Lossless triplet loss

lossless_triplet_loss.py

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