define softmax_loss

softmax_loss.py

def softmax_loss(vectors):
    anc, pos, neg = vectors

    c = 0.5

    anc = c * anc
    pos = c * pos 
    neg = c * neg 

    pos_sim = tf.reduce_sum((anc * pos), axis=-1, keepdims=True)
    neg_mul = tf.matmul(anc, neg, transpose_b=True)
    neg_sim = tf.log(tf.reduce_sum(tf.exp(neg_mul), axis=-1, keepdims=True))

    loss = tf.nn.relu(neg_sim - pos_sim)
    return loss