我想用Keras做一个自动编码器。在将编码器的输出发送到解码器之前，我想添加一个具有与编码器相同大小的噪声图像，然后将两者都发送到解码器。我想知道这是否可行。
遇到我的代码的这一部分：
merge_encoded_w=cv2.merge(encoded,w)
我收到此错误：
TypeError: Tensor objects are not iterable when eager execution is not enabled. To iterate over this tensor use tf.map_fn.
我的整个代码如下：
from keras.models import Sequential
from keras.layers import Input, Dense, Dropout, Activation,UpSampling2D,Conv2D, MaxPooling2D, GaussianNoise
from keras.models import Model
from keras.optimizers import SGD
from keras.datasets import mnist
from keras import regularizers
from keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
import cv2
from time import time
from keras.callbacks import TensorBoard
# Embedding phase
##encoder
w=np.random.random((1, 28,28))
input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
x = Conv2D(8, (5, 5), activation='relu', padding='same')(input_img)
#x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
#x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(2, (3, 3), activation='relu', padding='same')(x)
encoded = Conv2D(1, (3, 3), activation='relu', padding='same')(x)
merge_encoded_w=cv2.merge(encoded,w)
#
#decoder
x = Conv2D(2, (5, 5), activation='relu', padding='same')(merge_encoded_w)
#x = UpSampling2D((2, 2))(x)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
#x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu',padding='same')(x)
#x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
#Extraction phase
decodedWithNois=GaussianNoise(0.5)(decoded)
x = Conv2D(8, (5, 5), activation='relu', padding='same')(decodedWithNois)
#x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
#x = MaxPooling2D((2, 2), padding='same')(x)
final_image_watermark = Conv2D(2, (3, 3), activation='relu', padding='same')(x)
autoencoder = Model([input_img,w], [decoded,final_image_watermark(2)])
encoder=Model(input_img,encoded)
autoencoder.compile(optimizer='adadelta', loss=['mean_squared_error','mean_squared_error'],metrics=['accuracy'])
(x_train, _), (x_test, _) = mnist.load_data()
x_validation=x_train[1:10000,:,:]
x_train=x_train[10001:60000,:,:]
#
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_validation = x_validation.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format
x_validation = np.reshape(x_validation, (len(x_validation), 28, 28, 1)) # adapt this if using `channels_first` image data format
autoencoder.fit(x_train, x_train,
epochs=5,
batch_size=128,
shuffle=True,
validation_data=(x_validation, x_validation),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
decoded_imgs = autoencoder.predict(x_test)
encoded_imgs=encoder.predict(x_test)
请帮助我解决这个问题。谢谢。