小编max*_*pod的帖子

我训练了一个使用三重态损失的连体神经网络。这很痛苦，但我想我做到了。然而，我很难理解如何用这个模型进行评估。

国家神经网络：

def triplet_loss(y_true, y_pred):
    margin = K.constant(1)
    return K.mean(K.maximum(K.constant(0), K.square(y_pred[:,0]) - 0.5*(K.square(y_pred[:,1])+K.square(y_pred[:,2])) + margin))

def euclidean_distance(vects):
    x, y = vects
    return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))

anchor_input = Input((max_len, ), name='anchor_input')
positive_input = Input((max_len, ), name='positive_input')
negative_input = Input((max_len, ), name='negative_input')

Shared_DNN = create_base_network(embedding_dim = EMBEDDING_DIM, max_len=MAX_LEN, embed_matrix=embed_matrix)

encoded_anchor = Shared_DNN(anchor_input)
encoded_positive = Shared_DNN(positive_input)
encoded_negative = Shared_DNN(negative_input)

positive_dist = Lambda(euclidean_distance, name='pos_dist')([encoded_anchor, encoded_positive])
negative_dist = Lambda(euclidean_distance, name='neg_dist')([encoded_anchor, encoded_negative])
tertiary_dist = Lambda(euclidean_distance, name='ter_dist')([encoded_positive, encoded_negative])

stacked_dists = Lambda(lambda vects: K.stack(vects, axis=1), …