agg loss for multitask pipe

joint_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


pos_loss = tf.keras.losses.categorical_crossentropy(tgs_lbl_inp, seq_cls)
  
tx_loss = tf.keras.losses.binary_crossentropy(inp_tx_tag, tx_out)
  
triplet_loss = tf.keras.layers.Lambda(loss, name='loss')([anc_enc, pos_enc, neg_enc])
  
mean_op = tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x))
    
avg_loss = cls_w[0]* mean_op(loss) + cls_w[1]* mean_op(pos_loss) + cls_w[2]*tx_loss