def AGModel(x, signal, kernalshape, phase, height=None, width=None, scope=None):
with tf.name_scope(scope):
# attention input
Wg = weight_xavier_init(shape=kernalshape, n_inputs=kernalshape[0] * kernalshape[1] * kernalshape[2],
n_outputs=kernalshape[-1], activefunction='relu', variable_name=str(scope) + 'Wg')
Bg = bias_variable([kernalshape[-1]], variable_name=str(scope) + 'Bg')
convg = conv2d(signal, Wg) + Bg
convg = normalizationlayer(convg, phase, height=height, width=width, norm_type='group',
scope=str(scope) + 'normg')
# input
Wf = weight_xavier_init(shape=kernalshape, n_inputs=kernalshape[0] * kernalshape[1] * kernalshape[2],
n_outputs=kernalshape[-1], activefunction='relu', variable_name=str(scope) + 'Wf')
Bf = bias_variable([kernalshape[-1]], variable_name=str(scope) + 'Bf')
convf = conv2d(x, Wf) + Bf
convf = normalizationlayer(convf, phase, height=height, width=width, norm_type='group',
scope=str(scope) + 'normf')
# add input and attention input
convadd = resnet_Add(x1=convg, x2=convf)
convadd = tf.nn.relu(convadd)
# generate attention gat coe
attencoekernalshape = (1, 1, kernalshape[-1], 1)
Wpsi = weight_xavier_init(shape=attencoekernalshape,
n_inputs=attencoekernalshape[0] * attencoekernalshape[1] * attencoekernalshape[2],
n_outputs=attencoekernalshape[-1], activefunction='sigomd',
variable_name=str(scope) + 'Wpsi')
Bpsi = bias_variable([attencoekernalshape[-1]], variable_name=str(scope) + 'Bpsi')
convpsi = conv2d(convadd, Wpsi) + Bpsi
convpsi = normalizationlayer(convpsi, phase, height=height, width=width, norm_type='group',
scope=str(scope) + 'normpsi')
convpsi = tf.nn.sigmoid(convpsi)
# generate attention gat coe
attengatx = tf.multiply(x, convpsi)
return attengatx
def deconv_relu_drop(x, kernalshape, samefeture=False, scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernalshape, n_inputs=kernalshape[0] * kernalshape[1] * kernalshape[-1],
n_outputs=kernalshape[-2], activefunction='relu', variable_name=str(scope) + 'W')
B = bias_variable([kernalshape[-2]], variable_name=str(scope) + 'B')
dconv = tf.nn.relu(deconv2d(x, W, samefeature=samefeture) + B)
return dconv
def conv_relu(x, kernalshape, scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernalshape, n_inputs=kernalshape[0] * kernalshape[1] * kernalshape[2],
n_outputs=kernalshape[-1], activefunction='relu', variable_name=str(scope) + 'W')
B = bias_variable([kernalshape[-1]], variable_name=str(scope) + 'B')
conv = conv2d(x, W) + B
conv = tf.nn.relu(conv)
return conv
def down_sampling(x, kernalshape, phase, drop_conv, height=None, width=None, scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernalshape, n_inputs=kernalshape[0] * kernalshape[1] * kernalshape[2],
n_outputs=kernalshape[-1], activefunction='relu', variable_name=str(scope) + 'W')
B = bias_variable([kernalshape[-1]], variable_name=str(scope) + 'B')
conv = conv2d(x, W, 2) + B
conv = normalizationlayer(conv, phase, height=height, width=width, norm_type='group', scope=scope)
conv = tf.nn.dropout(tf.nn.relu(conv), drop_conv)
return conv
def conv_sigmod(x, kernalshape, scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernalshape,
n_inputs=kernalshape[0] * kernalshape[1] *
kernalshape[2],
n_outputs=kernalshape[-1],
activefuncation='sigomd',
variable_name=scope + 'W')
B = bias_variable([kernalshape[-1]], variable_name=scope + 'B')
conv = conv2d(x, W) + B
conv = tf.nn.sigmoid(conv)
return conv
def full_connected_relu_drop(x, kernal, drop, activefunction='relu', scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1],
n_outputs=kernal[-1], activefunction='relu', variable_name=scope + 'W')
B = bias_variable([kernal[-1]], variable_name=scope + 'B')
FC = tf.matmul(x, W) + B
if activefunction == 'relu':
FC = tf.nn.relu(FC)
FC = tf.nn.dropout(FC, drop)
elif activefunction == 'softmax':
FC = tf.nn.softmax(FC)
return FC
