如何合并具有相同输入的keras顺序模型? [英] How to merge keras sequential models with same input?

问题描述

我正在尝试在keras中创建我的第一个合奏模型.我的数据集中有3个输入值和一个输出值.

I am trying to create my first ensemble models in keras. I have 3 input values and a single output value in my dataset.

from keras.optimizers import SGD,Adam
from keras.layers import Dense,Merge
from keras.models import Sequential

model1 = Sequential()
model1.add(Dense(3, input_dim=3, activation='relu'))
model1.add(Dense(2, activation='relu'))
model1.add(Dense(2, activation='tanh'))
model1.compile(loss='mse', optimizer='Adam', metrics=['accuracy'])

model2 = Sequential()
model2.add(Dense(3, input_dim=3, activation='linear'))
model2.add(Dense(4, activation='tanh'))
model2.add(Dense(3, activation='tanh'))
model2.compile(loss='mse', optimizer='SGD', metrics=['accuracy'])

model3 = Sequential()
model3.add(Merge([model1, model2], mode = 'concat'))
model3.add(Dense(1, activation='sigmoid'))
model3.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])

model3.input_shape

整体模型(model3)编译时没有任何错误，但是在拟合模型时，我必须两次通过相同的输入model3.fit([X,X],y).我认为这是不必要的步骤，我不想为输入模型两次传递输入，而是希望有一个公共输入节点.我该怎么办?

The ensemble model(model3) compiles without any error but while fitting the model I have to pass the same input two times model3.fit([X,X],y). Which I think is an unnecessary step and instead of passing input twice I want to have a common input nodes for my ensemble model. How can I do it?

推荐答案

Keras 功能强大API 似乎更适合您的用例，因为它在计算图中提供了更大的灵活性.例如:

Keras functional API seems to be a better fit for your use case, as it allows more flexibility in the computation graph. e.g.:

from keras.layers import concatenate
from keras.models import Model
from keras.layers import Input, Merge
from keras.layers.core import Dense
from keras.layers.merge import concatenate

# a single input layer
inputs = Input(shape=(3,))

# model 1
x1 = Dense(3, activation='relu')(inputs)
x1 = Dense(2, activation='relu')(x1)
x1 = Dense(2, activation='tanh')(x1)

# model 2 
x2 = Dense(3, activation='linear')(inputs)
x2 = Dense(4, activation='tanh')(x2)
x2 = Dense(3, activation='tanh')(x2)

# merging models
x3 = concatenate([x1, x2])

# output layer
predictions = Dense(1, activation='sigmoid')(x3)

# generate a model from the layers above
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Always a good idea to verify it looks as you expect it to 
# model.summary()

data = [[1,2,3], [1,1,3], [7,8,9], [5,8,10]]
labels = [0,0,1,1]

# The resulting model can be fit with a single input:
model.fit(data, labels, epochs=50)

注意:

• Keras版本(之前和之后的版本2)之间的API可能会有细微差别
• 上面的示例为每个模型指定了不同的优化器和损失函数.但是，由于fit()仅被调用一次(在model3上)，因此与model3相同的设置将应用于整个模型.为了在训练子模型时具有不同的设置，必须分别使用fit()- 参见@Daniel的评论.

• There might be slight differences in the API between Keras versions (pre- and post- version 2)
• The example above specifies different optimizer and loss function for each of the models. However, since fit() is being called only once (on model3), the same settings - those of model3 - will apply to the entire model. In order to have different settings when training the sub-models, they will have to be fit() separately - see comment by @Daniel.

根据评论更新了笔记

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