如何在 Keras Python 中合并多个顺序模型？

我正在构建一个包含多个顺序模型的模型，在训练数据集之前需要合并这些模型。它似乎keras.engine.topology.MergeKeras 2.0 不再支持。我试过keras.layers.Add and keras.layers.Concatenate但效果不太好。

这是我的代码：

model = Sequential()

model1 = Sequential()
model1.add(Embedding(len(word_index) + 1, 300, weights = [embedding_matrix], input_length = 40, trainable = False))
model1.add(TimeDistributed(Dense(300, activation = 'relu')))
model1.add(Lambda(lambda x: K.sum(x, axis = 1), output_shape = (300, )))

model2 = Sequential()
###Same as model1###

model3 = Sequential()
model3.add(Embedding(len(word_index) + 1, 300, weights = [embedding_matrix], input_length = 40, trainable = False))
model3.add(Convolution1D(nb_filter = nb_filter, filter_length = filter_length, border_mode = 'valid', activation = 'relu', subsample_length = 1))
model3.add(GlobalMaxPooling1D())
model3.add(Dropout(0.2))
model3.add(Dense(300))
model3.add(Dropout(0.2))
model3.add(BatchNormalization())

model4 = Sequential()
###Same as model3###

model5 = Sequential()
model5.add(Embedding(len(word_index) + 1, 300, input_length = 40, dropout = 0.2))
model5.add(LSTM(300, dropout_W = 0.2, dropout_U = 0.2))

model6 = Sequential()
###Same as model5###

merged_model = Sequential()
merged_model.add(Merge([model1, model2, model3, model4, model5, model6], mode = 'concat'))
merged_model.add(BatchNormalization())
merged_model.add(Dense(300))
merged_model.add(PReLU())
merged_model.add(Dropout(0.2))
merged_model.add(Dense(1))
merged_model.add(BatchNormalization())
merged_model.add(Activation('sigmoid'))
merged_model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
checkpoint = ModelCheckpoint('weights.h5', monitor = 'val_acc', save_best_only = True, verbose = 2)
merged_model.fit([x1, x2, x1, x2, x1, x2], y = y, batch_size = 384, nb_epoch = 200, verbose = 1, validation_split = 0.1, shuffle = True, callbacks = [checkpoint])

Error:

name 'Merge' is not defined

Using keras.layers.Add and keras.layers.Concatenate说不能用顺序模型做到这一点。

有什么解决方法吗？

如果我是你，我会使用Keras 函数式 API https://keras.io/getting-started/functional-api-guide/在这种情况下，至少用于制作最终模型（即merged_model）。它为您提供了更大的灵活性，让您轻松定义复杂的模型：

from keras.models import Model
from keras.layers import concatenate

merged_layers = concatenate([model1.output, model2.output, model3.output,
                             model4.output, model5.output, model6.output])
x = BatchNormalization()(merged_layers)
x = Dense(300)(x)
x = PReLU()(x)
x = Dropout(0.2)(x)
x = Dense(1)(x)
x = BatchNormalization()(x)
out = Activation('sigmoid')(x)
merged_model = Model([model1.input, model2.input, model3.input,
                      model4.input, model5.input, model6.input], [out])
merged_model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

您也可以对您定义的其他模型执行相同的操作。正如我所提到的，函数式 API 使您可以更好地控制模型的结构，因此建议在创建此类复杂模型时使用它。