如何在Keras Python中合并多个顺序模型? [英] How to merge multiple sequential models in Keras Python?

问题描述

我正在建立一个具有多个顺序模型的模型，在训练数据集之前，我需要将这些模型合并.看来Keras 2.0不再支持keras.engine.topology.Merge.我尝试了keras.layers.Add和keras.layers.Concatenate，它也无法正常工作.

I'm building a model with multiple sequential models that I need to merge before training the dataset. It seems keras.engine.topology.Merge isn't supported on Keras 2.0 anymore. I tried keras.layers.Add and keras.layers.Concatenate and it doesn't work as well.

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])

错误:

name 'Merge' is not defined

使用keras.layers.Add和keras.layers.Concatenate表示不能对顺序模型执行此操作.

Using keras.layers.Add and keras.layers.Concatenate says cannot do it with sequential models.

有什么解决方法?

推荐答案

如果我是你，我会使用 Keras功能API 在这种情况下，至少是为了制作最终模型(即merged_model).它为您提供了更大的灵活性，并让您轻松定义复杂的模型:

If I were you, I would use Keras functional API in this case, at least for making the final model (i.e. merged_model). It gives you much more flexibility and let you easily define complex models:

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'])

您还可以对定义的其他模型执行相同的操作.如前所述，功能性API可让您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.

You can also do the same thing for other models you have defined. As I mentioned, functional API gives you more control over the structure of the model, so it is recommended to be used in case of creating complex models like this.

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