我的方法是创建其他模型，定义所有并行卷积和拉动操作，并将所有并行结果张量连接到单个输出张量.现在您可以像层一样在序列模型中添加这个并行模型图.这是我的解决方案，希望它能解决您的问题.

My approach is to create other model that defines all parallel convolution and pulling operations and concat all parallel result tensors to single output tensor. Now you can add this parallel model graph in your sequential model just like layer. Here is my solution, hope it solves your problem.

# variable initialization 
from keras import Input, Model, Sequential
from keras.layers import Conv2D, MaxPooling2D, Concatenate, Activation, Dropout, Flatten, Dense

nb_filters =100
kernel_size= {}
kernel_size[0]= [3,3]
kernel_size[1]= [4,4]
kernel_size[2]= [5,5]
input_shape=(32, 32, 3)
pool_size = (2,2)
nb_classes =2
no_parallel_filters = 3

# create seperate model graph for parallel processing with different filter sizes
# apply 'same' padding so that ll produce o/p tensor of same size for concatination
# cancat all paralle output

inp = Input(shape=input_shape)
convs = []
for k_no in range(len(kernel_size)):
    conv = Conv2D(nb_filters, kernel_size[k_no][0], kernel_size[k_no][1],
                    border_mode='same',
                         activation='relu',
                    input_shape=input_shape)(inp)
    pool = MaxPooling2D(pool_size=pool_size)(conv)
    convs.append(pool)

if len(kernel_size) > 1:
    out = Concatenate()(convs)
else:
    out = convs[0]

conv_model = Model(input=inp, output=out)

# add created model grapg in sequential model

model = Sequential()
model.add(conv_model)        # add model just like layer
model.add(Conv2D(nb_filters, kernel_size[1][0], kernel_size[1][0]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten(input_shape=input_shape))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('tanh'))

有关更多信息，请参阅类似问题:组合输出多个模型合并为一个模型

For more information refer similar question: Combining the outputs of multiple models into one model