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我如何在连体网络中使用两个相同网络的保存权重 - > 相似性网络（右/连体）和特征生成器（左）。

我遵循并实现了这个图像相似性示例，并将 siames 模型中两个相同网络的权重保存为simlarity_model.h5和feature_generator_model.h5。

如何使用这两个保存的权重进行预测？

相似模型 =>

from keras.layers import concatenate
img_a_in = Input(shape = x_train.shape[1:], name = 'ImageA_Input')
img_b_in = Input(shape = x_train.shape[1:], name = 'ImageB_Input')
img_a_feat = feature_model(img_a_in)
img_b_feat = feature_model(img_b_in)
combined_features = concatenate([img_a_feat, img_b_feat], name = 'merge_features')
combined_features = Dense(16, activation = 'linear')(combined_features)
combined_features = BatchNormalization()(combined_features)
combined_features = Activation('relu')(combined_features)
combined_features = Dense(4, activation = 'linear')(combined_features)
combined_features = BatchNormalization()(combined_features)
combined_features = Activation('relu')(combined_features)
combined_features = Dense(1, activation = 'sigmoid')(combined_features)
similarity_model = Model(inputs = [img_a_in, img_b_in], outputs = [combined_features], name = 'Similarity_Model')
similarity_model.summary()

特征生成器模型 =>

from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, MaxPool2D, Activation, Flatten, Dense, Dropout
img_in = Input(shape = x_train.shape[1:], name = 'FeatureNet_ImageInput')
n_layer = img_in
for i in range(2):
    n_layer = Conv2D(8*2**i, kernel_size = (3,3), activation = 'linear')(n_layer)
    n_layer = BatchNormalization()(n_layer)
    n_layer = Activation('relu')(n_layer)
    n_layer = Conv2D(16*2**i, kernel_size = (3,3), activation = 'linear')(n_layer)
    n_layer = BatchNormalization()(n_layer)
    n_layer = Activation('relu')(n_layer)
    n_layer = MaxPool2D((2,2))(n_layer)
n_layer = Flatten()(n_layer)
n_layer = Dense(32, activation = 'linear')(n_layer)
n_layer = Dropout(0.5)(n_layer)
n_layer = BatchNormalization()(n_layer)
n_layer = Activation('relu')(n_layer)
feature_model = Model(inputs = [img_in], outputs = [n_layer], name = 'FeatureGenerationModel')
feature_model.summary()

保存的重量 =>

feature_model.save('feature_model.h5')
similarity.save('feature_model.h5')
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