Is it possible to train using same model with two inputs? - keras

Question

Hello I have a some question for keras.
currently i want implement some network
using same cnn model, and use two images as input of cnn model
and use two result of cnn model, provide to Dense model
for example
def cnn_model():
input = Input(shape=(None, None, 3))
x = Conv2D(8, (3, 3), strides=(1, 1))(input)
x = GlobalAvgPool2D()(x)
model = Model(input, x)
return model
def fc_model(cnn1, cnn2):
input_1 = cnn1.output
input_2 = cnn2.output
input = concatenate([input_1, input_2])
x = Dense(1, input_shape=(None, 16))(input)
x = Activation('sigmoid')(x)
model = Model([cnn1.input, cnn2.input], x)
return model
def main():
cnn1 = cnn_model()
cnn2 = cnn_model()
model = fc_model(cnn1, cnn2)
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x=[image1, image2], y=[1.0, 1.0], batch_size=1, ecpochs=1)
i want to implement model something like this, and train models
but i got error message like below :
'All layer names should be unique'
Actually i want use only one CNN model as feature extractor and finally use two features to predict one float value as 0.0 ~ 1.0
so whole system -->>
using two images and extract features from same CNN model, and features are provided to Dense model to get one floating value
Please, help me implement this system and how to train..
Thank you

Answer 1

See the section of the Keras documentation on shared layers:
https://keras.io/getting-started/functional-api-guide/
A code snippet from the documentation above demonstrating this:
# This layer can take as input a matrix
# and will return a vector of size 64
shared_lstm = LSTM(64)
# When we reuse the same layer instance
# multiple times, the weights of the layer
# are also being reused
# (it is effectively *the same* layer)
encoded_a = shared_lstm(tweet_a)
encoded_b = shared_lstm(tweet_b)
# We can then concatenate the two vectors:
merged_vector = keras.layers.concatenate([encoded_a, encoded_b], axis=-1)
# And add a logistic regression on top
predictions = Dense(1, activation='sigmoid')(merged_vector)
# We define a trainable model linking the
# tweet inputs to the predictions
model = Model(inputs=[tweet_a, tweet_b], outputs=predictions)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit([data_a, data_b], labels, epochs=10)