Question

我正在研究Python中的Keras，我有一个神经网络（见下面的代码）。目前它仅适用于ReLu激活。

出于实验原因，我想在ReLu上有一些神经元，有些在softmax上（或任何其他激活函数）。例如，在一个有20个神经元的图层中，我希望有10个ReLu和10个Softmax。

我尝试了一些不同的方法，但始终无法获得输出。

你知道我应该怎么做吗？

# - Libraries
from keras.layers import Dense
from keras.models import Sequential
from keras.callbacks import EarlyStopping
early_spotting_monitor = EarlyStopping(patience=2)
layers = 4
neurons = 20
act = "ReLu"

# - Create Neural Network
model = Sequential()
model.add(Dense(neurons,activation=act,input_dim=X_train.shape[1]))

layers -= 1
while layers > 0:
    model.add(Dense(neurons,activation=act))  
    layers -= 1
model.add(Dense(n_months))
model.compile(optimizer="adam",loss="mean_absolute_error")

model.fit(X_train,Y_train,validation_split=0.10,epochs=13,callbacks=[early_spotting_monitor])

编辑：这是我的（工作）代码：

# - Libraries
from keras.callbacks import EarlyStopping
early_spotting_monitor = EarlyStopping(patience=2)
from keras.layers import Input, Dense
from keras.models import Model       
from keras.layers.merge import concatenate

# input layer
visible = Input(shape=(X_train.shape[1],))

hidden11 = Dense(14, activation='relu')(visible)
hidden12 = Dense(3, activation='softplus')(visible)
hidden13 = Dense(2, activation='linear')(visible)    
hidden13 = Dense(2, activation='selu')(visible)  
merge1 = concatenate([hidden11, hidden12, hidden13])

hidden21 = Dense(14, activation='relu')(merge1)
hidden22 = Dense(3, activation='softplus')(merge1)
hidden23 = Dense(2, activation='linear')(merge1)    
hidden13 = Dense(2, activation='selu')(visible) 
merge2 = concatenate([hidden21, hidden22, hidden23])

hidden3 = Dense(20, activation='relu')(merge2)

output = Dense(Y_train.shape[1],activation="linear")(hidden3)
model = Model(inputs=visible, outputs=output)

model.compile(optimizer="adam",loss="mean_absolute_error")
model.fit(X_train,Y_train,validation_split=0.10,epochs=13,callbacks=[early_spotting_monitor])  # starts training

return model

Answer 1

您必须使用Functional API来执行此操作，例如：

input = Input(shape = (X_train.shape[1]))
branchA = Dense(neuronsA, activation = "relu")(input)
branchB = Dense(neuronsB, activation = "sigmoid")(input)

out = concatenate([branchA, branchB])

您无法使用Sequential API执行此操作，因此我建议您将代码移至functional API。

Answer 2

这是我最近一直在尝试做的事情，到目前为止，这是我所做的。我认为它正在运行，但是如果有人告诉我我在做什么错，我将不胜感激。我仅在输出层上执行此操作，我的输出层有两个单元：

def activations(l):
    l_0 = tf.keras.activations.exponential(l[...,0])
    l_1 = tf.keras.activations.elu(l[...,1])
    lnew = tf.stack([l_0, l_1], axis = 1)
    return lnew

model = tf.keras.Sequential([..., Dense(2, activation = activations)])

如何在一个Keras层中使用不同的激活函数？

2 个答案: