如何使用暹罗网络保存，恢复，预测（三重丢失）

我正在尝试开发一个用于简单面部验证的暹罗网络（以及第二阶段的识别） . 我有一个网络，我设法训练，但我有点困惑，如何保存和恢复模型制作预测与训练模型 . 希望可能是该领域的有经验的人可以帮助取得进展..

以下是我创建暹罗网络的方式，首先......

model = ResNet50(weights='imagenet')   # get the original ResNet50 model
model.layers.pop()   # Remove the last layer
for layer in model.layers:
    layer.trainable = False   # do not train any of original layers

x = model.get_layer('flatten_1').output
model_out = Dense(128, activation='relu',  name='model_out')(x)
model_out = Lambda(lambda  x: K.l2_normalize(x,axis=-1))(model_out)
new_model = Model(inputs=model.input, outputs=model_out)

# At this point, a new layer (with 128 units) added and normalization applied.

# Now create siamese network on top of this

anchor_in = Input(shape=(224, 224, 3))
positive_in = Input(shape=(224, 224, 3))
negative_in = Input(shape=(224, 224, 3))

anchor_out = new_model(anchor_in)
positive_out = new_model(positive_in)
negative_out = new_model(negative_in)

merged_vector = concatenate([anchor_out, positive_out, negative_out], axis=-1)

# Define the trainable model
siamese_model = Model(inputs=[anchor_in, positive_in, negative_in],
                      outputs=merged_vector)
siamese_model.compile(optimizer=Adam(lr=.0001), 
                      loss=triplet_loss, 
                      metrics=[dist_between_anchor_positive,
                               dist_between_anchor_negative])

我训练siamese_model . 当我训练它时，如果我正确地解释结果，它不是真正训练基础模型，它只训练新的暹罗网络（基本上，只训练最后一层） .

但是这个模型有3个输入流 . 在训练之后，我需要以某种方式保存这个模型，这样它只需要1或2个输入，这样我就可以通过计算2个给定图像之间的距离来执行预测 . 如何保存此模型并立即重复使用？

先感谢您！

ADDENDUM：

万一你想知道，这里是暹罗模型的总结 .

siamese_model.summary()

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_2 (InputLayer)            (None, 224, 224, 3)  0                                            
__________________________________________________________________________________________________
input_3 (InputLayer)            (None, 224, 224, 3)  0                                            
__________________________________________________________________________________________________
input_4 (InputLayer)            (None, 224, 224, 3)  0                                            
__________________________________________________________________________________________________
model_1 (Model)                 (None, 128)          23849984    input_2[0][0]                    
                                                                 input_3[0][0]                    
                                                                 input_4[0][0]                    
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 384)          0           model_1[1][0]                    
                                                                 model_1[2][0]                    
                                                                 model_1[3][0]                    
==================================================================================================
Total params: 23,849,984
Trainable params: 262,272
Non-trainable params: 23,587,712
__________________________________________________________________________________________________