可以在Keras中创建断开连接的隐藏层吗? [英] Can one create disconnected hidden layers in Keras?

问题描述

是否可以使用Keras创建具有不同激活功能的隐藏层，这些激活层都连接到输入层而不相互连接?

例如，具有10个神经元的隐藏层，其中5个神经元具有ReLU激活，而5个神经元具有Sigmoid激活功能.我想创建一个平板架构神经网络.

解决方案

您可以创建两个单独的密集层.这是最简单的方法.

单独的图层:

from keras.layers import *
from keras.models import Model

#model's input and the basic syntax for creating layers

inputTensor = Input(some_shape)
outputTensor = SomeLayer(blablabla)(inputTensor)
outputTensor = AnotherLayer(bblablabla)(outputTensor)


#keep creating other layers like the previous one
#when you reach the point you want to divide:

out1 = Dense(5,activation='relu')(outputTensor)
out2 = Dense(5,activation='sigmoid')(outputTensor)


#you may concatenate the results:
outputTensor = Concatenate()([out1,out2])


#keep creating more layers....


#create the model
model = Model(inputTensor,outputTensor)

Is it possible to create hidden layers with different activation functions, which are both connected to the input layer and not to each other, using Keras?

For example a hidden layer with 10 neurons where say 5 neurons have ReLU activation and 5 neurons have say Sigmoid activation functions. I want to create a slab architecture neural network.

解决方案

You can create two separate dense layers. It's the simpliest way of doing it.

Separate layers:

from keras.layers import *
from keras.models import Model

#model's input and the basic syntax for creating layers

inputTensor = Input(some_shape)
outputTensor = SomeLayer(blablabla)(inputTensor)
outputTensor = AnotherLayer(bblablabla)(outputTensor)


#keep creating other layers like the previous one
#when you reach the point you want to divide:

out1 = Dense(5,activation='relu')(outputTensor)
out2 = Dense(5,activation='sigmoid')(outputTensor)


#you may concatenate the results:
outputTensor = Concatenate()([out1,out2])


#keep creating more layers....


#create the model
model = Model(inputTensor,outputTensor)

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