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Lossless triplet loss
def lossless_triplet_loss(y_true, y_pred, N = 3, beta=N, epsilon=1e-8):
"""
Implementation of the triplet loss function
Arguments:
y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.
y_pred -- python list containing three objects:
anchor -- the encodings for the anchor data
positive -- the encodings for the positive data (similar to anchor)
negative -- the encodings for the negative data (different from anchor)
N -- The number of dimension
beta -- The scaling factor, N is recommended
epsilon -- The Epsilon value to prevent ln(0)
Returns:
loss -- real number, value of the loss
"""
anchor = tf.convert_to_tensor(y_pred[:,0:N])
positive = tf.convert_to_tensor(y_pred[:,N:N*2])
negative = tf.convert_to_tensor(y_pred[:,N*2:N*3])
# distance between the anchor and the positive
pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,positive)),1)
# distance between the anchor and the negative
neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,negative)),1)
#Non Linear Values
# -ln(-x/N+1)
pos_dist = -tf.log(-tf.divide((pos_dist),beta)+1+epsilon)
neg_dist = -tf.log(-tf.divide((N-neg_dist),beta)+1+epsilon)
# compute loss
loss = neg_dist + pos_dist
return loss
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