practitioners have to do. Apart from saving humans time, it also helps by reducing the bias that manual decisions might introduce when designing efficient networks. Automation techniques can help improve Automated Hyper-Param Optimization (HPO) is one such technique that can be used to replace / supplement manual tweaking of hyper-parameters like learning rate, regularization, dropout, etc. This relies on search

out gradients after each update • t.grad.zero_() *Assume 't' is a tensor Autograd (continued) • Manual Weight Update - example Optimizer • Optimizers (optim package) • Adam, Adagrad, Adadelta, SGD etc

greater x.le/x.gt np.greater_equal/np.equal/np.not_equal x.ge/x.eq/x.ne 随机种子 np.random.seed torch.manual_seed 1.Tensors张量的概念 10  Python、PyTorch 1.x与TensorFlow2.x的比较 类别 Python PyTorch 1+ TensorFlow

performance drop for GNMT training ‣ On Volta: ‣ Up to 20% performance drop for Tacotron training. ‣ Manual synchronization is required in CUDA graphs workloads between graph replays. ‣ The PyTorch container 21.04: ‣ On NVIDIA Ampere Architecture GPUs: ‣ Up to 17% performance drop for VGG16 training ‣ Manual synchronization is required in CUDA graphs workloads between graph replays. ‣ The DLProf TensorBoard Up to 20% performance drop in MaskRCNN training ‣ Up to 15% performance drop in VGG16 training ‣ Manual synchronization is required in CUDA graphs workloads between graph replays. ‣ The DLProf TensorBoard

world, we must automate the embedding table generation because of the high costs associated with manual embeddings. One example of an automated embedding generation technique is the word2vec family of

clear_session keras.backend.clear_session() 销毁当前的 TF 图并创建一个新图。 有用于避免旧模型/网络层混乱。 manual_variable_initialization keras.backend.manual_variable_initialization(value) 设置变量手动初始化的标志。 这个布尔标志决定了变量是否应该在实例化时初始化（默认），或者用户是否应该自己处理

epochs = 10 # 最大训练的 epoch 次数 # GPU 运算设备 device = torch.device('cuda') # 设置随机种子 torch.manual_seed(1234) 预览版202112 第 15 章 自定义数据集 8 # 创建训练集 Dataset 对象 train_db = Pokemon('pokemon'