def main(unused_args):
config = get_config(FLAGS.model_size)
eval_config = get_config(FLAGS.model_size)
saved_model_path = FLAGS.model_path
weights_dir = FLAGS.weights_dir
verbose = FLAGS.verbose
debug = FLAGS.debug
if weights_dir is not None:
if not os.path.exists(weights_dir):
os.mkdir(weights_dir)
if not debug:
raw_data = reader.ptb_raw_data(FLAGS.data_path, "ptb.train.txt", "ptb.valid.txt", "ptb.test.txt")
else:
raw_data = reader.ptb_raw_data(FLAGS.data_path, "emma.txt", "emma.val.txt", "emma.test.txt")
# load up PTB data
train_data, val_data, test_data, vocab, word_to_id = raw_data
with tf.Graph().as_default(), tf.Session() as session:
initialiser = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.variable_scope('model', reuse=None, initializer=initialiser):
m = ACTModel(config,is_training=True)
# if we have a saved/pre-trained model, load it.
if saved_model_path is not None:
saveload.main(saved_model_path, session)
with tf.variable_scope("model", reuse=True):
m_val = ACTModel(config, is_training=False)
m_test = ACTModel(eval_config,is_training=False)
tf.initialize_all_variables().run()
print("starting training")
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
session.run(tf.assign(m.lr, config.learning_rate * lr_decay))
train_loss = run_epoch(session, m, train_data, m.train_op, verbose=True)
valid_loss = run_epoch(session, m_val, val_data, tf.no_op())
if verbose:
print("Epoch: {} Learning rate: {}".format(i + 1, session.run(m.lr)))
print("Epoch: {} Train Loss: {}".format(i + 1, train_loss))
print("Epoch: %d Valid Loss: %.3f" % (i + 1, valid_loss))
# save weights in a pickled dictionary format
if weights_dir is not None:
date = "{:%m.%d.%H.%M}".format(datetime.now())
saveload.main(weights_dir + "/Epoch_{:02}Train_{:0.3f}Val_{:0.3f}date{}.pkl"
.format(i+1,train_loss,valid_loss, date), session)
test_loss = run_epoch(session, m_test, test_data, tf.no_op())
if verbose:
print("Test Perplexity: %.3f" % test_loss)
def main(unused_args):
config = get_config(FLAGS.model_size)
eval_config = get_config(FLAGS.model_size)
saved_model_path = FLAGS.model_path
weights_dir = FLAGS.weights_dir
verbose = FLAGS.verbose
debug = FLAGS.debug
if weights_dir is not None:
if not os.path.exists(weights_dir):
os.mkdir(weights_dir)
raw_data = ptb_raw_data(FLAGS.data_path, "ptb.train.txt", "ptb.valid.txt", "ptb.test.txt")
train_data, val_data, test_data, vocab, word_to_id = raw_data
with tf.Graph().as_default(), tf.Session() as session:
initialiser = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.variable_scope('model', reuse=None, initializer=initialiser):
m = ACTModel(config, is_training=True)
# If we have a saved/pre-trained model, load it.
if saved_model_path is not None:
saveload.main(saved_model_path, session)
with tf.variable_scope("model", reuse=True):
m_val = ACTModel(config, is_training=False)
m_test = ACTModel(eval_config, is_training=False)
tf.global_variables_initializer().run()
print("starting training")
max_steps = 100 if debug else None
val_max_steps = 10 if debug else None
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
session.run(tf.assign(m.lr, config.learning_rate * lr_decay))
train_loss = run_epoch(session, m, train_data, m.train_op, max_steps=max_steps,verbose=True)
valid_loss = run_epoch(session, m_val, val_data, tf.no_op(), max_steps=val_max_steps)
if verbose:
print("Epoch: {} Learning rate: {}".format(i + 1, session.run(m.lr)))
print("Epoch: {} Train Loss: {}".format(i + 1, train_loss))
print("Epoch: %d Valid Loss: %.3f" % (i + 1, valid_loss))
if weights_dir is not None:
date = "{:%m.%d.%H.%M}".format(datetime.now())
saveload.main(weights_dir + "/Epoch_{:02}Train_{:0.3f}Val_{:0.3f}date{}.pkl"
.format(i+1, train_loss, valid_loss, date), session)
test_loss = run_epoch(session, m_test, test_data, tf.no_op())
if verbose:
print("Test Perplexity: %.3f" % test_loss)
def main(unused_args):
saved_model_path = args.model_path
vocab_path = args.vocab_path
weights_dir = args.weights_dir
verbose = args.verbose
debug = args.debug
embeddings = args.embedding_path
MODEL, config = get_config_and_model(args.model)
_, eval_config = get_config_and_model(args.model)
if weights_dir is not None:
if not os.path.exists(weights_dir):
os.mkdir(weights_dir)
vocab = Vocab(vocab_path, args.data_path,max_vocab_size=40000)
config.vocab_size = vocab.size()
eval_config.vocab_size = vocab.size()
train, val, test = "snli_1.0_train.jsonl","snli_1.0_dev.jsonl","snli_1.0_test.jsonl"
# Dataset Stats(we ignore bad labels)
# train val test
# max hypothesis: 82 55 30
# max premise: 62 59 57
# bad labels 785 158 176
if debug:
buckets = [(15,10)]
raw_data = snli_reader.load_data(args.data_path,train, val, test, vocab, False,
max_records=100,buckets=buckets, batch_size=config.batch_size)
else:
buckets = [(10,5),(20,10),(30,20),(40,30),(50,40),(82,62)]
raw_data = snli_reader.load_data(args.data_path,train, val, test, vocab, False,
max_records=None, buckets=buckets, batch_size=config.batch_size)
train_data, val_data, test_data, stats = raw_data
print(stats)
test_buckets = {x:v for x,v in enumerate(test_data)}
if config.use_embeddings:
print("loading embeddings from {}".format(embeddings))
vocab_dict = import_embeddings(embeddings)
config.embedding_size = len(vocab_dict["the"])
eval_config.embedding_size = config.embedding_size
embedding_var = np.random.normal(0.0, config.init_scale, [config.vocab_size, config.embedding_size])
no_embeddings = 0
for word in vocab.token_id.keys():
try:
embedding_var[vocab.token_id[word],:] = vocab_dict[word]
except KeyError:
no_embeddings +=1
continue
print("num embeddings with no value:{}".format(no_embeddings))
else:
embedding_var = None
print("loading models into memory")
trainingStats = defaultdict(list)
trainingStats["config"].append(config)
trainingStats["eval_config"].append(eval_config)
with tf.Graph().as_default(), tf.Session() as session:
initialiser = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
# generate a model per bucket which share parameters
# store in list where index corresponds to the id of the batch
with tf.variable_scope("all_models_with_buckets"):
models_test = []
for i in range(len(test_buckets)):
# correct config for this bucket
config.prem_steps, config.hyp_steps = buckets[i]
eval_config.prem_steps, eval_config.hyp_steps = buckets[i]
with tf.variable_scope('model', reuse= True if i > 0 else None, initializer=initialiser):
#### Reload Model ####
if saved_model_path is not None:
saveload.main(saved_model_path, session)
models_test.append(MODEL(eval_config, pretrained_embeddings=embedding_var,
update_embeddings=config.train_embeddings, is_training=False))
test_loss, test_acc = run_epoch(session, models_test, test_buckets, training=False)
date = "{:%m.%d.%H.%M}".format(datetime.now())
trainingStats["test_loss"].append(test_loss)
trainingStats["test_acc"].append(test_acc)
saveload.main(weights_dir + "/FinalTestAcc_{:0.5f}date{}.pkl"
.format(test_acc, date), session)
pickle.dump(trainingStats,open(os.path.join(weights_dir, "stats.pkl"), "wb"))
if verbose:
print("Test Accuracy: {}".format(test_acc))
def main(unused_args):
saved_model_path = args.model_path
vocab_path = args.vocab_path
weights_dir = args.weights_dir
verbose = args.verbose
debug = args.debug
embeddings = args.embedding_path
multi_thread = args.multi_thread
MODEL, config = get_config_and_model(args.model)
_, eval_config = get_config_and_model(args.model)
if weights_dir is not None:
if not os.path.exists(weights_dir):
os.mkdir(weights_dir)
vocab = Vocab(vocab_path, args.data_path, max_vocab_size=40000)
config.vocab_size = vocab.size()
eval_config.vocab_size = vocab.size()
if args.grid_search:
config.hidden_size = int(args.hidden_size)
config.learning_rate = float(args.learning_rate)
config.eps = float(args.eps)
config.step_penalty = float(args.step_penalty)
config.keep_prob = float(args.keep_prob)
eval_config.hidden_size = int(args.hidden_size)
eval_config.learning_rate = float(args.learning_rate)
eval_config.eps = float(args.eps)
eval_config.step_penalty = float(args.step_penalty)
eval_config.keep_prob = float(args.keep_prob)
train, val, test = "snli_1.0_train.jsonl", "snli_1.0_dev.jsonl", "snli_1.0_test.jsonl"
# Dataset Stats(we ignore bad labels)
# train val test
# max hypothesis: 82 55 30
# max premise: 62 59 57
# bad labels 785 158 176
if debug:
buckets = [(15, 10)]
raw_data = snli_reader.load_data(args.data_path,
train,
val,
test,
vocab,
False,
max_records=100,
buckets=buckets,
batch_size=config.batch_size)
else:
buckets = [(10, 5), (20, 10), (30, 20), (40, 30), (50, 40), (82, 62)]
raw_data = snli_reader.load_data(args.data_path,
train,
val,
test,
vocab,
False,
max_records=None,
buckets=buckets,
batch_size=config.batch_size)
train_data, val_data, test_data, stats = raw_data
print(stats)
# data = list(bucketed data)
# bucketed data = list(tuple(dict(hypothesis,premise), target))
# where the sentences will be of length buckets[bucket_id]
# bucket_id : data
train_buckets = {x: v for x, v in enumerate(train_data)}
val_buckets = {x: v for x, v in enumerate(val_data)}
test_buckets = {x: v for x, v in enumerate(test_data)}
if config.use_embeddings:
print("loading embeddings from {}".format(embeddings))
vocab_dict = import_embeddings(embeddings)
config.embedding_size = len(vocab_dict["the"])
eval_config.embedding_size = config.embedding_size
embedding_var = np.random.normal(
0.0, config.init_scale, [config.vocab_size, config.embedding_size])
no_embeddings = 0
for word in vocab.token_id.keys():
try:
embedding_var[vocab.token_id[word], :] = vocab_dict[word]
except KeyError:
no_embeddings += 1
continue
print("num embeddings with no value:{}".format(no_embeddings))
else:
embedding_var = None
print("loading models into memory")
trainingStats = defaultdict(list)
trainingStats["config"].append(config)
trainingStats["eval_config"].append(eval_config)
with tf.Graph().as_default(), tf.Session() as session:
initialiser = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
# generate a model per bucket which share parameters
# store in list where index corresponds to the id of the batch
with tf.variable_scope("all_models_with_buckets"):
models, models_val, models_test = [], [], []
for i in range(len(train_buckets)):
# correct config for this bucket
config.prem_steps, config.hyp_steps = buckets[i]
eval_config.prem_steps, eval_config.hyp_steps = buckets[i]
with tf.variable_scope('model',
reuse=True if i > 0 else None,
initializer=initialiser):
models.append(
MODEL(config,
pretrained_embeddings=embedding_var,
update_embeddings=config.train_embeddings,
is_training=True))
with tf.variable_scope('model', reuse=True):
models_val.append(
MODEL(config,
pretrained_embeddings=embedding_var,
update_embeddings=config.train_embeddings,
is_training=False))
models_test.append(
MODEL(eval_config,
pretrained_embeddings=embedding_var,
update_embeddings=config.train_embeddings,
is_training=False))
tf.initialize_all_variables().run()
if config.use_embeddings:
session.run(
[models[0].embedding_init],
feed_dict={models[0].embedding_placeholder: embedding_var})
del embedding_var
#### Reload Model ####
if saved_model_path is not None:
saveload.main(saved_model_path, session)
try:
trainingStats = pickle.load(
open(os.path.join(weights_dir, "stats.pkl"), "rb"))
except:
print(
"unable to rejoin original statistics - ignore if not continuing training."
)
epochs = [i for i in range(config.max_max_epoch)]
epochs = epochs[len(trainingStats["epoch"]):]
print("beginning training")
print(epochs)
with open(weights_dir + "/text_summary.txt", "a+") as summary:
summary.write(" ".join(["Epoch", "Train", "Val", "Date",
"\n"]))
#tf.get_default_graph().finalize()
for i in epochs:
#lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
#session.run(tf.assign(m[model].lr, config.learning_rate * lr_decay))
print("Epoch {}, Training data".format(i + 1))
if multi_thread:
train_acc = "N/A"
train_loss = "N/A"
async_single_epoch(multi_thread, session, models,
train_buckets)
else:
train_loss, train_acc = run_epoch(session,
models,
train_buckets,
training=True,
verbose=True)
print("Epoch {}, Validation data".format(i + 1))
valid_loss, valid_acc = run_epoch(session,
models_val,
val_buckets,
training=False)
trainingStats["train_loss"].append(train_loss)
trainingStats["train_acc"].append(train_acc)
trainingStats["val_loss"].append(valid_loss)
trainingStats["val_acc"].append(valid_acc)
trainingStats["epoch"].append(i)
# if trainingStats["val_acc"][i-1] >= trainingStats["val_acc"][i]:
# print("decaying learning rate")
# trainingStats["lr_decay"].append(i)
# current_lr = session.run(models[0].lr)
# session.run(tf.assign(models[0].lr, config.lr_decay * current_lr))
print("Epoch: {} Train Loss: {} Train Acc: {}".format(
i + 1, train_loss, train_acc))
print("Epoch: {} Valid Loss: {} Valid Acc: {}".format(
i + 1, valid_loss, valid_acc))
####### Model Hooks ########
if weights_dir is not None and (trainingStats["val_acc"][i - 1]
<= valid_acc):
date = "{:%m.%d.%H.%M}".format(datetime.now())
with open(weights_dir + "/text_summary.txt",
"a+") as summary:
summary.write(" ".join([
str(i + 1),
str(train_acc),
str(valid_acc), date, "\n"
]))
with open(weights_dir + "/weights.pkl", "wb") as file:
variables = tf.trainable_variables()
values = session.run(variables)
pickle.dump(
{
var.name: val
for var, val in zip(variables, values)
}, file)
pickle.dump(trainingStats,
open(os.path.join(weights_dir, "stats.pkl"), "wb"))
# load weights with best validation performance:
best_epoch = np.argmax(trainingStats["val_acc"])
v_dic = {v.name: v for v in tf.trainable_variables()}
for key, value in pickle.load(
open(weights_dir + "/weights.pkl", "rb")).items():
session.run(tf.assign(v_dic[key], value))
test_loss, test_acc = run_epoch(session,
models_test,
test_buckets,
training=False)
date = "{:%m.%d.%H.%M}".format(datetime.now())
trainingStats["test_loss"].append(test_loss)
trainingStats["test_acc"].append(test_acc)
file = "/BestEpoch_{}FinalTestAcc_{:0.5f}date{}.txt".format(
best_epoch, test_acc, date)
trainingStats["test_file"].append("/weights.pkl")
with open(weights_dir + file, "a+") as test_file:
test_file.write(" ".join(
[str(best_epoch),
str(test_acc),
str(date), "\n"]))
pickle.dump(trainingStats,
open(os.path.join(weights_dir, "stats.pkl"), "wb"))
if verbose:
print("Test Accuracy: {}".format(test_acc))