Hi, I'm using your code as pattern for my, as I'm trying to implement triplet loss with keras too. The main difference is that I'm using a sequential model, so I can not use "merge". My model looks like this:
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def build_model(img_x, img_y):
input_shape = (img_x, img_y, 3)
reid_model = Sequential()
reid_model.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1),
activation='relu',
input_shape=input_shape))
reid_model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
reid_model.add(Flatten())
reid_model.add(Dense(512, activation='sigmoid'))
anchor = Input(shape=(128, 254, 3))
positive = Input(shape=(128, 254, 3))
negative = Input(shape=(128, 254, 3))
anchor_embed = reid_model(anchor)
positive_embed = reid_model(positive)
negative_embed = reid_model(negative)
dist_anchor_positive = distance(anchor_embed, positive_embed)
dist_anchor_negative = distance(anchor_embed, negative_embed)
loss = triplets_max(dist_anchor_positive, dist_anchor_negative, 0.05)
model = Model(inputs=[anchor, positive, negative], outputs=loss)
model.compile(optimizer='Adam', loss='mean_absolute_error')
return model
But I'm getting a ValueError: Output tensors to a Model must be the output of a Keras `Layer`. Any suggestion?
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