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,
multiG,
dim=64,
batch_sizeK=1024,
batch_sizeA=32,
a1=5.,
a2=0.5,
m1=0.5,
save_path='this-model.ckpt',
multiG_save_path='this-multiG.bin',
L1=False):
self.multiG = multiG
self.dim = self.multiG.dim = self.multiG.KG1.dim = self.multiG.KG2.dim = dim
#self.multiG.KG1.wv_dim = self.multiG.KG2.wv_dim = wv_dim
self.batch_sizeK = self.multiG.batch_sizeK = batch_sizeK
self.batch_sizeA = self.multiG.batch_sizeA = batch_sizeA
self.multiG_save_path = multiG_save_path
self.save_path = save_path
self.L1 = self.multiG.L1 = L1
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=dim,
batch_sizeK=self.batch_sizeK,
batch_sizeA=self.batch_sizeA,
L1=self.L1)
self.tf_parts._m1 = m1
self.sess = sess = tf.Session()
sess.run(tf.initialize_all_variables())
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)
def build(self,
multiG,
method='transe',
bridge='CG-one',
dim1=300,
dim2=50,
batch_sizeK1=1024,
batch_sizeK2=1024,
batch_sizeA=32,
a1=5.,
a2=0.5,
m1=0.5,
m2=1.0,
save_path='this-model.ckpt',
multiG_save_path='this-multiG.bin',
log_save_path='tf_log',
L1=False):
self.multiG = multiG
self.method = method
self.bridge = bridge
self.dim1 = self.multiG.dim1 = self.multiG.KG1.dim = dim1 # update dim
self.dim2 = self.multiG.dim2 = self.multiG.KG2.dim = dim2 # update dim
#self.multiG.KG1.wv_dim = self.multiG.KG2.wv_dim = wv_dim
self.batch_sizeK1 = self.multiG.batch_sizeK1 = batch_sizeK1
self.batch_sizeK2 = self.multiG.batch_sizeK2 = batch_sizeK2
self.batch_sizeA = self.multiG.batch_sizeA = batch_sizeA
self.multiG_save_path = multiG_save_path
self.log_save_path = log_save_path
self.save_path = save_path
self.L1 = self.multiG.L1 = L1
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=dim1,
dim2=dim2,
batch_sizeK1=self.batch_sizeK1,
batch_sizeK2=self.batch_sizeK2,
batch_sizeA=self.batch_sizeA,
L1=self.L1)
self.tf_parts._m1 = m1
self.tf_parts._m2 = m2
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
self.writer = tf.summary.FileWriter(log_save_path,
graph=tf.get_default_graph())
def load_tfparts(multiG,
dim=64,
batch_sizeK=1024,
batch_sizeA=64,
save_path='this-model.ckpt',
L1=False):
tf_parts = model.TFParts(num_rels1=multiG.KG1.num_rels(),
num_ents1=multiG.KG1.num_ents(),
num_rels2=multiG.KG2.num_rels(),
num_ents2=multiG.KG2.num_ents(),
dim=dim,
batch_sizeK=batch_sizeK,
batch_sizeA=batch_sizeA,
L1=L1)
#with tf.Session() as sess:
sess = tf.Session()
tf_parts._saver.restore(sess, save_path)
def build(self,
multiG,
dim=64,
batch_sizeK=1024,
batch_sizeA=32,
batch_sizeH=1024,
a1=5.,
a2=0.5,
m1=0.5,
save_path='this-model.ckpt',
multiG_save_path='this-multiG.bin',
L1=False):
self.multiG = multiG
self.dim = self.multiG.dim = self.multiG.KG1.dim = self.multiG.KG2.dim = dim
# self.multiG.KG1.wv_dim = self.multiG.KG2.wv_dim = wv_dim
self.batch_sizeK = self.multiG.batch_sizeK = batch_sizeK
self.batch_sizeA = self.multiG.batch_sizeA = batch_sizeA
self.batch_sizeH = self.multiG.batch_sizeH = batch_sizeH
self.batch_sizeL1 = self.batch_sizeH * self.multiG.KG1.low_high
self.batch_sizeL2 = self.batch_sizeH * self.multiG.KG2.low_high
self.multiG_save_path = multiG_save_path
self.save_path = save_path
self.L1 = self.multiG.L1 = L1
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=dim,
batch_sizeK=self.batch_sizeK,
batch_sizeA=self.batch_sizeA,
batch_sizeH=self.batch_sizeH,
batch_sizeL1=self.batch_sizeL1,
batch_sizeL2=self.batch_sizeL2,
L1=self.L1)
self.tf_parts._m1 = m1
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)
sess.run(tf.initialize_all_variables())
def load_tfparts(multiG,
method='transe',
bridge='CG-one',
dim1=300,
dim2=100,
batch_sizeK1=1024,
batch_sizeK=1024,
batch_sizeA=64,
save_path='this-model.ckpt',
L1=False):
tf_parts = model.TFParts(num_rels1=multiG.KG1.num_rels(),
num_ents1=multiG.KG1.num_ents(),
num_rels2=multiG.KG2.num_rels(),
num_ents2=multiG.KG2.num_ents(),
method=self.method,
bridge=self.bridge,
dim1=dim1,
dim2=dim2,
batch_sizeK=batch_sizeK,
batch_sizeA=batch_sizeA,
L1=L1)
#with tf.Session() as sess:
sess = tf.Session()
tf_parts._saver.restore(sess, save_path)