def evaluate(hparams, ckpt):
if hparams.model_architecture == "rnn-model": model_creator = model.RNN
else: raise ValueError("Unknown model architecture. Only simple_rnn is supported so far.")
if hparams.val_target_path:
eval_model = model_helper.create_eval_model(model_creator, hparams, tf.contrib.learn.ModeKeys.EVAL)
eval_sess = tf.Session(config=utils.get_config_proto(), graph=eval_model.graph)
with eval_model.graph.as_default():
loaded_eval_model = model_helper.load_model(eval_model.model, eval_sess, "evaluation", ckpt)
iterator_feed_dict={
eval_model.input_file_placeholder: hparams.eval_input_path,
eval_model.output_file_placeholder: hparams.eval_target_path
}
eval_loss = eval(loaded_eval_model, eval_sess, eval_model.iterator, iterator_feed_dict)
print("Eval loss: %.3f"%eval_loss)
print("Starting predictions:")
prediction_model = model_helper.create_infer_model(model_creator, hparams, tf.contrib.learn.ModeKeys.INFER)
prediction_sess = tf.Session(config=utils.get_config_proto(), graph=prediction_model.graph)
with prediction_model.graph.as_default():
loaded_prediction_model = model_helper.load_model(prediction_model.model, prediction_sess, "prediction", ckpt)
iterator_feed_dict = {
prediction_model.input_file_placeholder: hparams.val_input_path,
}
predictions=predict(loaded_prediction_model, prediction_sess, prediction_model.iterator, iterator_feed_dict)
np.savetxt(os.path.join(hparams.eval_output_folder, "classes.txt"), predictions["classes"])
np.savetxt(os.path.join(hparams.eval_output_folder, "probabilities.txt"), predictions["probabilities"])
def start_sess_and_load_model(infer_model, ckpt_path):
"""Start session and load model."""
sess = tf.Session(graph=infer_model.graph, config=utils.get_config_proto())
with infer_model.graph.as_default():
loaded_infer_model = model_helper.load_model(infer_model.model,
ckpt_path, sess, "infer")
return sess, loaded_infer_model
def main(args):
mnist = utils.read_data_sets(args.train_dir)
config_proto = utils.get_config_proto()
with tf.device('/gpu:0'):
if not os.path.exists("../saves"):
os.mkdir("../saves")
sess = tf.Session(config=config_proto)
model = gan.GAN(args, sess)
total_batch = mnist.train.num_examples // args.batch_size
for epoch in range(1, args.nb_epochs + 1):
for i in range(1, total_batch + 1):
global_step = sess.run(model.global_step)
x_batch, _ = mnist.train.next_batch(args.batch_size)
noise = np.random.normal(size=[args.batch_size, args.noise_dim])
D_loss = model.d_batch_fit(x_batch, noise)
G_loss = model.g_batch_fit(noise)
if i % args.log_period == 0:
print "Epoch: ", '%02d' % epoch, "Batch: ", '%04d' % i, "D_loss: ", '%9.9f' % D_loss, "G_loss: ", '%9.9f' % G_loss
if epoch % 50 == 0:
print "- " * 50
if epoch % args.save_period == 0:
if not os.path.exists("../saves/imgs"):
os.mkdir("../saves/imgs")
z = np.random.normal(size=[100, args.noise_dim])
gen_images = np.reshape(model.generate(z), (100, 28, 28, 1))
utils.save_images(gen_images, [10, 10], os.path.join(args.save_dir, "imgs/sample%s.jpg" % epoch))
def main(args):
#
save_dir = os.path.join(args.save_dir, args.model_type)
img_dir = os.path.join(args.img_dir, args.model_type)
log_dir = os.path.join(args.log_dir, args.model_type)
train_dir = args.train_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(img_dir):
os.makedirs(img_dir)
mnist = utils.read_data_sets(args.train_dir)
summary_writer = tf.summary.FileWriter(log_dir)
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
if args.model_type == "vae":
model = VAE(args, sess, name="vae")
elif args.model_type == "dcvae":
model = DCVAE(args, sess, name="dcvae")
total_batch = mnist.train.num_examples // args.batch_size
for epoch in range(1, args.nb_epoch + 1):
print "Epoch %d start with learning rate %f" % (
epoch, model.learning_rate.eval(sess))
print "- " * 50
epoch_start_time = time.time()
step_start_time = epoch_start_time
for i in range(1, total_batch + 1):
x_batch, y_batch = mnist.train.next_batch(args.batch_size)
_, loss, loss_rec, loss_kl, global_step, summaries = model.train(
x_batch)
summary_writer.add_summary(summaries, global_step)
step_start_time = time.time()
if global_step % args.log_period == 0:
print "global step %d, loss %.9f, loss_rec %.9f, loss_kl %.9f, time %.2fs" \
% (global_step, loss, loss_rec, loss_kl, time.time() - step_start_time)
step_start_time = time.time()
if args.anneal and epoch >= args.anneal_start:
sess.run(model.learning_rate_decay_op)
if epoch % args.save_period == 0:
z = np.random.normal(size=[100, args.latent_dim])
if args.model_type == "vae":
gen_images = np.reshape(model.generate(z), (100, 28, 28, 1))
elif args.model_type == "dcvae":
gen_images = np.reshape(model.generate(z, 100),
(100, 28, 28, 1))
utils.save_images(gen_images, [10, 10],
os.path.join(img_dir, "sample%s.jpg" % epoch))
model.saver.save(sess, os.path.join(save_dir, "model.ckpt"))
print "Model stored...."
def main(args):
#
save_dir = os.path.join(args.save_dir, args.model_type)
img_dir = os.path.join(args.img_dir, args.model_type)
log_dir = os.path.join(args.log_dir, args.model_type)
train_dir = args.train_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(img_dir):
os.makedirs(img_dir)
mnist = utils.read_data_sets(args.train_dir)
summary_writer = tf.summary.FileWriter(log_dir)
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
model = VQVAE(args, sess, name="vqvae")
total_batch = mnist.train.num_examples // args.batch_size
for epoch in range(1, args.nb_epoch + 1):
print "Epoch %d start with learning rate %f" % (
epoch, model.learning_rate.eval(sess))
print "- " * 50
epoch_start_time = time.time()
step_start_time = epoch_start_time
for i in range(1, total_batch + 1):
global_step = sess.run(model.global_step)
x_batch, y_batch = mnist.train.next_batch(args.batch_size)
_, loss, rec_loss, vq, commit, global_step, summaries = model.train(
x_batch)
summary_writer.add_summary(summaries, global_step)
if i % args.print_step == 0:
print "epoch %d, step %d, loss %f, rec_loss %f, vq_loss %f, commit_loss %f, time %.2fs" \
% (epoch, global_step, loss, rec_loss, vq, commit, time.time()-step_start_time)
step_start_time = time.time()
if epoch % 50 == 0:
print "- " * 5
if args.anneal and epoch >= args.anneal_start:
sess.run(model.lr_decay_op)
if epoch % args.save_epoch == 0:
x_batch, y_batch = mnist.test.next_batch(100)
x_recon = model.reconstruct(x_batch)
utils.save_images(x_batch.reshape(-1, 28, 28, 1), [10, 10],
os.path.join(img_dir, "rawImage%s.jpg" % epoch))
utils.save_images(
x_recon, [10, 10],
os.path.join(img_dir, "reconstruct%s.jpg" % epoch))
model.saver.save(sess, os.path.join(save_dir, "model.ckpt"))
print "Model stored...."
def test(args):
if not op.exists(args.test_save):
os.makedirs(args.test_save)
latest_ckpt = tf.train.latest_checkpoint(args.save_dir)
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
model = DarkEnhance(args, sess, name="darkenhance")
model.saver.restore(sess, latest_ckpt)
print('start testing ...')
start_time = time.time()
evaluation(args, model)
end_time = time.time()
def main(args):
#
save_dir = args.save_dir
log_dir = args.log_dir
train_dir = args.data_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = utils.load_data(
args.data_type)
features = utils.preprocess_features(features)
support = [utils.preprocess_adj(adj)]
args.num_supports = 1
args.input_size, args.features_size = features[2][1], features[2]
args.output_size = y_train.shape[1]
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
model = GCN(args, sess, name="gcn")
summary_writer = tf.summary.FileWriter(log_dir)
for epoch in range(1, args.nb_epoch + 1):
epoch_start_time = time.time()
feed_dict = utils.construct_feed_dict(model, features, support,
y_train, train_mask)
_, train_loss, train_acc, summaries = model.train(feed_dict)
if epoch % args.summary_epoch == 0:
summary_writer.add_summary(summaries, epoch)
if epoch % args.print_epoch == 0:
feed_dict_val = utils.construct_feed_dict(model, features, support,
y_val, val_mask)
val_loss, val_acc = model.evaluate(feed_dict_val)
print "epoch %d, train_loss %f, train_acc %f, val_loss %f, val_acc %f, time %.5fs" % \
(epoch, train_loss, train_acc, val_loss, val_acc, time.time()-epoch_start_time)
if args.anneal and epoch >= args.anneal_start:
sess.run(model.lr_decay_op)
model.saver.save(sess, os.path.join(save_dir, "model.ckpt"))
print "Model stored...."
def main(args):
save_dir = os.path.join(args.save_dir)
log_dir = os.path.join(args.log_dir)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
summary_writer = tf.summary.FileWriter(log_dir)
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
models = get_multi_gpu_models(args, sess)
# trainer = MultiGPU(args, models, sess)
model = models[0]
saver=tf.train.Saver(max_to_keep=1)
train_loss = model.total_loss
train_step = tf.train.AdamOptimizer(args.learning_rate).minimize(train_loss)
sess.run(tf.global_variables_initializer())
if args.use_pretrained:
models = restore_models(args, sess, models)
model = models[0]
for step in range(1, args.train_steps + 1):
step_start_time = time.time()
train_images, train_labels = utils.read_to_batch(sess, filename='3D-Block-2016-05.npy',
batch_size=args.batch_size * args.num_gpu)
images_batch,labels_batch = sess.run([train_images,train_labels])
# print(type(train_images),type(train_labels))
feed_dict = model.build_feed_dict(images_batch, labels_batch, True)
_,loss_value = sess.run([train_step, train_loss],feed_dict=feed_dict)
# _, loss,summaries = trainer.train(sess, images_batch, labels_batch)
# _, loss, accuracy, summaries = trainer.train(sess, train_images, train_labels)
summaries = sess.run(model.summary,feed_dict=feed_dict)
summary_writer.add_summary(summaries, step)
if step % args.log_step == 0:
print ("step %d, loss %.5f, time %.2fs" % (step, loss_value, time.time() - step_start_time))
# if step % args.eval_step == 0:
# val_accuracy, test_time = valid(args, 'list/testlist.txt', trainer, sess)
# print ("test accuracy: %.5f, test time: %.5f" % (val_accuracy, test_time))
saver.save(sess,'./save/StepNetwork.ckpt', global_step=args.train_steps)
def main(args):
utils.print_out("Sequence-to-Sequence dialogue")
utils.print_out("- " * 50)
queries, answers, index2word, word2index = prepare_quries_answers(args)
batch_data = utils.get_batches(queries, answers, args.batch_size)
config_proto = utils.get_config_proto()
with tf.device("/gpu:0"):
if not os.path.exists("../saves"):
os.mkdir("../saves")
save_dir = args.save_dir
train_model = helper.build_train_model(args)
eval_model = helper.build_eval_model(args)
infer_model = helper.build_infer_model(args)
train_sess = tf.Session(config=config_proto, graph=train_model.graph)
eval_sess = tf.Session(config=config_proto, graph=eval_model.graph)
infer_sess = tf.Session(config=config_proto, graph=infer_model.graph)
with train_model.graph.as_default():
new_train_model, global_step = helper.create_or_load_model(
train_model.model, args.save_dir, train_sess, name="train")
for epoch in range(1, args.nb_epoch + 1):
utils.print_out("Epoch: %d start" % epoch)
utils.print_out("- " * 50)
loss = 0.0
start_train_time = time.time()
for idx, batch in enumerate(batch_data):
queries, qry_lens, answers, ans_lens = batch
loss_t = new_train_model.train(queries, qry_lens, answers, ans_lens, train_sess)
if idx % 100 == 0:
print "Epoch: ", '%01d' % epoch, "Batch: ", '%04d' % idx, "Loss: ", '%9.9f' % loss_t
#saver.save(session, save_dir, global_step=new_train_model.global_step)
infer_test(infer_model, infer_sess, args, queries, index2word)
def main(args):
save_dir = os.path.join(args.save_dir)
log_dir = os.path.join(args.log_dir)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
summary_writer = tf.summary.FileWriter(log_dir)
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
models = get_multi_gpu_models(args, sess)
trainer = MultiGPU(args, models)
sess.run(tf.global_variables_initializer())
if args.use_pretrained:
models = restore_models(args, sess, models)
for step in range(1, args.nb_steps + 1):
step_start_time = time.time()
train_images, train_labels, _, _, _ = utils.read_clip_and_label(
filename='list/trainlist.txt',
batch_size=args.batch_size * args.num_gpu,
num_frames_per_clip=args.frame_size,
crop_size=args.img_h,
shuffle=True)
_, loss, accuracy, summaries = trainer.train(sess, train_images,
train_labels)
summary_writer.add_summary(summaries, step)
if step % args.log_step == 0:
print("step %d, loss %.5f, accuracy %.5f, time %.2fs" %
(step, loss, accuracy, time.time() - step_start_time))
if step % args.eval_step == 0:
val_accuracy, test_time = valid(args, 'list/testlist.txt', trainer,
sess)
print("test accuracy: %.5f, test time: %.5f" %
(val_accuracy, test_time))
def main(args):
#
save_dir = os.path.join(args.save_dir, args.model_type)
img_dir = os.path.join(args.img_dir, args.model_type)
log_dir = os.path.join(args.log_dir, args.model_type)
train_dir = args.train_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(img_dir):
os.makedirs(img_dir)
summary_writer = tf.summary.FileWriter(log_dir)
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
model = VQVAE(args, sess, name="vqvae")
img_paths = glob.glob('data/img_align_celeba/*.jpg')
train_paths, test_paths = train_test_split(img_paths,
test_size=0.1,
random_state=args.random_seed)
celeba = utils.DiskImageData(sess,
train_paths,
args.batch_size,
shape=[218, 178, 3])
total_batch = celeba.num_examples // args.batch_size
for epoch in range(1, args.nb_epoch + 1):
print "Epoch %d start with learning rate %f" % (
epoch, model.learning_rate.eval(sess))
print "- " * 50
epoch_start_time = time.time()
step_start_time = epoch_start_time
for i in range(1, total_batch + 1):
global_step = sess.run(model.global_step)
x_batch = celeba.next_batch()
_, loss, rec_loss, vq, commit, global_step, summaries = model.train(
x_batch)
summary_writer.add_summary(summaries, global_step)
if i % args.print_step == 0:
print "epoch %d, step %d, loss %f, rec_loss %f, vq_loss %f, commit_loss %f, time %.2fs" \
% (epoch, global_step, loss, rec_loss, vq, commit, time.time()-step_start_time)
step_start_time = time.time()
if args.anneal and epoch >= args.anneal_start:
sess.run(model.lr_decay_op)
if epoch % args.save_epoch == 0:
x_batch = celeba.next_batch()
x_recon = model.reconstruct(x_batch)
utils.save_images(x_batch, [10, 10],
os.path.join(img_dir, "rawImage%s.jpg" % epoch))
utils.save_images(
x_recon, [10, 10],
os.path.join(img_dir, "reconstruct%s.jpg" % epoch))
model.saver.save(sess, os.path.join(save_dir, "model.ckpt"))
print "Model stored...."
def train(hparams, scope=None, target_session=""):
"""Train a translation model."""
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
# Create model
model_creator = get_model_creator(hparams)
train_model = model_helper.create_train_model(model_creator, hparams,
scope)
eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
dev_lbl_file = "%s.%s" % (hparams.dev_prefix, hparams.lbl)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
sample_lbl_data = inference.load_data(dev_lbl_file)
summary_name = "train_log"
model_dir = hparams.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("\n# log_file=%s" % log_file, log_f)
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement)
train_sess = tf.Session(target=target_session,
config=config_proto,
graph=train_model.graph)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
# First evaluation
run_full_eval(model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data,
sample_lbl_data)
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
stats, info, start_train_time = before_train(loaded_train_model,
train_model, train_sess,
global_step, hparams, log_f)
while global_step < num_train_steps:
### Run a step ###
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
hparams.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
hparams.epoch_step = 0
utils.print_out("# Finished an epoch, step %d." % global_step)
#run_sample_decode(infer_model, infer_sess, model_dir, hparams,
# summary_writer, sample_src_data, sample_tgt_data)
#run_external_eval(infer_model, infer_sess, model_dir, hparams,
# summary_writer)
train_sess.run(train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Process step_result, accumulate stats, and write summary
global_step, info["learning_rate"], step_summary = update_stats(
hparams, stats, start_time, step_result)
summary_writer.add_summary(step_summary, global_step)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
is_overflow = process_stats(hparams, stats, info, global_step,
steps_per_stats, log_f)
print_step_info(" ", global_step, info, get_best_results(hparams),
log_f)
if is_overflow:
break
# Reset statistics
stats = init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.print_out("# Save eval, global step %d" % global_step)
add_info_summaries(summary_writer, global_step, info)
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data,
sample_lbl_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
(result_summary, _, final_eval_metrics) = (run_full_eval(
model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
summary_writer, sample_src_data, sample_tgt_data, sample_lbl_data))
print_step_info("# Final, ", global_step, info, result_summary, log_f)
utils.print_time("# Done training!", start_train_time)
summary_writer.close()
utils.print_out("# Start evaluating saved best models.")
for metric in hparams.metrics:
best_model_dir = getattr(hparams, "best_" + metric + "_dir")
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data,
sample_lbl_data)
print_step_info("# Best %s, " % metric, best_global_step, info,
result_summary, log_f)
summary_writer.close()
return final_eval_metrics, global_step
def main(args):
# main
save_dir = args.save_dir
train_data = utils.load_data(args.train_dir)
eval_data = utils.load_data(args.eval_dir)
infer_data = utils.load_data(args.infer_dir)
index2word, word2index = utils.load_vocab_from_file(args.vocab_dir)
train_data_vec = utils.vectorize(train_data, word2index)
eval_data_vec = utils.vectorize(eval_data, word2index)
infer_data_vec = utils.vectorize(infer_data, word2index)
train_data_size = len(train_data_vec)
train_model = model_helper.build_train_model(args, use_attention=False)
eval_model = model_helper.build_eval_model(args, use_attention=False)
infer_model = model_helper.build_infer_model(args, use_attention=False)
config_proto = utils.get_config_proto()
train_sess = tf.Session(config=config_proto, graph=train_model.graph)
eval_sess = tf.Session(config=config_proto, graph=eval_model.graph)
infer_sess = tf.Session(config=config_proto, graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, args.save_dir, train_sess, name="train")
for epoch in range(1, args.max_epoch + 1):
print "Epoch %d start with learning rate %f" % \
(epoch, loaded_train_model.learning_rate.eval(train_sess))
print "- " * 50
epoch_start_time = time.time()
all_batch = utils.get_batches(train_data_vec, args.batch_size)
step_start_time = epoch_start_time
for idx, batch in enumerate(all_batch):
train_loss, train_ppl, global_step, predict_count, batch_size = loaded_train_model.train(
train_sess, batch.user, batch.product, batch.rating,
batch.review_input, batch.review_output, batch.review_length)
if global_step % args.print_step == 0:
print "global step: %d, loss: %.9f, ppl: %.2f, time %.2fs" % \
(global_step, train_loss, train_ppl, time.time() - step_start_time)
step_start_time = time.time()
if global_step % args.eval_step == 0:
loaded_train_model.saver.save(train_sess,
os.path.join(
args.save_dir,
"gen_review.ckpt"),
global_step=global_step)
eval_start_time = time.time()
eval_avg_loss, eval_ppl = run_internal_eval(
args, eval_model, eval_sess, args.save_dir, eval_data_vec)
print "eval loss: %f, eval ppl: %.2f after training of step %d, time %.2fs" % \
(eval_avg_loss, eval_ppl, global_step, time.time() - eval_start_time)
run_sample_decode(args, infer_model, infer_sess, args.save_dir,
infer_data_vec)
step_start_time = time.time()
if args.anneal and global_step > (train_data_size /
batch_size) * args.anneal_start:
train_sess.run(train_model.learning_rate_decay_op)
print "one epoch finish, time %.2fs" % (time.time() - epoch_start_time)
def train(hparams):
num_epochs = hparams.num_epochs
num_ckpt_epochs = hparams.num_ckpt_epochs
summary_name = "train_log"
out_dir = hparams.out_dir
model_dir = out_dir
log_device_placement = hparams.log_device_placement
input_emb_weights = np.loadtxt(
hparams.input_emb_file,
delimiter=' ') if hparams.input_emb_file else None
if hparams.model_architecture == "rnn-model": model_creator = model.RNN
else:
raise ValueError(
"Unknown model architecture. Only simple_rnn is supported so far.")
#create 2 models in 2 graphs for train and evaluation, with 2 sessions sharing the same variables.
train_model = model_helper.create_train_model(
model_creator,
hparams,
hparams.train_input_path,
hparams.train_target_path,
mode=tf.contrib.learn.ModeKeys.TRAIN)
eval_model = model_helper.create_eval_model(model_creator, hparams,
tf.contrib.learn.ModeKeys.EVAL)
# some configuration of gpus logging
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement, allow_soft_placement=True)
# create two separate sessions for trai/eval
train_sess = tf.Session(config=config_proto, graph=train_model.graph)
eval_sess = tf.Session(config=config_proto, graph=eval_model.graph)
# create a new train model by initializing all variables of the train graph in the train_sess.
# or, using the latest checkpoint in the model_dir, load all variables of the train graph in the train_sess.
# Note that at this point, the eval graph variables are not initialized.
with train_model.graph.as_default():
loaded_train_model = model_helper.create_or_load_model(
train_model.model, train_sess, "train", model_dir,
input_emb_weights)
# create a log file with name summary_name in out_dir. The file is written asynchronously during the training process.
# We also passed the train graph in order to be able to display it in Tensorboard
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
#run first evaluation before starting training
dev_loss = run_evaluation(eval_model, eval_sess, model_dir,
hparams.val_input_path, hparams.val_target_path,
input_emb_weights, summary_writer)
train_loss = run_evaluation(eval_model, eval_sess, model_dir,
hparams.train_input_path,
hparams.train_target_path, input_emb_weights,
summary_writer)
print("Dev loss before training: %.3f" % dev_loss)
print("Train loss before training: %.3f" % train_loss)
# Start training
start_train_time = time.time()
epoch_time = 0.0
batch_loss, epoch_loss = 0.0, 0.0
batch_count = 0.0
#initialize train iterator in train_sess
train_sess.run(train_model.iterator.initializer)
#keep lists of train/val losses for all epochs
train_losses = []
dev_losses = []
#train the model for num_epochs. One epoch means a pass through the whole train dataset, i.e., through all the batches.
for epoch in range(num_epochs):
#go through all batches for the current epoch
while True:
start_batch_time = 0.0
try:
# this call will run operations of train graph in train_sess
step_result = loaded_train_model.train(train_sess)
(_, batch_loss, batch_summary, global_step, learning_rate,
batch_size, inputs, targets) = step_result
epoch_time += (time.time() - start_batch_time)
epoch_loss += batch_loss
batch_count += 1
except tf.errors.OutOfRangeError:
#when the iterator of the train batches reaches the end, break the loop
#and reinitialize the iterator to start from the beginning of the train data.
train_sess.run(train_model.iterator.initializer)
break
# average epoch loss and epoch time over batches
epoch_loss /= batch_count
epoch_time /= batch_count
batch_count = 0.0
#print results if the current epoch is a print results epoch
if (epoch + 1) % num_ckpt_epochs == 0:
print("Saving checkpoint...")
model_helper.add_summary(summary_writer, "train_loss", epoch_loss)
# save checkpoint. We save the values of the variables of the train graph.
# train_sess is the session in which the train graph was launched.
# global_step parameter is optional and is appended to the name of the checkpoint.
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "rnn.ckpt"),
global_step=epoch)
print("Results: ")
dev_loss = run_evaluation(eval_model, eval_sess, model_dir,
hparams.val_input_path,
hparams.val_target_path,
input_emb_weights, summary_writer)
# tr_loss = run_evaluation(eval_model, eval_sess, model_dir, hparams.train_input_path, hparams.train_target_path, input_emb_weights, summary_writer)
# print("check %.3f:"%tr_loss)
print(" epoch %d lr %g "
"train_loss %.3f, dev_loss %.3f" %
(epoch,
loaded_train_model.learning_rate.eval(session=train_sess),
epoch_loss, dev_loss))
train_losses.append(epoch_loss)
dev_losses.append(dev_loss)
# save final model
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "rnn.ckpt"),
global_step=num_epochs)
print("Done training in %.2fK" % (time.time() - start_train_time))
min_dev_loss = np.min(dev_losses)
min_dev_idx = np.argmin(dev_losses)
print("Min val loss: %.3f at epoch %d" % (min_dev_loss, min_dev_idx))
summary_writer.close()
def main(args):
print "loadding reviews and labels from dataset"
data = pd.read_csv('data/labeledTrainData.tsv.zip',
compression='zip',
delimiter='\t',
header=0,
quoting=3)
reviews = data["review"]
labels = list(data['sentiment'])
sentences = []
for review in reviews:
if len(review) > 0:
sentences.append(
utils.review_to_wordlist(review.decode('utf8').strip(),
remove_stopwords=True))
print "loaded %d reviews from dataset" % len(sentences)
word_dict = utils.build_vocab(sentences, max_words=10000)
vec_reviews = utils.vectorize(sentences, word_dict, verbose=True)
train_x = vec_reviews[0:20000]
train_y = labels[0:20000]
train_y = utils.one_hot(train_y, args.nb_classes)
test_x = vec_reviews[20000:]
test_y = labels[20000:]
test_y = utils.one_hot(test_y, args.nb_classes)
save_dir = args.save_dir
log_dir = args.log_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
with tf.Graph().as_default():
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
if args.model_type == "cnn":
model = TextCNN(args, "TextCNN")
test_batch = utils.get_batches(test_x, test_y, args.max_size)
elif args.model_type in ["rnn", "bi_rnn"]:
model = TextRNN(args, "TextRNN")
test_batch = utils.get_batches(test_x,
test_y,
args.max_size,
type="rnn")
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
for epoch in range(1, args.nb_epochs + 1):
print "epoch %d start" % epoch
print "- " * 50
loss = 0.
total_reviews = 0
accuracy = 0.
if args.model_type == "cnn":
train_batch = utils.get_batches(train_x, train_y,
args.batch_size)
elif args.model_type in ["rnn", "bi_rnn"]:
train_batch = utils.get_batches(train_x,
train_y,
args.batch_size,
type="rnn")
epoch_start_time = time.time()
step_start_time = epoch_start_time
for idx, batch in enumerate(train_batch):
reviews, reviews_length, labels = batch
_, loss_t, accuracy_t, global_step, batch_size, summaries = model.train(
sess, reviews, reviews_length, labels, args.keep_prob)
loss += loss_t * batch_size
total_reviews += batch_size
accuracy += accuracy_t * batch_size
summary_writer.add_summary(summaries, global_step)
if global_step % 50 == 0:
print "epoch %d, step %d, loss %f, accuracy %.4f, time %.2fs" % \
(epoch, global_step, loss_t, accuracy_t, time.time() - step_start_time)
step_start_time = time.time()
epoch_time = time.time() - epoch_start_time
print "%.2f seconds in this epoch" % (epoch_time)
print "train loss %f, train accuracy %.4f" % (
loss / total_reviews, accuracy / total_reviews)
total_reviews = 0
accuracy = 0.
for batch in test_batch:
reviews, reviews_length, labels = batch
accuracy_t, batch_size = model.test(sess, reviews,
reviews_length, labels,
1.0)
total_reviews += batch_size
accuracy += accuracy_t * batch_size
print "accuracy %.4f in %d test reviews" % (
accuracy / total_reviews, total_reviews)
def main(args):
print "loadding data and labels from dataset"
train = pd.read_csv(args.train_dir)
ch_train = pd.read_csv(args.chtrain_dir)
x_train = train["comment_text"]
x_chtrain = ch_train["comment_text"]
target_cols = [
'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'
]
x = []
x_ch = []
for line in x_train:
if len(line) > 0:
x.append(utils.review_to_wordlist(line.strip()))
print "loaded %d comments from dataset" % len(x)
for line in x_chtrain:
if len(line) > 0:
x_ch.append(utils.review_to_wordlist_char(line.strip()))
print "loaded %d comments from dataset" % len(x)
y = train[target_cols].values
index2word, word2index = utils.load_vocab(args.vocab_dir)
index2char, char2index = utils.load_char(args.char_dir)
x_vector = utils.vectorize(x, word2index, verbose=False)
x_vector = np.array(x_vector)
char_vector = utils.vectorize_char(x_ch, char2index, verbose=False)
char_vector = np.array(char_vector)
print char_vector[0]
save_dir = os.path.join(args.save_dir, args.model_type)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if args.model_type in ["cnn", "cnnfe", "chcnn", "chcnn2"]:
max_step = args.max_step_cnn
max_size = args.max_size_cnn
nb_epochs = args.nb_epochs_cnn
elif args.model_type in [
"rnn", "rnnfe", "rnnfe2", "chrnn", "chrnnfe", "rcnn"
]:
max_step = args.max_step_rnn
max_size = args.max_size_rnn
nb_epochs = args.nb_epochs_rnn
ex_features = add_features("../data/train.csv")
nfolds = args.nfolds
skf = KFold(n_splits=nfolds, shuffle=True, random_state=2018)
test_prob = []
stack_logits = np.zeros((len(x_vector), len(target_cols)))
for (f, (train_index, test_index)) in enumerate(skf.split(x_vector)):
x_train, x_eval = x_vector[train_index], x_vector[test_index]
char_train, char_eval = char_vector[train_index], char_vector[
test_index]
y_train, y_eval = y[train_index], y[test_index]
with tf.Graph().as_default():
config_proto = utils.get_config_proto()
sess = tf.Session(config=config_proto)
if args.model_type == "cnn":
model = TextCNN(args, "TextCNN")
elif args.model_type == "cnnfe":
model = TextCNNFE(args, "TextCNNFE")
elif args.model_type == "rnn":
model = TextRNN(args, "TextRNN")
elif args.model_type == "rnnfe":
model = TextRNNFE(args, "TextRNNFE")
elif args.model_type == "rcnn":
model = TextRCNN(args, "TextRCNN")
elif args.model_type == "attention":
model = RNNWithAttention(args, "Attention")
elif args.model_type == "chrnn":
model = TextRNNChar(args, "TextRNNChar")
elif args.model_type == "chcnn":
model = TextCNNChar(args, "TextCNNChar")
elif args.model_type == "chcnn2":
model = TextCNNChar(args, "TextCNNChar2")
elif args.model_type == "rnnfe2":
model = TextRNNFE2(args, "TextCNNCharFE2")
elif args.model_type == "chrnnfe":
model = TextRNNCharFE(args, "TextCNNCharFE")
else:
raise ValueError("Unknown model_type %s" % args.model_type)
sess.run(tf.global_variables_initializer())
if args.use_ft:
pretrain_dir = args.ft_dir
print "use FastText word vector"
embedding = utils.load_fasttext(pretrain_dir, index2word)
if not args.use_ft:
pretrain_dir = args.glove_dir
print "use Glove word vector"
embedding = utils.load_glove(pretrain_dir, index2word)
sess.run(model.embedding_init,
{model.embedding_placeholder: embedding})
for line in model.tvars:
print line
print "training %s model for toxic comments classification" % (
args.model_type)
print "%d fold start training" % f
for epoch in range(1, nb_epochs + 1):
print "epoch %d start with lr %f" % (
epoch,
model.learning_rate.eval(session=sess)), "\n", "- " * 50
loss, total_comments = 0.0, 0
if args.model_type in ["cnn", "rnn", "rcnn"]:
train_batch = utils.get_batches(x_train, y_train,
args.batch_size,
args.max_len)
valid_batch = utils.get_batches(x_eval, y_eval, max_size,
args.max_len, False)
elif args.model_type in ["chrnn", "chcnn", "chcnn2"]:
train_batch = utils.get_batches_with_char(
x_train, char_train, y_train, args.batch_size,
args.max_len)
valid_batch = utils.get_batches_with_char(
x_eval, char_eval, y_eval, max_size, args.max_len,
False)
elif args.model_type in ["rnnfe", "cnnfe", "rnnfe2"]:
train_batch = utils.get_batches_with_fe(
x_train, y_train, ex_features, args.batch_size,
args.max_len)
valid_batch = utils.get_batches_with_fe(
x_eval, y_eval, ex_features, max_size, args.max_len,
False)
elif args.model_type in ["chrnnfe"]:
train_batch = utils.get_batches_with_charfe(
x_train, char_train, y_train, ex_features,
args.batch_size, args.max_len)
valid_batch = utils.get_batches_with_charfe(
x_eval, char_eval, y_eval, ex_features, max_size,
args.max_len, False)
epoch_start_time = time.time()
step_start_time = epoch_start_time
for idx, batch in enumerate(train_batch):
if args.model_type in ["cnn", "rnn", "rcnn"]:
comments, comments_length, labels = batch
_, loss_t, global_step, batch_size = model.train(
sess, comments, comments_length, labels)
elif args.model_type in ["chrnn", "chcnn", "chcnn2"]:
comments, comments_length, chs, labels = batch
_, loss_t, global_step, batch_size = model.train(
sess, comments, comments_length, chs, labels)
elif args.model_type in ["rnnfe", "cnnfe", "rnnfe2"]:
comments, comments_length, exs, labels = batch
_, loss_t, global_step, batch_size = model.train(
sess, comments, comments_length, labels, exs)
elif args.model_type in ["chrnnfe"]:
comments, comments_length, chs, exs, labels = batch
_, loss_t, global_step, batch_size = model.train(
sess, comments, comments_length, chs, labels, exs)
loss += loss_t * batch_size
total_comments += batch_size
if global_step % 200 == 0:
print "epoch %d step %d loss %f time %.2fs" % (
epoch, global_step, loss_t,
time.time() - step_start_time)
if global_step % 200 == 0:
_ = run_valid(valid_batch, model, sess,
args.model_type)
# model.saver.save(sess, os.path.join(save_dir, "model.ckpt"), global_step=global_step)
step_start_time = time.time()
epoch_time = time.time() - epoch_start_time
sess.run(model.learning_rate_decay_op)
print "%.2f seconds in this epoch with train loss %f" % (
epoch_time, loss / total_comments)
test_prob.append(run_test(args, model, sess))
stack_logits[test_index] = run_valid(valid_batch, model, sess,
args.model_type)
preds = np.zeros((test_prob[0].shape[0], len(target_cols)))
for prob in test_prob:
preds += prob
print prob[0]
preds /= len(test_prob)
print len(test_prob)
write_predict(stack_logits, args.model_type)
write_results(preds, args.model_type)
