def __init__(self, data_dir='data/mdbt'):
Tracker.__init__(self)
# data profile
self.data_dir = data_dir
self.validation_url = os.path.join(self.data_dir, 'data/validate.json')
self.word_vectors_url = os.path.join(
self.data_dir, 'word-vectors/paragram_300_sl999.txt')
self.training_url = os.path.join(self.data_dir, 'data/train.json')
self.ontology_url = os.path.join(self.data_dir, 'data/ontology.json')
self.testing_url = os.path.join(self.data_dir, 'data/test.json')
self.model_url = os.path.join(self.data_dir, 'models/model-1')
self.graph_url = os.path.join(self.data_dir, 'graphs/graph-1')
self.results_url = os.path.join(self.data_dir, 'results/log-1.txt')
self.kb_url = os.path.join(self.data_dir, 'data/') # not used
self.train_model_url = os.path.join(self.data_dir,
'train_models/model-1')
self.train_graph_url = os.path.join(self.data_dir,
'train_graph/graph-1')
print('Configuring MDBT model...')
self.word_vectors = load_word_vectors(self.word_vectors_url)
# Load the ontology and extract the feature vectors
self.ontology, self.ontology_vectors, self.slots = load_ontology(
self.ontology_url, self.word_vectors)
# Load and process the training data
self.dialogues, self.actual_dialogues = load_woz_data(
self.testing_url, self.word_vectors, self.ontology)
self.no_dialogues = len(self.dialogues)
self.model_variables = model_definition(self.ontology_vectors,
len(self.ontology),
self.slots,
num_hidden=None,
bidir=True,
net_type=None,
test=True,
dev='cpu')
self.state = init_state()
_config = tf.ConfigProto()
_config.gpu_options.allow_growth = True
_config.allow_soft_placement = True
self.sess = tf.Session(config=_config)
self.param_restored = False
self.det_dic = {}
for domain, dic in REF_USR_DA.items():
for key, value in dic.items():
assert '-' not in key
self.det_dic[key.lower()] = key + '-' + domain
self.det_dic[value.lower()] = key + '-' + domain
self.value_dict = json.load(
open(os.path.join(self.data_dir, '../multiwoz/value_dict.json')))
def train(self):
"""
Train the model.
Model saved to
"""
num_hid, bidir, net_type, n2p, batch_size, model_url, graph_url, dev = \
None, True, None, None, None, None, None, None
global train_batch_size, MODEL_URL, GRAPH_URL, device, TRAIN_MODEL_URL, TRAIN_GRAPH_URL
if batch_size:
train_batch_size = batch_size
print("Setting up the batch size to {}.........................".
format(batch_size))
if model_url:
TRAIN_MODEL_URL = model_url
print("Setting up the model url to {}.........................".
format(TRAIN_MODEL_URL))
if graph_url:
TRAIN_GRAPH_URL = graph_url
print("Setting up the graph url to {}.........................".
format(TRAIN_GRAPH_URL))
if dev:
device = dev
print(
"Setting up the device to {}.........................".format(
device))
# 1 Load and process the input data including the ontology
# Load the word embeddings
word_vectors = load_word_vectors(self.word_vectors_url)
# Load the ontology and extract the feature vectors
ontology, ontology_vectors, slots = load_ontology(
self.ontology_url, word_vectors)
# Load and process the training data
dialogues, _ = load_woz_data(self.training_url, word_vectors, ontology)
no_dialogues = len(dialogues)
# Load and process the validation data
val_dialogues, _ = load_woz_data(self.validation_url, word_vectors,
ontology)
# Generate the validation batch data
val_data = generate_batch(val_dialogues, 0, len(val_dialogues),
len(ontology))
val_iterations = int(len(val_dialogues) / train_batch_size)
# 2 Initialise and set up the model graph
# Initialise the model
graph = tf.Graph()
with graph.as_default():
model_variables = model_definition(ontology_vectors,
len(ontology),
slots,
num_hidden=num_hid,
bidir=bidir,
net_type=net_type,
dev=device)
(user, sys_res, no_turns, user_uttr_len, sys_uttr_len, labels,
domain_labels, domain_accuracy, slot_accuracy, value_accuracy,
value_f1, train_step, keep_prob, _, _, _) = model_variables
[precision, recall, value_f1] = value_f1
saver = tf.train.Saver()
if device == 'gpu':
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
else:
config = tf.ConfigProto(device_count={'GPU': 0})
sess = tf.Session(config=config)
if os.path.exists(TRAIN_MODEL_URL + ".index"):
saver.restore(sess, TRAIN_MODEL_URL)
print("Loading from an existing model {} ....................".
format(TRAIN_MODEL_URL))
else:
if not os.path.exists(TRAIN_MODEL_URL):
os.makedirs('/'.join(TRAIN_MODEL_URL.split('/')[:-1]))
os.makedirs('/'.join(TRAIN_GRAPH_URL.split('/')[:-1]))
init = tf.global_variables_initializer()
sess.run(init)
print(
"Create new model parameters....................................."
)
merged = tf.summary.merge_all()
val_accuracy = tf.summary.scalar('validation_accuracy',
value_accuracy)
val_f1 = tf.summary.scalar('validation_f1_score', value_f1)
train_writer = tf.summary.FileWriter(TRAIN_GRAPH_URL, graph)
train_writer.flush()
# 3 Perform an epoch of training
last_update = -1
best_f_score = -1
for epoch in range(no_epochs):
batch_size = train_batch_size
sys.stdout.flush()
iterations = math.ceil(no_dialogues / train_batch_size)
start_time = time.time()
val_i = 0
shuffle(dialogues)
for batch_id in range(iterations):
if batch_id == iterations - 1 and no_dialogues % iterations != 0:
batch_size = no_dialogues % train_batch_size
batch_user, batch_sys, batch_labels, batch_domain_labels, batch_user_uttr_len, batch_sys_uttr_len, \
batch_no_turns = generate_batch(dialogues, batch_id, batch_size, len(ontology))
[_, summary, da, sa, va, vf, pr, re] = sess.run(
[
train_step, merged, domain_accuracy, slot_accuracy,
value_accuracy, value_f1, precision, recall
],
feed_dict={
user: batch_user,
sys_res: batch_sys,
labels: batch_labels,
domain_labels: batch_domain_labels,
user_uttr_len: batch_user_uttr_len,
sys_uttr_len: batch_sys_uttr_len,
no_turns: batch_no_turns,
keep_prob: 0.5
})
print(
"The accuracies for domain is {:.2f}, slot {:.2f}, value {:.2f}, f1_score {:.2f} precision {:.2f}"
" recall {:.2f} for batch {}".format(
da, sa, va, vf, pr, re, batch_id + iterations * epoch))
train_writer.add_summary(
summary, start_batch + batch_id + iterations * epoch)
# ================================ VALIDATION ==============================================
if batch_id % batches_per_eval == 0 or batch_id == 0:
if batch_id == 0:
print("Batch", "0", "to", batch_id, "took",
round(time.time() - start_time, 2), "seconds.")
else:
print("Batch",
batch_id + iterations * epoch - batches_per_eval,
"to", batch_id + iterations * epoch, "took",
round(time.time() - start_time, 3), "seconds.")
start_time = time.time()
_, _, v_acc, f1_score, sm1, sm2 = evaluate_model(
sess, model_variables, val_data,
[val_accuracy, val_f1], batch_id, val_i)
val_i += 1
val_i %= val_iterations
train_writer.add_summary(
sm1, start_batch + batch_id + iterations * epoch)
train_writer.add_summary(
sm2, start_batch + batch_id + iterations * epoch)
stime = time.time()
current_metric = f1_score
print(" Validation metric:", round(current_metric,
5), " eval took",
round(time.time() - stime, 2), "last update at:",
last_update, "/", iterations)
# and if we got a new high score for validation f-score, we need to save the parameters:
if current_metric > best_f_score:
last_update = batch_id + iterations * epoch + 1
print(
"\n ====================== New best validation metric:",
round(current_metric,
4), " - saving these parameters. Batch is:",
last_update, "/", iterations,
"---------------- =========== \n")
best_f_score = current_metric
saver.save(sess, TRAIN_MODEL_URL)
print("The best parameters achieved a validation metric of",
round(best_f_score, 4))