0)
callbacks = [tb]
if save_to:
callbacks.append(
ModelCheckpoint(filepath=save_to,
verbose=1,
save_best_only=True,
save_weights_only=True,
monitor="val_loss"))
model.fit_generator(bg.batches_guargded(batch_size),
int(trainig_set_size / batch_size / 100),
epochs=1000,
workers=4,
callbacks=callbacks,
validation_data=validation_data)