Keras 自定义 lambda 层：如何规范化/缩放输出

Question

我正在努力扩展 lambda 层的输出。代码如下： 我的 X_train 是 100*15*24，Y_train 是 100*1（网络由 LSTM 层 + Dense 层组成）

input_shape=(timesteps, num_feat)
data_input = Input(shape=input_shape, name="input_layer")
lstm1 = LSTM(10, name="lstm_layer")(data_input)
dense1 = Dense(4, activation="relu", name="dense1")(lstm1)
dense2 = Dense(1, activation = "custom_activation_1", name = "dense2")(dense1)
dense3 = Dense(1, activation = "custom_activation_2", name = "dense3")(dense1) 
#dense 2 and 3 has customed activation function with range the REAL LINE (so I need to normalize it)


## custom lambda layer/ loss function ##
def custom_layer(new_input):

    add_input = new_input[0]+new_input[1]
    
    #below three lines are where problem occurs that makes the program does not work
    ###############################################
    scaler = MinMaxScaler()
    scaler.fit(add_input)
    normalized = scaler.transform(add_input)
    ###############################################
    return normalized

lambda_layer = Lambda(custom_layer, name="lambda_layer")([dense2, dense3])

model = Model(inputs=data_input, outputs=lambda_layer) 
model.compile(loss='mse', optimizer='adam',metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=2, batch_size=216)

如何正确规范 lambda_layer 的输出？任何想法或建议表示赞赏！

Answer 1

我认为 Scikit 转换器不能在 Lambda 层中工作。如果您只对传入数据的标准化输出感兴趣，您可以这样做，

from tensorflow.keras.layers import Input, LSTM, Dense, Lambda
from tensorflow.keras.models import Model
import tensorflow as tf


timesteps = 3
num_feat = 12
input_shape=(timesteps, num_feat)
data_input = Input(shape=input_shape, name="input_layer")
lstm1 = LSTM(10, name="lstm_layer")(data_input)
dense1 = Dense(4, activation="relu", name="dense1")(lstm1)
dense2 = Dense(1, activation = "custom_activation_1", name = "dense2")(dense1)
dense3 = Dense(1, activation = "custom_activation_2", name = "dense3")(dense1) 
#dense 2 and 3 has customed activation function with range the REAL LINE (so I need to normalize it)


## custom lambda layer/ loss function ##
def custom_layer(new_input):

    add_input = new_input[0]+new_input[1]
    
    normalized = (add_input - tf.reduce_min(add_input, axis=0, keepdims=True))/(tf.reduce_max(add_input, axis=0, keepdims=True) - tf.reduce_max(add_input, axis=0, keepdims=True))
    
    return normalized

lambda_layer = Lambda(custom_layer, name="lambda_layer")([dense2, dense3])

model = Model(inputs=data_input, outputs=lambda_layer) 
model.compile(loss='mse', optimizer='adam',metrics=['accuracy'])