inbatch softmax loss 代码实现

def inbatch_softmax_loss(user_pred_vector, item_pred_vector, item_id, labels):
    labels = tf.linalg.diag(tf.reshape(tf.ones_like(labels),[-1]))
    diff = tf.expand_dims(item_id,-1) - tf.expand_dims(item_id,0) + labels
    same_mask = tf.where(tf.math.abs(diff) < 0.5,tf.zeros_like(diff,dtype=tf.float32),tf.ones_like(diff,dtype=tf.float32))
    logits = tf.matmul(user_pred_vector,item_pred_vector,transpose_b=True)
    
    return tf.losses.softmax_cross_entropy(labels,logits,label_smoothing = 0.05)