mfile = 'models/en2de.model.h5'
lr_scheduler = LRSchedulerPerStep(
d_model, 4000) # there is a warning that it is slow, however, it's ok.
#lr_scheduler = LRSchedulerPerEpoch(d_model, 4000, Xtrain.shape[0]/64) # this scheduler only update lr per epoch
model_saver = ModelCheckpoint(mfile,
save_best_only=True,
save_weights_only=True)
s2s.compile(Adam(0.001, 0.9, 0.98, epsilon=1e-9))
s2s.model.summary()
try:
s2s.model.load_weights(mfile)
except:
print('\n\nnew model')
if 'test' in sys.argv:
print(
s2s.decode_sequence_fast('A black dog eats food .'.split(),
delimiter=' '))
while True:
quest = input('> ')
print(s2s.decode_sequence_fast(quest.split(), delimiter=' '))
rets = s2s.beam_search(quest.split(), delimiter=' ')
#for x, y in rets: print(x, y)
else:
s2s.model.fit([Xtrain, Ytrain], None, batch_size=64, epochs=5, \
validation_split=.1, \
callbacks=[lr_scheduler, model_saver])
rr = next(gen)
print(rr[0][0].shape, rr[0][1].shape)
rr = next(gen)
print(rr[0][0].shape, rr[0][1].shape)
s2s.compile(opt, active_layers=1)
#s2s.model.fit_generator(gen, steps_per_epoch=2000, epochs=5, callbacks=[lr_scheduler, model_saver])
s2s.model.fit_generator(gen,
steps_per_epoch=5,
epochs=1,
callbacks=[lr_scheduler, model_saver])
s2s.compile(opt, active_layers=2)
s2s.model.fit_generator(gen,
steps_per_epoch=5,
epochs=1,
callbacks=[lr_scheduler, model_saver])
s2s.compile(opt, active_layers=3)
s2s.model.fit_generator(gen,
steps_per_epoch=5,
epochs=1,
callbacks=[lr_scheduler, model_saver])
elif 'test' in cmds:
print(
s2s.decode_sequence_fast(
'Two young , White males are outside near many bushes .'.split()))
while True:
quest = input('> ')
print(s2s.decode_sequence_fast(quest.split()))
rets = s2s.beam_search(quest.split())
for x, y in rets:
print(x, y)
mfile = 'models/en2de.model.h5'
lr_scheduler = LRSchedulerPerStep(d_model, 4000)
model_saver = ModelCheckpoint(mfile,
save_best_only=True,
save_weights_only=True)
s2s.compile(Adam(0.001, 0.9, 0.98, epsilon=1e-9))
try:
s2s.model.load_weights(mfile)
except:
print('\n\nnew model')
if 'eval' in sys.argv:
for x, y in s2s.beam_search('A black dog eats food .'.split(),
delimiter=' '):
print(x, y)
print(
s2s.decode_sequence_readout('A black dog eats food .'.split(),
delimiter=' '))
print(
s2s.decode_sequence_fast('A black dog eats food .'.split(),
delimiter=' '))
while True:
quest = input('> ')
print(s2s.decode_sequence_fast(quest.split(), delimiter=' '))
rets = s2s.beam_search(quest.split(), delimiter=' ')
for x, y in rets:
print(x, y)
elif 'test' in sys.argv:
import ljqpy
