How to merge multiple sequential models in Keras Python?

Question

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.

Here's my code:

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 says cannot do it with sequential models.

What's the workaround for it?

today · Accepted Answer · 2018-09-08 09:48:11Z

8

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'])

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.

answered Sep 8, 2018 at 9:48

today

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3 Comments

K. K. Over a year ago

What is model1.input and model1.output? Do I need to define that?

today Over a year ago

@K.K. No, it is a built-in property of Model and Sequential class. They refer to input and output tensors of the model.

K. K. Over a year ago

Thanks! It worked. But I don't think my CPU can handle such deep neural network lol.

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