def encoder(inputs_list,
nb_nodes,
training,
attn_drop,
ffd_drop,
bias_mat_local_list,
bias_mat_global_list,
hid_units,
n_heads,
mp_att_size=16,
activation=tf.nn.elu,
residual=False):
embed_list = []
for inputs, bias_mat_local, bias_mat_global in zip(
inputs_list, bias_mat_local_list, bias_mat_global_list):
attns = []
for _ in range(n_heads):
attn_temp = layers.sp_attn_head(inputs,
adj_mat_local=bias_mat_local,
adj_mat_global=bias_mat_global,
out_sz=hid_units[0],
activation=activation,
in_drop=ffd_drop,
coef_drop=attn_drop,
residual=False)
attns.append(attn_temp)
h_1 = tf.concat(attns, axis=-1)
embed_list.append(tf.expand_dims(tf.squeeze(h_1), axis=1))
multi_embed = tf.concat(embed_list, axis=1)
final_embed, alpha = layers.SimpleAttLayer(multi_embed,
mp_att_size,
time_major=False,
return_alphas=True)
return final_embed
def inference(inputs, nb_classes, nb_nodes, training, attn_drop, ffd_drop,
bias_mat_list, hid_units, n_heads, activation=tf.nn.elu, residual=False,
mp_att_size=128):
embed_list = []
# coef_list = []
for bias_mat in bias_mat_list:
attns = []
head_coef_list = []
for _ in range(n_heads[0]):
attns.append(layers.attn_head(inputs, bias_mat=bias_mat,
out_sz=hid_units[0], activation=activation,
in_drop=ffd_drop, coef_drop=attn_drop, residual=False,
return_coef=return_coef))
h_1 = tf.concat(attns, axis=-1)
for i in range(1, len(hid_units)):
h_old = h_1
attns = []
for _ in range(n_heads[i]):
attns.append(layers.attn_head(h_1,
bias_mat=bias_mat,
out_sz=hid_units[i],
activation=activation,
in_drop=ffd_drop,
coef_drop=attn_drop,
residual=residual))
h_1 = tf.concat(attns, axis=-1)
embed_list.append(tf.expand_dims(tf.squeeze(h_1), axis=1))
# print('att for mp')
multi_embed = tf.concat(embed_list, axis=1)
final_embed, att_val = layers.SimpleAttLayer(multi_embed, mp_att_size,
time_major=False,
return_alphas=True)
# print(att_val)
# last layer for clf
out = []
for i in range(n_heads[-1]):
out.append(tf.layers.dense(final_embed, nb_classes, activation=None))
# out.append(layers.attn_head(h_1, bias_mat=bias_mat,
# out_sz=nb_classes, activation=lambda x: x,
# in_drop=ffd_drop, coef_drop=attn_drop, residual=False))
logits = tf.add_n(out) / n_heads[-1]
# logits_list.append(logits)
print('de')
logits = tf.expand_dims(logits, axis=0)
# if return_coef:
# return logits, final_embed, att_val, coef_list
# else:
return logits, final_embed, att_val
def inference(inputs_list, nb_classes, nb_nodes, training, attn_drop, ffd_drop,
bias_mat_list, hid_units, n_heads, features, labels, activation=tf.nn.elu, residual=False,
mp_att_size=200, feature_size=100):
#Metric Learning
temp = inputs_list[0]
# temp2 = tf.reduce_sum(temp, 0)
# print ("temp2 check: ", temp2)
MetricInputs = tf.layers.dense(temp, feature_size, activation=None)
# ExpendMetricInputs = tf.expand_dims(MetricInputs, 0)
print ("ExpendMetricInputs: check :", MetricInputs)
inputs_list = [MetricInputs]
# tests
mp_att_size = 200
embed_list = []
for inputs, bias_mat in zip(inputs_list, bias_mat_list):
attns = []
jhy_embeds = []
for _ in range(n_heads[0]):
attns.append(layers.attn_head(inputs, bias_mat=bias_mat,
out_sz=hid_units[0], activation=activation,
in_drop=ffd_drop, coef_drop=attn_drop, residual=False))
h_1 = tf.concat(attns, axis=-1)
for i in range(1, len(hid_units)):
h_old = h_1
attns = []
for _ in range(n_heads[i]):
attns.append(layers.attn_head(h_1, bias_mat=bias_mat,
out_sz=hid_units[i],
activation=activation,
in_drop=ffd_drop,
coef_drop=attn_drop, residual=residual))
h_1 = tf.concat(attns, axis=-1)
embed_list.append(tf.expand_dims(tf.squeeze(h_1), axis=1))
multi_embed = tf.concat(embed_list, axis=1)
print ("multi_embed: ", multi_embed, ", mp_att_size: ", mp_att_size)
final_embed, att_val = layers.SimpleAttLayer(multi_embed, mp_att_size,
time_major=False,
return_alphas=True)
# feature_size, labels, features
# num_classes, feature_size, labels, features
centers_embed = HeteGAT_multi.getCenters(len(set(labels)), feature_size, labels, final_embed)
centers_embed = tf.transpose(centers_embed)
out = []
for i in range(n_heads[-1]):
out.append(tf.layers.dense(final_embed, nb_classes, activation=None))
# out.append(layers.attn_head(h_1, bias_mat=bias_mat,
# out_sz=nb_classes, activation=lambda x: x,
# in_drop=ffd_drop, coef_drop=attn_drop, residual=False))
logits = tf.add_n(out) / n_heads[-1]
# logits_list.append(logits)
print('de')
logits = tf.expand_dims(logits, axis=0)
test_final_embeed = tf.reduce_sum(MetricInputs,0)
return logits, final_embed , att_val, centers_embed, test_final_embeed
def inference(inputs_list,
attn_drop,
ffd_drop,
bias_mat_list,
hid_units,
n_heads,
activation=tf.nn.elu,
residual=False,
mp_att_size=128):
embed_list = []
for inputs, bias_mat in zip(inputs_list, bias_mat_list):
attns = []
jhy_embeds = []
for _ in range(n_heads[0]): # 8
attns.append(
layers.attn_head(inputs,
bias_mat=bias_mat,
out_sz=hid_units[0],
activation=activation,
in_drop=ffd_drop,
coef_drop=attn_drop,
residual=False))
h_1 = tf.concat(attns, axis=-1) # 1*x*(8*8)
for i in range(1, len(hid_units)): #range(1,1)
h_old = h_1
attns = []
for _ in range(n_heads[i]):
attns.append(
layers.attn_head(h_1,
bias_mat=bias_mat,
out_sz=hid_units[i],
activation=activation,
in_drop=ffd_drop,
coef_drop=attn_drop,
residual=residual))
h_1 = tf.concat(attns, axis=-1)
# h_1: 1*X*64 ==> X*1*64
# embed_list.append(tf.expand_dims(tf.squeeze(h_1), axis=1))
# change API because X != 1
embed_list.append(tf.transpose(h_1, [1, 0, 2]))
# size of inputs_list * (X*1*64) ==> X*L*64
multi_embed = tf.concat(embed_list, axis=1)
print("inference/multi_embed is {}".format(multi_embed))
final_embed, att_val = layers.SimpleAttLayer(multi_embed,
mp_att_size,
time_major=False,
return_alphas=True)
# out = []
# for i in range(n_heads[-1]):
# out.append(tf.layers.dense(final_embed, nb_classes, activation=None))
# # out.append(layers.attn_head(h_1, bias_mat=bias_mat,
# # out_sz=nb_classes, activation=lambda x: x,
# # in_drop=ffd_drop, coef_drop=attn_drop, residual=False))
# logits = tf.add_n(out) / n_heads[-1]
# # logits_list.append(logits)
# print('de')
# logits = tf.expand_dims(logits, axis=0)
# return logits, final_embed, att_val
return final_embed, att_val
