Triplet Loss in Keras/Tensorflow backend

import tensorflow as tf tf.set_random_seed(1) from keras.models import Model from keras.layers import Input, merge from keras.optimizers import Adam ALPHA = 0.2 # Triplet Loss Parameter # Source: https://github.com/davidsandberg/facenet/blob/master/src/facenet.py def triplet_loss(x): anchor, positive, negative = x pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1) neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1) basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), ALPHA) loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0) return loss # Builds an embedding for each example (i.e., positive, negative, anchor) # Then calculates the triplet loss between their embedding. # Then applies identity loss on the triplet loss value to minimize it on training. def build_model(input_shape): # Standardizing the input shape order K.set_image_dim_ordering('th') positive_example = Input(shape=input_shape) negative_example = Input(shape=input_shape) anchor_example = Input(shape=input_shape) # Create Common network to share the weights along different examples (+/-/Anchor) embedding_network = create_embedding_network(input_shape) positive_embedding = embedding_network(positive_example) negative_embedding = embedding_network(negative_example) anchor_embedding = embedding_network(anchor_example) loss = merge([anchor_embedding, positive_embedding, negative_embedding], mode=triplet_loss, output_shape=(1,)) model = Model(inputs=[anchor_example, positive_example, negative_example], outputs=loss) model.compile(loss='mean_absolute_error', optimizer=Adam()) return model # When fitting the model (i.e., model.fit()); use as an input [anchor_example, # positive_example, negative_example] in that order and as an output zero. # The reason to use the output as zero is that you are trying to minimize the # triplet loss as much as possible and the minimum value of the loss is zero.