tensorflow --batch内负采样

class NegativeCosineLayer():
    """ 自定义batch内负采样并做cosine相似度的层 """
    """
    负采样原理：
    query_input.shape = [batch_size, dim]
    doc_input.shape = [batch_size, dim]
    默认 query点击该doc。每个点击的item， 随机采集NEG个item负样本
    1. 假设每个正样本要采集N个负样本。
    2. 分N次采集负样本，每次采集batch_size大小的样本。即每次给所有的正样本采集到一个负样本。shape = [batch_size, dim]
    3. N次采集的负样本与之前正样本进行concat。shape=[batch_size * NEG, dim]
    4. 对应的query特征进行扩展。(batch_size * NEG, dim)
    5. query特征按行计算Norm。
    6. item特征按行计算Norm。
    7. query * item/｜query｜*｜item｜
    """
    #
    def __init__(self, NEG, t):
        self.NEG = NEG
        self.t = t
    def __call__(self, inputs):
        def _cosine(x):
            query_encoder, doc_encoder = x
            doc_encoder_fd = doc_encoder
            print(query_encoder shape, query_encoder.shape)
            print(doc_encoder shape, doc_encoder.shape)
            print(doc_encoder_fd shape, doc_encoder_fd.shape)
            #
            for i in range(self.NEG):
                ss = tf.gather(doc_encoder, tf.random.shuffle(tf.range(tf.shape(doc_encoder)[0])))
                print("---ss shape", tf.range(tf.shape(doc_encoder)[0]), ss.shape)
                doc_encoder_fd = tf.concat([doc_encoder_fd, ss], axis=0)  # []
            print(doc_encoder_fd shape, doc_encoder_fd.shape)
            query_norm = tf.tile(tf.sqrt(tf.reduce_sum(tf.square(query_encoder), axis=1, keepdims=True)), [self.NEG + 1, 1])
            print(query_norm shape, query_norm.shape)
            doc_norm = tf.sqrt(tf.reduce_sum(tf.square(doc_encoder_fd), axis=1, keepdims=True))
            print(doc_norm shape, doc_norm.shape)
            query_encoder_fd = tf.tile(query_encoder, [self.NEG + 1, 1])
            print(query_encoder_fd shape, query_encoder_fd.shape)
            prod = tf.reduce_sum(tf.multiply(query_encoder_fd, doc_encoder_fd, name="sim-multiply"), axis=1,
                                 keepdims=True)
            print(prod shape, prod.shape)
            norm_prod = tf.multiply(query_norm, doc_norm)
            print(prod norm_prod, norm_prod.shape)
            cos_sim_raw = tf.truediv(prod, norm_prod)
            cos_sim = tf.transpose(tf.reshape(tf.transpose(cos_sim_raw), [self.NEG + 1, -1])) * 20
            print("-----cos_sim", cos_sim.shape, cos_sim)
            #
            #prob = tf.nn.softmax(cos_sim, name="sim-softmax")
            prob = (tf.exp(cos_sim) / self.t) / tf.reduce_sum(tf.exp(cos_sim) / self.t, axis=0)  # t为平滑参数
            print("-----prob", prob.shape, prob)
            hit_prob = tf.slice(prob, [0, 0], [-1, 1], name="sim-slice")
            print("-----hit_prob", hit_prob.shape, hit_prob)
            loss = -tf.reduce_mean(tf.math.log(hit_prob), name="sim-mean")
            #loss = -tf.reduce_sum(tf.math.log(hit_prob), name="sim-mean")
            print("-----loss", loss.shape, loss)
            return loss

        #
        #output_shape = (1,)
        #value = tf.keras.layers.Lambda(_cosine, output_shape=output_shape)([inputs[0], inputs[1]])
        loss = _cosine([inputs[0], inputs[1]])
        loss = tf.expand_dims(loss, 0)
        return loss

if __name__ == __main__:
   query_encoder = tf.constant([[1, 2],
                      [2, 1],
                      [3, 2],
                      [4, 1]
                      ], dtype=tf.float16)
   doc_encoder = tf.constant([[1, 2],
                      [2, 1],
                      [3, 2],
                      [4, 1]
                      ], dtype=tf.float16)
   cos_sim = NegativeCosineLayer()([query_encoder, doc_encoder])