使用功能性API模型

inputs = Input((75,))                                         #shape (batch,75)
output1 = Dense(75,activation='tanh',name="layer1" )(inputs) #shape (batch,75)
output2 = Dense(3,name="layer2" )(output1) #shape (batch,3)
output3 = Dense(1,name="layer3")(output2)                     #shape (batch,1)

cross1 = Lambda(lambda x: x[0] * x[1])([output2,output3])    #shape (batch,3)

model = Model(inputs,cross1)

请注意，形状与您期望的完全不同。

我建议您通过自定义层而不是Lambda层来执行此操作。为什么？量身定制的产品将使您有更多的自由做东西，并且在查看所需的重量方面也更加透明。更准确地说，如果通过Lambda层进行操作，则恒定权重将不会保存为模型的一部分，但如果使用自定义层，则将被保存。

这里是一个例子

from keras import backend as K
from keras.layers import *
from keras.models import *
import numpy as np 


class MyLayer(Layer) :
    # see https://keras.io/layers/writing-your-own-keras-layers/
    def __init__(self,w_vec=None,allow_training=False,**kwargs) :
        self._w_vec = w_vec
        assert allow_training or (w_vec is not None),\
            "ERROR: non-trainable w_vec must be initialized"
        self.allow_training = allow_training
        super().__init__(**kwargs)
        return
    def build(self,input_shape) :
        batch_size,num_feats = input_shape
        self.w_vec = self.add_weight(shape=(1,num_feats),name='w_vec',initializer='uniform',# <- use your own preferred initializer
                                     trainable=self.allow_training,)
        if self._w_vec is not None :
            # predefined w_vec
            assert self._w_vec.shape[1] == num_feats,\
                "ERROR: initial w_vec shape mismatches the input shape"
            # set it to the weight
            self.set_weights([self._w_vec])  # <- set weights to the supplied one
        super().build(input_shape)
        return
    def call(self,x) :
        # Given:
        #   x = [H21,H22,H23]
        #   w_vec = [w1,w2,w3]
        # Step 1: output elem_prod
        #   elem_prod = [H21*w1,H22*w2,H23*w3]
        elem_prod = x * self.w_vec
        # Step 2: output ret
        #   ret = (H21*w1) * (H22*w2) * (H23*w3)
        ret = K.prod(elem_prod,axis=-1,keepdims=True)
        return ret
    def compute_output_shape(self,input_shape) :
        return (input_shape[0],1)

def make_test_cases(w_vec=None,allow_training=False):
    x = Input(shape=(75,))
    y = Dense(75,name='fc1')(x)
    y = Dense(3,name='fc2')(y)
    y = MyLayer(w_vec,allow_training,name='core')(y)
    y = Dense(1,name='fc3')(y)
    net = Model(inputs=x,outputs=y,name='{}-{}'.format( 'randomInit' if w_vec is None else 'assignInit','trainable' if allow_training else 'nontrainable'))
    print(net.name)
    print(net.layers[-2].get_weights()[0])
    print(net.summary())
    return net

然后您可以运行以下测试用例来查看差异（注意打印输出中的第一行和最后一行，分别为您提供初始值和常数参数的数量）

a。恒定重量，不可训练

m1 = make_test_cases(w_vec=np.arange(3).reshape([1,3]),allow_training=False)

会给你

assignInit-nontrainable [[0. 1. 2.]]
_________________________________________________________________  
Layer (type)                 Output Shape              Param # 

=================================================================  
input_4 (InputLayer)         (None,75)                0         
_________________________________________________________________  
fc1 (Dense)                  (None,75)                5700      
_________________________________________________________________  
fc2 (Dense)                  (None,3)                 228       
_________________________________________________________________  
core (MyLayer)               (None,1)                 3         
_________________________________________________________________  
fc3 (Dense)                  (None,1)                 2         
=================================================================  
Total params: 5,933  
Trainable params: 5,930  
Non-trainable params: 3
_________________________________________________________________ 

b。恒定重量，可训练

m2 = make_test_cases(w_vec=np.arange(3).reshape([1,allow_training=True)

会给你

assignInit-trainable    [[0. 1. 2.]]
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         (None,75)                0         
_________________________________________________________________
fc1 (Dense)                  (None,75)                5700      
_________________________________________________________________
fc2 (Dense)                  (None,3)                 228       
_________________________________________________________________
core (MyLayer)               (None,1)                 3         
_________________________________________________________________
fc3 (Dense)                  (None,1)                 2         
=================================================================
Total params: 5,933
Trainable params: 5,933
Non-trainable params: 0
_________________________________________________________________

c。随机权重，可训练

m3 = make_test_cases(w_vec=None,allow_training=True)

会给你

randomInit-trainable [[ 0.02650297 -0.02010062 -0.03771694]]
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_6 (InputLayer)         (None,933
Non-trainable params: 0
_________________________________________________________________

最后的话

我会说，目前尚不清楚哪种情况可能会更好地解决您的问题，但是尝试所有这三种听起来都不错。