def pretrain_decade(decades):
model.compile(PRETRAIN(lr=PRETRAIN_LR), "categorical_crossentropy")
unbroken = True
for decade in range(1, decades+1):
try:
model.fit(X, y, nb_epoch=10, validation_data=val)
except KeyboardInterrupt:
unbroken = False
spoken.append("RMSprop pretrain epoch {}: {}".format(decade * 10, keras_speak(model, petofi)))
print(spoken[-1])
log(spoken[-1])
if not unbroken:
log("RMSprop pretrain BROKEN with KEYBOARD_INTERRUPT")
return
def finetune_decade(decades):
model.compile("adam", "categorical_crossentropy")
unbroken = True
for decade in range(1, decades+1):
try:
model.fit(X, y, nb_epoch=10, validation_data=val)
except KeyboardInterrupt:
unbroken = False
spoken.append("SGD finetune epoch {}: {}".format(10 * decade, keras_speak(model, petofi)))
print(spoken[-1])
log(spoken[-1])
if not unbroken:
log("SGD finetune BROKEN with KEYBOARD_INTERRUPT")
return
def finetune_century(century):
model.compile("adam", "categorical_crossentropy")
unbroken = True
for decade in range(1, century+1):
try:
model.fit_generator(the_generator, samples_per_epoch=petofi.N, nb_epoch=1)
except KeyboardInterrupt:
unbroken = False
spoken.append("SGD finetune epoch {}: {}".format(10 * decade, keras_speak(model, petofi)))
print(spoken[-1])
log(spoken[-1])
if not unbroken:
log("SGD finetune BROKEN with KEYBOARD_INTERRUPT")
return
def sample(stochastic=False):
smpl = keras_speak(model, petofi, stochastic, ngrams=SAMPLE_NO_NGRAMS)
log(smpl)
print(smpl)
return smpl