def MT_model(perm):
#def funx1(i,maxL):
#if i<maxL:
#return Input(shape=(fpSize,),name=f"MRg_{i}")
#elif i<2*maxL-3 and i!=maxL+2:
# elif maxL<=i< 2*maxL:
#return Input(shape=(FpLen,),name=f"Ml_{i}")
#else:
# return Input(shape=(FpLen1,),name=f"Sq_{i}")
def funx1(i, maxL):
if i < maxL:
return Input(shape=(256, ), name=f"MoR_{i}")
x2 = [funx1(i, maxL) for i in range(1 * maxL)]
# x2_1=Concatenate()([x2[0],x2[1],x2[2]])
xs = []
xs = [x2[perm[0]], x2[perm[1]], x2[perm[2]]]
#xs=[x2[1],x2[2],x2[0]]
#xs=[x2[1],x2[0],x2[2]]
#xs=[x2[2],x2[0],x2[1]]
#xs=[x2[2],x2[1],x2[0]]
#xs=[x2[2],x2[0],x2[1]]
#xs1=[]
xs2 = []
#xs.append(x2)
#for i in range(maxL):
# xs.append(x2[i])
# xs.append(x2[maxL+i])
# xs.append(x2[2*maxL+i])
list1 = [100, 100, 100, 100]
#list1=[100,58,58,58]
# list2=[112,112,112,112]
pairwise_model = pl.PairwiseModel((256, ),
pl.repeat_layers(Dense,
list1,
name="hidden",
activation='relu'),
name="pairwise_model")
perm_encoder = pl.PermutationalEncoder(pairwise_model,
maxL,
name="permutational_encoder")
perm_layer = pl.PermutationalLayer1(perm_encoder,
name="permutational_layer")
outputs = perm_layer.model(xs)
outputs = average(outputs)
#outputs = maximum(outputs)
# print("x2",x2)
output_51 = Dense(100, activation='relu',
kernel_initializer=my_init)(outputs)
# output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
# output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
# output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
# output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
# output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
# #output_51 = Dropout(0.5)(output_51)
# output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
#
output_Loss = Dense(17,
name='Loss_output',
activation='softmax',
kernel_initializer=my_init)(output_51)
# output_Loss=Dense(17,name='Loss_output',activation='linear',kernel_initializer=my_init)(output_51)
#output_Loss=Dense(17,name='Loss_output',activation='linear',kernel_initializer=my_init)(output_51)
model = Model(inputs=x2, outputs=output_Loss)
# model.compile(loss={'Loss_output':'categorical_crossentropy'},
# optimizer=Adam(lr=0.00005, beta_1=0.9, beta_2=0.999, epsilon=1e-8), loss_weights = {'Loss_output':1.}
# ,metrics=['accuracy']
# )
model.compile(
loss={'Loss_output': 'categorical_crossentropy'},
optimizer=Adam(lr=0.00276, beta_1=0.9, beta_2=0.999, epsilon=1e-8),
loss_weights={'Loss_output': 1.}
#optimizer=Adam(lr=0.00005, beta_1=0.9, beta_2=0.999, epsilon=1e-8), loss_weights = {'Loss_output':1.}
,
metrics=['accuracy'])
model.summary()
plot_model(model, 'Config3Mod.png', show_shapes=True)
return model
def MT_model(perm):
#def funx1(i,maxL):
#if i<maxL:
#return Input(shape=(fpSize,),name=f"MRg_{i}")
#elif i<2*maxL-3 and i!=maxL+2:
# elif maxL<=i< 2*maxL:
#return Input(shape=(FpLen,),name=f"Ml_{i}")
#else:
# return Input(shape=(FpLen1,),name=f"Sq_{i}")
def funx1(i, maxL):
if i < maxL:
return Input(shape=(FpLen, ), name=f"Mol_{i}")
elif maxL <= i < 2 * maxL:
return Input(shape=(FpLen1, ), name=f"S2q_{i}")
else:
return Input(shape=(767, ), name=f"Mfp2q_{i}")
x2 = [funx1(i, maxL) for i in range(2 * maxL)]
xs = []
xs1 = []
xs2 = []
for i in range(maxL):
xs.append(x2[perm[i]])
xs.append(x2[maxL + perm[i]])
# xs.append(x2[2*maxL+i])
list1 = [112, 112, 112, 112]
#list2=[112,112,112,112]
pairwise_model = pl.PairwiseModel((256, ),
pl.repeat_layers(Dense,
list1,
name="hidden",
activation='relu'),
name="pairwise_model")
perm_encoder = pl.PermutationalEncoder(pairwise_model,
2 * maxL,
name="permutational_encoder")
perm_layer = pl.PermutationalLayer1(perm_encoder,
name="permutational_layer")
outputs = perm_layer.model(xs)
perm_layer4 = pl.PermutationalLayer1(
pl.PermutationalEncoder(
pl.PairwiseModel((112, ),
pl.repeat_layers(Dense, [256],
activation="linear")), 2 * maxL),
name="permutational_layer4",
)
outputs = perm_layer4.model(outputs)
#outputs = Add()(outputs)
#outputs1 = Add()(outputs1)
outputs = maximum(outputs)
#outputs1 = maximum(outputs1)
#outputs2 = maximum(outputs2)
#outputs = average(outputs)
# outputs1 = average(outputs1)
# outputs2 = average(outputs2)
# output_3 = Concatenate()([outputs,outputs1,outputs2])
output_3 = outputs
output_3 = Dense(100, activation='relu',
kernel_initializer=my_init)(output_3)
output_Loss = Dense(17,
name='Loss_output',
activation='softmax',
kernel_initializer=my_init)(output_3)
#output_Loss=Dense(17,name='Loss_output',activation='sigmoid',kernel_initializer=my_init)(output_3)
model = Model(inputs=x2, outputs=output_Loss)
model.compile(
loss={'Loss_output': 'categorical_crossentropy'},
#model.compile(loss={'Loss_output':'binary_crossentropy'},
optimizer=Adam(lr=0.00276, beta_1=0.9, beta_2=0.999, epsilon=1e-8),
loss_weights={'Loss_output': 1.}
# optimizer=Adam(lr=0.00005, beta_1=0.9, beta_2=0.999, epsilon=1e-8), loss_weights = {'Loss_output':1.}
,
metrics=['accuracy'])
model.summary()
plot_model(model, 'Config3Mod.png', show_shapes=True)
return model