是否可以设置keras层输出？

Question

我需要修改第二个卷积层的最后一个特征图的输出。
或者如果可能的话，将数组添加到我的转换层输出中。
下面是我创建的 python 脚本和输出所需更改的示例。
感谢您的帮助！

import numpy as np
from keras import backend as K

num=18  
m=11  
n=50  
k=3  
np.random.seed(100)  
features = np.random.rand(num,m,n,k)

型号

input_shape=features.shape[1:]  
model = Sequential()  
model.add(Conv2D(2, kernel_size=(1, 3), strides=(1,1),activation='relu',input_shape=input_shape))  
model.add(Conv2D(21, kernel_size=(1, 48), strides=(1,1),padding="valid",activation='relu'))  
model.add(Conv2D(1, kernel_size=(1, 1), strides=(1, 1),activation='relu',padding="valid"))  
model.add(Dense(1, activation='softmax'))  
Adam = optimizers.Adam(lr=0.00003, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)  
model.compile(loss='mse',optimizer=Adam)  

get_1st_layer_output = K.function([model.layers[0].input],
[model.layers[1].output])  
layer_output = get_1st_layer_output([features])

设置 DESIRED layer_output 值
我需要在每个传播步骤都这样做。

for i in range(0,11):
    layer_output[0][0][i][0][20]=0.1
    print(layer_output[0][0][i][0][20])  

Answer 1

在这种情况下，我想我会使用带有常数张量的连接。不幸的是，我不能让它发挥作用，但我还是会分享我的工作，希望能帮助你。

import numpy as np
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Conv2D, Dense, Concatenate
from keras import optimizers

num=18
m=11
n=50
k=3
np.random.seed(100)
features = np.random.rand(num, m, n, k)

custom_tensor = K.constant(0.1, shape=(11, 48, 1))

input_shape = features.shape[1:]
input = keras.Input(shape=input_shape)
print(K.ndim(input))
layer0 = Conv2D(2, kernel_size=(1, 3), strides=(1,1),activation='relu')(input)
layer0_added = Concatenate(axis=-1)([layer0, custom_tensor])
layer1 = Conv2D(20, kernel_size=(1, 48), strides=(1,1),padding="valid",activation='relu')(layer0_added)
layer2 = Conv2D(1, kernel_size=(1, 1), strides=(1, 1),activation='relu',padding="valid")(layer1)
layer3 = Dense(1, activation='softmax')(layer2)

model = keras.models.model(layer0)
Adam = optimizers.Adam(lr=0.00003, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='mse', optimizer=Adam)

它产生了一个错误

ValueError: `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 11, 48, 2), (11, 48, 1)]

但希望这对您有所帮助。