我有一个带有两个输出的CNN结构。我训练了这个网络，但是在测试期间，我想将此网络分成两个部分，分别产生输出并对其进行测试。但是我不知道如何将整个网络学习的学习权重矩阵加载到网络的每个部分。您能告诉我如何为该目标节省每个零件的重量吗？
根据我的代码，我得到了这个model=Model(inputs=[image,wtm],outputs=decoded)和这个watermark_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])，它们的解码结果和pred_w是我的两个输出。在测试期间，我希望有两个从主网络构建的网络，每个网络都产生上述输出。但是我不知道如何分割该网络，以及如何将权重矩阵输入这些网络？
from keras.layers import Input, Concatenate, GaussianNoise,Dropout,BatchNormalization
from keras.layers import Conv2D, AtrousConv2D
from keras.models import Model
from keras.datasets import mnist
from keras.callbacks import TensorBoard
from keras import backend as K
from keras import layers
import matplotlib.pyplot as plt
import tensorflow as tf
import keras as Kr
from keras.optimizers import SGD,RMSprop,Adam
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
import numpy as np
import pylab as pl
import matplotlib.cm as cm
import keract
from matplotlib import pyplot
from keras import optimizers
from keras import regularizers
from tensorflow.python.keras.layers import Lambda;
#-----------------building w train---------------------------------------------
w_expand=np.zeros((49999,28,28),dtype='float32')
wv_expand=np.zeros((9999,28,28),dtype='float32')
wt_random=np.random.randint(2, size=(49999,4,4))
wt_random=wt_random.astype(np.float32)
wv_random=np.random.randint(2, size=(9999,4,4))
wv_random=wv_random.astype(np.float32)
w_expand[:,:4,:4]=wt_random
wv_expand[:,:4,:4]=wv_random
x,y,z=w_expand.shape
w_expand=w_expand.reshape((x,y,z,1))
x,y,z=wv_expand.shape
wv_expand=wv_expand.reshape((x,y,z,1))
#-----------------building w test---------------------------------------------
w_test = np.random.randint(2,size=(1,4,4))
w_test=w_test.astype(np.float32)
wt_expand=np.zeros((1,28,28),dtype='float32')
wt_expand[:,0:4,0:4]=w_test
wt_expand=wt_expand.reshape((1,28,28,1))
#-----------------------encoder------------------------------------------------
#------------------------------------------------------------------------------
wtm=Input((28,28,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
#conv3 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl3e', kernel_initializer='Orthogonal',bias_initializer='glorot_uniform')(conv2)
BN=BatchNormalization()(conv3)
#DrO1=Dropout(0.25,name='Dro1')(BN)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
#-----------------------adding w---------------------------------------
#add_const = Kr.layers.Lambda(lambda x: x + Kr.backend.constant(w_expand))
#encoded_merged=keras.layers.Add()([encoded,wtm])
#add_const = Kr.layers.Lambda(lambda x: x + wtm)
#encoded_merged = add_const(encoded)
#encoder=Model(inputs=image, outputs= encoded_merged)
#encoded_merged = Concatenate(axis=3)([encoded, wtm])
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,wtm])
#encoder=Model(inputs=[image,wtm], outputs= encoded_merged ,name='encoder')
#encoder.summary()
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
#deconv_input=Input((28,28,1),name='inputTodeconv')
#encoded_merged = Input((28, 28, 2))
deconv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl3d')(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl4d')(deconv3)
BNd=BatchNormalization()(deconv3)
#DrO2=Dropout(0.25,name='DrO2')(BNd)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
model=Model(inputs=[image,wtm],outputs=decoded)
decoded_noise = GaussianNoise(0.5)(decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(16, (3,3), activation='relu', padding='same', name='conl1w')(decoded_noise)
convw2 = Conv2D(16, (3, 3), activation='relu', padding='same', name='convl2w')(convw1)
convw3 = Conv2D(16, (3, 3), activation='relu', padding='same', name='conl3w')(convw2)
convw4 = Conv2D(8, (3, 3), activation='relu', padding='same', name='conl4w')(convw3)
convw5 = Conv2D(8, (3, 3), activation='relu', padding='same', name='conl5w')(convw4)
convw6 = Conv2D(4, (3, 3), activation='relu', padding='same', name='conl6w')(convw5)
#BNed=BatchNormalization()(convw6)
#DrO3=Dropout(0.25, name='DrO3')(BNed)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W')(convw6)
# reconsider activation (is W positive?)
# should be filter=1 to match W
watermark_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
watermark_extraction.summary()
#----------------------training the model--------------------------------------
#------------------------------------------------------------------------------
#----------------------Data preparation----------------------------------------
(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))
#---------------------compile and train the model------------------------------
#opt=SGD(momentum=0.99)
watermark_extraction.compile(optimizer='adam', loss={'decoder_output':'mse','reconstructed_W':'binary_crossentropy'}, loss_weights={'decoder_output': 0.1, 'reconstructed_W': 1.0},metrics=['mae'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)
#rlrp = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=20, min_delta=1E-4, verbose=1)
mc = ModelCheckpoint('best_model_5x5F_dp_gn_add_adam.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)
history=watermark_extraction.fit([x_train,w_expand], [x_train,w_expand],
epochs=200,
batch_size=32,
validation_data=([x_validation,wv_expand], [x_validation,wv_expand]),
callbacks=[TensorBoard(log_dir='E:/concatnatenetwork', histogram_freq=0, write_graph=False),es,mc])
watermark_extraction.summary()
WEIGHTS_FNAME = 'v1_adam_model_5x5F_add_dp_gn.hdf'
watermark_extraction.save_weights(WEIGHTS_FNAME, overwrite=True)