def build(self, save_path = 'this-model.ckpt', data_save_path = 'this-data.bin'):
self.multiG = multiG.multiG()
self.multiG.load(data_save_path)
self.tf_parts = model.TFParts(num_rels1=self.multiG.KG1.num_rels(),
num_ents1=self.multiG.KG1.num_ents(),
num_rels2=self.multiG.KG2.num_rels(),
num_ents2=self.multiG.KG2.num_ents(),
dim=self.multiG.dim,
#batch_sizeK=self.batch_sizeK,
#batch_sizeA=self.batch_sizeA,
L1=self.multiG.L1)
c = tf.ConfigProto(inter_op_parallelism_threads=3, intra_op_parallelism_threads=3)
c.gpu_options.allow_growth = True
self.sess = sess = tf.Session(config=c)
self.tf_parts._saver.restore(sess, save_path) # load it
value_ht1, value_r1, value_ht2, value_r2, value_M, value_b = sess.run([self.tf_parts._ht1_norm, self.tf_parts._r1, self.tf_parts._ht2_norm, self.tf_parts._r2, self.tf_parts._M, self.tf_parts._b]) # extract values.
#value_ht1, value_r1, value_ht2, value_r2, value_M = sess.run([self.tf_parts._ht1_norm, self.tf_parts._r1, self.tf_parts._ht2_norm, self.tf_parts._r2, self.tf_parts._M])
#sess.close()
self.vec_e[1] = np.array(value_ht1)
self.vec_e[2] = np.array(value_ht2)
self.vec_r[1] = np.array(value_r1)
self.vec_r[2] = np.array(value_r2)
self.mat = np.array(value_M)
self._b = np.array(value_b)
def build(self,
save_path='this-model.ckpt',
data_save_path='this-data.bin'):
self.multiG = multiG.multiG()
self.multiG.load(data_save_path)
self.tf_parts = model.TFParts(
num_rels1=self.multiG.KG1.num_rels(),
num_ents1=self.multiG.KG1.num_ents(),
num_rels2=self.multiG.KG2.num_rels(),
num_ents2=self.multiG.KG2.num_ents(),
dim=self.multiG.dim,
#batch_sizeK=self.batch_sizeK,
#batch_sizeA=self.batch_sizeA,
L1=self.multiG.L1)
self.sess = sess = tf.Session()
self.tf_parts._saver.restore(sess, save_path) # load it
value_ht1, value_r1, value_ht2, value_r2, value_M, value_b = sess.run([
self.tf_parts._ht1_norm, self.tf_parts._r1,
self.tf_parts._ht2_norm, self.tf_parts._r2, self.tf_parts._M,
self.tf_parts._b
]) # extract values.
#value_ht1, value_r1, value_ht2, value_r2, value_M = sess.run([self.tf_parts._ht1_norm, self.tf_parts._r1, self.tf_parts._ht2_norm, self.tf_parts._r2, self.tf_parts._M])
#sess.close()
self.vec_e[1] = np.array(value_ht1)
self.vec_e[2] = np.array(value_ht2)
self.vec_r[1] = np.array(value_r1)
self.vec_r[2] = np.array(value_r2)
self.mat = np.array(value_M)
self._b = np.array(value_b)
this_dim = 50
if len(sys.argv) > 1:
this_dim = int(sys.argv[1])
model_path = sys.argv[2]
data_path = sys.argv[3]
kgf1 = sys.argv[4]
kgf2 = sys.argv[5]
alignf = sys.argv[6]
KG1 = KG()
KG2 = KG()
KG1.load_triples(filename=kgf1, splitter='@@@', line_end='\n')
KG2.load_triples(filename=kgf2, splitter='@@@', line_end='\n')
this_data = multiG(KG1, KG2)
this_data.load_align(filename=alignf,
lan1='en',
lan2='fr',
splitter='@@@',
line_end='\n')
# In[ ]:
m_train = Trainer()
m_train.build(this_data,
dim=this_dim,
batch_sizeK=128,
batch_sizeA=64,
a1=5.,
a2=0.5,
def build(self,
save_path='this-model.ckpt',
data_save_path='this-data.bin',
method='transe',
bridge='CG',
int_method='gumbel'):
self.multiG = multiG.multiG()
self.multiG.load(data_save_path)
self.method = method
self.bridge = bridge
self.int_method = int_method
self.tf_parts = model.TFParts(num_rels1=self.multiG.KG1.num_rels(),
num_ents1=self.multiG.KG1.num_ents(),
num_rels2=self.multiG.KG2.num_rels(),
num_ents2=self.multiG.KG2.num_ents(),
method=self.method,
bridge=self.bridge,
dim1=self.multiG.dim1,
dim2=self.multiG.dim2,
int_method=int_method,
L1=self.multiG.L1)
print(self.method, self.bridge, self.int_method) #load
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = sess = tf.Session(config=config)
self.tf_parts._saver.restore(sess, save_path) # load it
# if self.tf_parts.bridge == "CMP-double":
# value_ht1, value_r1, value_ht2, value_r2, value_Mc, value_bc, value_Me, value_be = sess.run(
# [self.tf_parts._ht1_norm, self.tf_parts._r1, self.tf_parts._ht2_norm, self.tf_parts._r2, self.tf_parts._Mc, self.tf_parts._bc, self.tf_parts._Me, self.tf_parts._be]) # extract values.
# self._Mc = np.array(value_Mc)
# self._bc = np.array(value_bc)
# self._Me = np.array(value_Me)
# self._be = np.array(value_be)
# print(self._Mc.shape, self._bc.shape, self._Me.shape, self._be.shape)
# else:
# value_ht1, value_r1, value_ht2, value_r2, value_M, value_b = sess.run(
# [self.tf_parts._ht1_norm, self.tf_parts._r1, self.tf_parts._ht2_norm, self.tf_parts._r2, self.tf_parts._M, self.tf_parts._b]) # extract values.
# self.mat = np.array(value_M)
# self._b = np.array(value_b)
# print(self.mat.shape, self._b.shape)
value_ht1, value_r1, ht2_min, ht2_delta, value_r2_head, value_r2_tail = sess.run(
[
self.tf_parts._ht1_norm, self.tf_parts._r1,
self.tf_parts._ht2.min_embed, self.tf_parts._ht2.delta_embed,
self.tf_parts._r2_head, self.tf_parts._r2_tail
])
self.vec_e[1] = np.array(value_ht1)
self.vec_e[2] = np.array(ht2_min)
self.vec_e[3] = np.array(ht2_delta)
self.vec_r[1] = np.array(value_r1)
self.vec_r[2] = np.array(value_r2_head)
self.vec_r[3] = np.array(value_r2_tail)
self._ht2 = BoxMethods(ht2_min.shape[1],
ht2_min.shape[0],
int_method=self.int_method)
print(self.vec_e[1].shape, self.vec_e[2].shape, self.vec_r[1].shape,
self.vec_r[2].shape)
sess.close()