如何合并两个CNN模型？

Question

我有 1D-CNN 模型和 2D-​​CNN 模型，想将它们合并，如本文所述 ，我如何合并它们？任何帮助将不胜感激，非常感谢！

from keras import Sequential, Model
from keras.layers.core import Dense, Activation
from keras.layers.convolutional import Conv2D , Conv1D
from keras.layers import Conv2D, Conv1D,MaxPooling2D, Reshape, Concatenate, Dropout , MaxPooling1D
from keras.layers.merge import concatenate
from keras.layers import Dense, Input

model_1D = Sequential()
# 1
model_1D.add(Conv1D(32, kernel_size= 5 , strides=1, activation='relu' , input_shape = (7380, 128000)))
model_1D.add(MaxPooling1D(pool_size= 4, strides=4))
# 2 
model_1D.add(Conv1D(32, kernel_size= 5 , strides=1 , activation='relu'))
model_1D.add(MaxPooling1D(pool_size= 4, strides=4))
# 3
model_1D.add(Conv1D(64, kernel_size= 5 , strides=1 , activation='relu'))
model_1D.add(MaxPooling1D(pool_size= 4, strides=4))
# 4 
model_1D.add(Conv1D(64, kernel_size= 5 , strides=1 , activation='relu'))
model_1D.add(MaxPooling1D(pool_size= 2, strides=2))
# 5
model_1D.add(Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu'))
model_1D.add(MaxPooling1D(pool_size= 2, strides= 2))
# 6
model_1D.add(Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu'))
model_1D.add(MaxPooling1D(pool_size= 2, strides= 2))
model_1D.add(Dense(9 , activation='relu'))
#model_1D.summary()
# ----------------------- 2D CNN ----------------------
model_2D = Sequential()
model_2D.add(Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu' , input_shape = (7380, 128, 251)))
model_2D.add(Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu'))
model_2D.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model_2D.add(Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu'))
model_2D.add(Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu'))
model_2D.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model_2D.add(Dense(9 , activation='relu'))
model_2D.summary()

Answer 1

你想建立一个由两个分支组成的模型，而不是两个模型，就像论文所说的那样。两个分支都需要使用Concatenate()图层合并在一起。您的代码中还缺少的另一件事是 'Flatten()' 层，必须在Dense()每个分支的最后一层之前插入。这是我的建议：

from keras import Model
from keras.layers.core import Dense, Activation
from keras.layers import Conv2D, Conv1D, MaxPooling2D, Reshape, Concatenate, Dropout , MaxPooling1D, Flatten
from keras.layers import Dense, Input

model_1D = Input((7380, 128000))
# 1
model_1D = Conv1D(32, kernel_size= 5 , strides=1, activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 2
model_1D = Conv1D(32, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 3
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 4, strides=4)(model_1D)
# 4
model_1D = Conv1D(64, kernel_size= 5 , strides=1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides=2)(model_1D)
# 5
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
# 6
model_1D = Conv1D(128, kernel_size= 5 , strides= 1 , activation='relu')(model_1D)
model_1D = MaxPooling1D(pool_size= 2, strides= 2)(model_1D)
model_1D = Flatten()(model_1D)
model_1D = Dense(9 , activation='relu')(model_1D)
# ----------------------- 2D CNN ----------------------
model_2D = Input((7380, 128, 251))
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = Conv2D(32, kernel_size=(3, 3) , strides=(1,1), activation='relu')(model_2D)
model_2D = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(model_2D)
model_2D = Flatten()(model_2D)
model_2D = Dense(9 , activation='relu')(model_2D)
merged = Concatenate()([model_1D, model_2D])
output = Dense(7, activation='softmax')(merged)

model_final = Model(inputs=[in_1D, in_2D], outputs=[output])

请注意，输入层必须明确存在，因此它们可以绑定到模型的输入中。在训练之前编译并可视化最终模型以确保架构正确：

model_final.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
from keras.utils import plot_model
plot_model(model_final, to_file='model_final.png')

在您自己的工作中，您可以使用任何其他损失函数、优化器等。