通过 Dataset.shuffle(buffer_size)工具可以设置 Dataset 对象随机打散数据之间的顺序， 防止每次训练时数据按固定顺序产生，从而使得模型尝试“记忆”住标签信息，代码实现 如下： train_db = train_db.shuffle(10000) # 随机打散样本，不会打乱样本与标签映射关系 其中，buffer_size 参数指定缓冲池的大小，一般设置为一个较大的常数即可。调用 self.actor = Actor() # 创建 Actor 网络 self.critic = Critic() # 创建 Critic 网络 self.buffer = [] # 数据缓冲池 self.actor_optimizer = optimizers.Adam(1e-3) # Actor 优化器 self.critic_optimizer total += reward # 累积激励 if done: # 合适的时间点训练网络 if len(agent.buffer) >= batch_size: agent.optimize() # 训练网络 break 网络优化 当缓冲池达到一定容量后，通过

during in-place ncclReduce() operations, whereas normally only the "root" (target) device's output buffer should be modified. This is fixed in later versions of NCCL, as will be packaged in later versions all GPUs to the same values, or use out-of-place ncclReduce(), wherein the output buffer is distinct from the input buffer. PyTorch RN-08516-001_v23.07 | 358 Chapter 75. PyTorch Release 17.05 The during in-place ncclReduce() operations, whereas normally only the "root" (target) device's output buffer should be modified. This is fixed in later versions of NCCL, as will be packaged in later versions

16 def train(model, tds, vds, epochs, callbacks=[]): tds = tds.prefetch(buffer_size=tf.data.AUTOTUNE) vds = vds.prefetch(buffer_size=tf.data.AUTOTUNE) if vds else None history = model.fit(tds, validation_data=vds

EarlyStopping def train(model, tds, vds, epochs=100): tds = tds.prefetch(buffer_size=tf.data.AUTOTUNE) vds = vds.prefetch(buffer_size=tf.data.AUTOTUNE) cb_checkpoint = ModelCheckpoint( str(CHKPT_TMPL)