功能性API中的Keras Multiply()层 [英] Keras Multiply() layer in functional API

问题描述

在新的API更改下，如何在Keras中进行层的元素逐级乘法?在旧的API下，我会尝试这样的事情:

Under the new API changes, how do you do element-wise multiplication of layers in Keras? Under the old API, I would try something like this:

merge([dense_all, dense_att], output_shape=10, mode='mul')

我已经尝试过(MWE):

I've tried this (MWE):

from keras.models import Model
from keras.layers import Input, Dense, Multiply

def sample_model():
        model_in = Input(shape=(10,))
        dense_all = Dense(10,)(model_in)
        dense_att = Dense(10, activation='softmax')(model_in)
        att_mull = Multiply([dense_all, dense_att]) #merge([dense_all, dense_att], output_shape=10, mode='mul')
        model_out = Dense(10, activation="sigmoid")(att_mull)
        return 0

if __name__ == '__main__':
        sample_model()

完整跟踪:

Using TensorFlow backend.
Traceback (most recent call last):
  File "testJan17.py", line 13, in <module>
    sample_model()
  File "testJan17.py", line 8, in sample_model
    att_mull = Multiply([dense_all, dense_att]) #merge([dense_all, dense_att], output_shape=10, mode='mul')
TypeError: __init__() takes exactly 1 argument (2 given)

我尝试实现tensorflow的逐元素乘法函数.当然，结果不是 Layer()实例，因此它不起作用.这是后代的尝试:

I tried implementing tensorflow's elementwise multiply function. Of course, the result is not a Layer() instance, so it doesn't work. Here's the attempt, for posterity:

def new_multiply(inputs): #assume two only - bad practice, but for illustration...
        return tf.multiply(inputs[0], inputs[1])


def sample_model():
        model_in = Input(shape=(10,))
        dense_all = Dense(10,)(model_in)
        dense_att = Dense(10, activation='softmax')(model_in) #which interactions are important?
        new_mult = new_multiply([dense_all, dense_att])
        model_out = Dense(10, activation="sigmoid")(new_mult)
        model = Model(inputs=model_in, outputs=model_out)
        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
        return model

推荐答案

使用 keras > 2.0:

from keras.layers import multiply
output = multiply([dense_all, dense_att])

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