def main():
dl = IMDBDataLoader()
create_logging()
estimator = tf.estimator.Estimator(model_fn)
estimator.train(dl.train_input_fn())
y_pred = np.fromiter(estimator.predict(dl.predict_input_fn()), np.int32)
tf.logging.info('\n' + classification_report(dl.y_test, y_pred))
def main():
create_logging()
tf.logging.info('\n'+pprint.pformat(args.__dict__))
dl = DataLoader()
if not os.path.exists(MODEL_PATH):
os.makedirs(MODEL_PATH)
prev_models = os.listdir(MODEL_PATH)
if prev_models is not None:
for f in prev_models:
os.remove(os.path.join(MODEL_PATH, f))
estimator = tf.estimator.Estimator(model_fn,
model_dir=MODEL_PATH,
config=tf.estimator.RunConfig(keep_checkpoint_max=1))
y_true = get_val_labels()
for _ in range(args.n_epochs):
estimator.train(lambda: dl.train_input_fn())
y_pred = list(estimator.predict(lambda: dl.val_input_fn()))
tf.logging.info('\nVal Log Loss: %.3f\n' % log_loss(
np.asarray(y_true, np.float64),
np.asarray(y_pred, np.float64),
labels=[0, 1]))
submit_arr = np.asarray(list(estimator.predict(lambda: dl.predict_input_fn())))
print(submit_arr.shape)
submit = pd.DataFrame()
submit['y_pre'] = submit_arr
submit.to_csv(SUBMIT_PATH, index=False)
def main():
create_logging()
sess = tf.Session()
dl = IMDBDataLoader(sess)
model = Model(dl)
trainer = Trainer(sess, model, dl)
trainer.train()
def main():
create_logging()
sess = tf.Session()
vocab = IMDBVocab()
dl = VAEDataLoader(sess, vocab)
model = VAE(dl, vocab)
tf.logging.info('\n' + pprint.pformat(tf.trainable_variables()))
trainer = VAETrainer(sess, model, dl, vocab)
trainer.train()
def main():
create_logging()
sess = tf.Session()
vocab = IMDBVocab()
dl = WakeSleepDataLoader(sess, vocab)
model = WakeSleepController(dl, vocab)
tf.logging.info('\n' + pprint.pformat(tf.trainable_variables()))
trainer = WakeSleepTrainer(sess, model, dl, vocab)
model.load(sess, args.vae_ckpt_dir)
trainer.train()
def main():
create_logging()
tf.logging.info('\n' + pprint.pformat(args.__dict__))
sess = tf.Session()
vocab = IMDBVocab()
discri_dl = DiscriminatorDataLoader(sess, vocab)
wake_sleep_dl = WakeSleepDataLoader(sess, vocab)
model = WakeSleepController(discri_dl, wake_sleep_dl, vocab)
tf.logging.info('\n' + pprint.pformat(tf.trainable_variables()))
trainer = WakeSleepTrainer(sess, model, discri_dl, wake_sleep_dl, vocab)
model.load(sess, args.vae_ckpt_dir)
trainer.train()
def main():
create_logging()
dl = DataLoader()
estimator = tf.estimator.Estimator(model_fn)
y_true = get_val_labels()
for _ in range(args.n_epochs):
estimator.train(lambda: dl.train_input_fn())
y_pred = list(estimator.predict(lambda: dl.val_input_fn()))
tf.logging.info('\nVal Log Loss: %.3f\n' %
log_loss(y_true, y_pred, eps=1e-15))
submit_arr = np.asarray(
list(estimator.predict(lambda: dl.predict_input_fn())))
print(submit_arr.shape)
submit = pd.DataFrame()
submit['y_pre'] = submit_arr
submit.to_csv('./submit_siamese_rnn.csv', index=False)