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Question

我需要修改第二个卷积层的最后一个特征映射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脚本和输出中所需更改的示例谢谢您的帮助！

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)

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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])

设置所需的layer_output值
我需要在每个传播步骤中都这样做。

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

我认为在这种情况下我会使用具有常数张量的连接。不幸的是，我无法让它工作，但无论如何我都会分享我的工作，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)]

但希望无论如何这对你有帮助。

是否可以设置keras层输出？

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1 个答案: