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train.py
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train.py
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from visual_relationship_dataset import *
import tensorflow as tf
import randomly_weighted_feature_networks as rwfn
import time
rwfn.default_optimizer = "ftrl"
# swith between GPU and CPU
config = tf.ConfigProto(device_count={'GPU': 1})
number_of_positive_examples_types = 100
number_of_negative_examples_types = 100
number_of_positive_examples_predicates = 100
number_of_negative_examples_predicates = 100
# Load training data
train_data, pairs_of_train_data, types_of_train_data, triples_of_train_data, cartesian_of_train_data, _, cartesian_of_bb_idxs = get_data(
"train", True)
set_triples_of_train_data = set([(bb_pairs_idx[0], bb_pairs_idx[1]) for bb_pairs_idx in triples_of_train_data[:, :2]])
idxs_of_negative_examples = [idx for idx, pair in enumerate(cartesian_of_bb_idxs) if
tuple(pair) not in set_triples_of_train_data]
# Computing positive and negative examples for predicates and types
idxs_of_positive_examples_of_predicates = {}
idxs_of_negative_examples_of_predicates = {}
idxs_of_positive_examples_of_types = {}
for type in selected_types:
idxs_of_positive_examples_of_types[type] = np.where(types_of_train_data == type)[0]
for predicate in selected_predicates:
idxs_of_positive_examples_of_predicates[predicate] = \
np.where(predicates[triples_of_train_data[:, -1]] == predicate)[0]
idxs_of_negative_examples_of_predicates[predicate] = idxs_of_negative_examples
print "finished to upload and analyze data"
print "Start model definition"
# domain definition
clause_for_positive_examples_of_predicates = [
rwfn.Clause([rwfn.Literal(True, isInRelation[p], object_pairs_in_relation[p])],
label="examples_of_object_pairs_in_" + p.replace(" ", "_") + "_relation", weight=1.0) for p in
selected_predicates]
clause_for_negative_examples_of_predicates = [
rwfn.Clause([rwfn.Literal(False, isInRelation[p], object_pairs_not_in_relation[p])],
label="examples_of_object_pairs_not_in_" + p.replace(" ", "_") + "_relation", weight=1.0) for p in
selected_predicates]
# axioms from the Visual Relationship Ontology
isa_subrelation_of, has_subrelations, inv_relations_of, not_relations_of, reflexivity_relations, symmetry, domain_relation, range_relation = get_vrd_ontology()
so_domain = {}
os_domain = {}
for predicate in selected_predicates:
so_domain[predicate] = rwfn.Domain(number_of_features * 2 + number_of_extra_features,
label="object_pairs_for_axioms")
for type in selected_types:
so_domain[type] = rwfn.Domain(number_of_features * 2 + number_of_extra_features, label="object_pairs_for_axioms")
os_domain[type] = rwfn.Domain(number_of_features * 2 + number_of_extra_features,
label="inverse_object_pairs_for_axioms")
clauses_for_not_domain = [rwfn.Clause([rwfn.Literal(False, isInRelation[pred], so_domain[subj[4:]]),
rwfn.Literal(False, isOfType[subj[4:]], objects_of_type[subj[4:]])],
label="not_domain_of_" + pred.replace(" ", "_"))
for pred in domain_relation.keys() for subj in domain_relation[pred]
if len(domain_relation[pred]) > 0 and subj.split(" ")[0] == "not"]
clauses_for_not_range = [rwfn.Clause([rwfn.Literal(False, isInRelation[pred], os_domain[obj[4:]]),
rwfn.Literal(False, isOfType[obj[4:]], objects_of_type[obj[4:]])],
label="not_range_of_" + pred.replace(" ", "_"))
for pred in range_relation.keys() for obj in range_relation[pred]
if len(range_relation[pred]) > 0 and obj.split(" ")[0] == "not"]
def train(number_of_training_iterations=2500,
frequency_of_feed_dict_generation=250,
with_constraints=False,
start_from_iter=1,
saturation_limit=0.90):
global idxs_of_positive_examples_of_predicates, idxs_of_negative_examples_of_predicates
# defining the clauses of the background knowledge
clauses = clause_for_positive_examples_of_predicates + clause_for_negative_examples_of_predicates
if with_constraints:
clauses = clauses + \
clauses_for_not_domain + \
clauses_for_not_range
# defining the label of the background knowledge
for cl in clauses: print cl.label
if with_constraints:
kb_label = "RWFN_KB_wc"
else:
kb_label = "RWFN_KB_nc"
# definition of the KB
models_path = "models/"
KB = rwfn.KnowledgeBase(kb_label, clauses, models_path)
# start training
init = tf.initialize_all_variables()
sess = tf.Session(config=config)
if start_from_iter == 1:
sess.run(init)
if start_from_iter > 1:
KB.restore(sess)
feed_dict = get_feed_dict(idxs_of_positive_examples_of_predicates, idxs_of_negative_examples_of_predicates,
idxs_of_positive_examples_of_types, with_constraints=with_constraints)
train_kb = True
for i in range(start_from_iter, number_of_training_iterations + 1):
if i % frequency_of_feed_dict_generation == 0:
if train_kb:
print i
else:
train_kb = True
if train_kb and (i == number_of_training_iterations):
KB.save(sess, version="_" + str(i))
feed_dict = get_feed_dict(idxs_of_positive_examples_of_predicates,
idxs_of_negative_examples_of_predicates,
idxs_of_positive_examples_of_types,
with_constraints=with_constraints)
print "---- TRAIN", kb_label, "----"
if train_kb:
sat_level = sess.run(KB.tensor, feed_dict)
if np.isnan(sat_level):
train_kb = False
if sat_level >= saturation_limit:
train_kb = False
else:
KB.train(sess, feed_dict)
print str(i) + ' --> ' + str(sat_level)
print "end of training"
sess.close()
def get_feed_dict(idxs_of_pos_ex_of_predicates, idxs_of_neg_ex_of_predicates, idxs_of_pos_ex_of_types,
with_constraints=True):
print "selecting new training data"
feed_dict = {}
# positive and negative examples for predicates
for p in predicates:
feed_dict[object_pairs_in_relation[p].tensor] = \
pairs_of_train_data[
np.random.choice(idxs_of_pos_ex_of_predicates[p], number_of_positive_examples_predicates)]
feed_dict[object_pairs_not_in_relation[p].tensor] = \
cartesian_of_train_data[
np.random.choice(idxs_of_neg_ex_of_predicates[p], number_of_negative_examples_predicates)]
# feed data for axioms
if with_constraints:
for predicate in predicates:
feed_dict[so_domain[predicate].tensor] = feed_dict[object_pairs_in_relation[predicate].tensor]
for t in selected_types:
idxs_bb_type = np.random.choice(idxs_of_pos_ex_of_types[t], number_of_positive_examples_types)
feed_dict[objects_of_type[t].tensor] = train_data[idxs_bb_type][:, 1:]
idxs_bb_pairs_subj = []
idxs_bb_pairs_obj = []
for idx in idxs_bb_type:
idxs_bb_pairs_subj.append(np.random.choice(np.where(cartesian_of_bb_idxs[:, 0] == idx)[0], 1)[0])
idxs_bb_pairs_obj.append(np.random.choice(np.where(cartesian_of_bb_idxs[:, 1] == idx)[0], 1)[0])
feed_dict[so_domain[t].tensor] = cartesian_of_train_data[idxs_bb_pairs_subj]
feed_dict[os_domain[t].tensor] = cartesian_of_train_data[idxs_bb_pairs_obj]
return feed_dict
if __name__ == "__main__":
list_train_time = []
for wc in [True, False]:
if wc:
num_iteration = 10000
sat_limit = .96
start_time = time.time()
else:
num_iteration = 10000
sat_limit = .86
start_time = time.time()
train(number_of_training_iterations=num_iteration,
frequency_of_feed_dict_generation=50,
with_constraints=wc,
start_from_iter=1,
saturation_limit=sat_limit)
list_train_time.append((time.time() - start_time))
print "RWFN with wc: {}, RWFN with nc: {}".format(list_train_time[0], list_train_time[1])