def triplet_loss(y_true, y_pred, alpha = 0.2):
    """
    Implementation of the triplet loss as defined by formula (3)
    
    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 images, of shape (None, 128)
            positive -- the encodings for the positive images, of shape (None, 128)
            negative -- the encodings for the negative images, of shape (None, 128)
    
    Returns:
    loss -- real number, value of the loss
    """
    
    anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
    
    # Compute the (encoding) distance between the anchor and the positive
    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)))
    # Compute the (encoding) distance between the anchor and the negative
    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)))
    # Subtract the two previous distances and add alpha.
    basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
    # Take the maximum of basic_loss and 0.0. Sum over the training examples.
    loss = tf.maximum(tf.reduce_mean(basic_loss), 0.0)

    return loss