在Conv1D keras中合并6个输入

from keras.models import Model
from keras.layers import Input, Conv1D, Concatenate

# 定义6个输入
input1 = Input(shape=(100, 1))
input2 = Input(shape=(100, 1))
input3 = Input(shape=(100, 1))
input4 = Input(shape=(100, 1))
input5 = Input(shape=(100, 1))
input6 = Input(shape=(100, 1))

# 定义卷积层
conv1 = Conv1D(filters=32, kernel_size=3, activation='relu')

# 对每个输入进行卷积操作
output1 = conv1(input1)
output2 = conv1(input2)
output3 = conv1(input3)
output4 = conv1(input4)
output5 = conv1(input5)
output6 = conv1(input6)

# 合并6个输出
merged_output = Concatenate(axis=1)([output1, output2, output3, output4, output5, output6])

# 定义模型
model = Model(inputs=[input1, input2, input3, input4, input5, input6], outputs=merged_output)

# 输出模型结构
model.summary()