问题
I am trying to develop a siamese network for simple face verification (and recognition in the second stage). I have a network in place that I managed to train but I am a bit puzzled when it comes to how to save and restore the model + making predictions with the trained model. Hoping that maybe an experienced person in the domain can help to make progress..
Here is how I create my siamese network, to begin with...
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])
And I train the siamese_model. When I train it, if I interpret results right, it is not really training the underlying model, it just trains the new siamese network (essentially, just the last layer is trained).
But this model has 3 input streams. After the training, I need to save this model in a way so that it just takes 1 or 2 inputs so that I can perform predictions by calculating the distance between 2 given images. How do I save this model and reuse it now?
Thank you in advance!
ADDENDUM:
In case you wonder, here is the summary of siamese model.
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
__________________________________________________________________________________________________
回答1:
You can use below code to save your model siamese_model.save_weights(MODEL_WEIGHTS_FILE)
And then to load your model you need to use siamese_model.load_weights(MODEL_WEIGHTS_FILE)
Thanks
来源：https://stackoverflow.com/questions/50558154/how-to-save-restore-make-predictions-with-siamese-network-with-triplet-loss