p-NET损失函数图像代码

p-NET是一种常用的人脸识别模型，其损失函数包括三部分：triplet loss、softmax loss和center loss。其中triplet loss和center loss用于增强特征的区分度，softmax loss用于提高分类准确度。以下是p-NET损失函数的图像代码实现：

import tensorflow as tf

def p_net_loss(y_true, y_pred, alpha = 0.5, n_classes = 10, n_features = 128):

    # Triplet Loss
    anchor, positive, negative = y_pred[:, :n_features], y_pred[:, n_features:2*n_features], y_pred[:, 2*n_features:]
    pos_dist = tf.reduce_sum(tf.square(anchor - positive), axis = 1)
    neg_dist = tf.reduce_sum(tf.square(anchor - negative), axis = 1)
    triplet_loss = tf.reduce_mean(tf.maximum(pos_dist - neg_dist + alpha, 0))

    # Softmax Loss
    softmax_w = tf.Variable(tf.random.truncated_normal([n_features, n_classes], stddev = 0.1))
    softmax_b = tf.Variable(tf.constant(0.1, shape = [n_classes]))
    logits = tf.matmul(y_pred, softmax_w) + softmax_b
    softmax_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_true, logits = logits))

    # Center Loss
    centers = tf.Variable(tf.zeros([n_classes, n_features]), dtype=tf.float32)
    label = tf.argmax(y_true, axis = 1)
    centers_batch = tf.gather(centers, label)
    center_loss = tf.reduce_mean(tf.square(y_pred - centers_batch))
    center_update_op = tf.scatter_sub(centers, label, alpha * (centers_batch - y_pred))

    total_loss = softmax_loss + triplet_loss + center_loss
    return total_loss

其中，y_true为标签，y_pred为模型输出，alpha为triplet loss的margin，n_classes为分类数目，n_features为特征维度。在函数中，首先使用y_pred计算triplet loss，然后使用softmax loss和center loss提高分类准确度和区分度。其中，softmax loss使用随机初始化的权重和偏置，center loss使用每个类别的中心点来计算。最后，将三个部分的损失相加得到总损失total_loss，并返回。