callbacks=None, validation_data=None, validation_steps=None, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0) 使用 Python 生成器逐批生成的数据，按批次训练模型。 生成 Sequence：如果未指定，将使用 len(generator) 作为步数。 • class_weight: 将类别映射为权重的字典。 • max_queue_size: 生成器队列的最大尺寸。 • workers: 使用的最大进程数量。 • use_multiprocessing: 如果 True，则使用基于进程的多线程。请注意，因为此实现依赖于多 进程，所以不应将不可传递的参数传递给生成器，因为它们不能被轻易地传递给子进程。 4.2.3.9 evaluate_generator evaluate_generator(self, generator, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False) 在数据生成器上评估模型。 这个生成器应该返回与 test_on_batch 所接收的同样的数据。 参数 • generator:

256 def get_dataloader_workers(): #@save """使用4个进程来读取数据""" return 4 train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()) 我们看一下读取训练数据所需的时间。 timer DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()), data.DataLoader(mnist_test, batch_size, shuffle=False, num_workers=get_dataloader_workers())) 下面，我们通过指定resize参数来测试load_data_f 公共抽象值得使用的原因，公共抽象即重 新定义具有更新语义的键－值存储（key‐value store）的抽象。 在许多工作节点和许多GPU中，梯度i的计算可以定义为 gi = � k∈workers � j∈GPUs gijk, (12.7.1) 其中gijk是在工作节点k的GPUj上拆分的梯度i的一部分。这个运算的关键在于它是一个交换归约（commu‐ tative reduct

shuffle=True, num_workers=8) 其中 dataset 对象就是 torch.utils.data.Dataset 类实例，已经完成了随机裁剪、标准化等操 作，并通过 DataLoader 类实现批加载，其中 batch_size 创建各个交互环境 workers = [Worker(self.server, self.opt, res_queue, i) for i in range(multiprocessing.cpu_count())] for i, worker in enumerate(workers): print("Starting .append(reward) else: # 结束标志 break [w.join() for w in workers] # 等待线程退出 14.6 小结 本章介绍了强化学习的问题设定和基础理论，并引出解决强化学习问题的两个系列算 法：策略梯度方法和值函数方法。策略梯度方法直接优化策略模型，简单直接，但是采样

batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False) Here, all arguments are optional except the first argument, which

Install Docker. ‣ For NVIDIA DGX™ users, see Preparing to use NVIDIA Containers Getting Started Guide. ‣ For non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation that you have access and can log in to the NGC container registry. Refer to NGC Getting Started Guide for more information. The deep learning frameworks, the NGC Docker containers, and the deep learning examples can be found here. For more information about AMP, see the Training With Mixed Precision Guide. Tensor Core Examples The tensor core examples provided in GitHub and NGC focus on achieving the

com/applied-deep-learning-part-3-autoencoders- 1c083af4d798 https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-1- autoencoder-d9a5f8795af4 How to Train? PCA V.S. Auto-Encoders Adversarial AutoEncoders ▪ Distribution of hidden code https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-latent-space-with-adversarial-2d53a6f8a4f9 Adversarial Adversarial AutoEncoders ▪ Give more details after GAN https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-latent-space-with-adversarial-2d53a6f8a4f9 Another Approach:

well as time-consuming. However, as initial pointers you can refer to this guide for pre-training BERT in Keras, and this guide for some optimizations to make it efficient. Also consider going through the

languages (like Java for Android or C++ for iOS and other platforms) for inference. The authoritative guide for TFLite inference is available on the tensorflow website. def tflite_model_eval(model_content As mentioned earlier, the tflite evaluation is a boiler-plate code. You can refer to the TFLite guide for more details. We start the model conversion by creating a converter object using the from_keras_model()

effects of transformations visually. The above list is not exhaustive, rather we have used it as a guide to help make better transformation choices. A few other commonly used techniques are contrast augmentation family to decide whether it is a good decision. We rely on their perspectives and life experiences to guide us through the process. Similarly, when ensembling we hope that each individual model would learn

Convolution Moving window Several kernels Animation https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural- networks-260c2de0a050 Notation Input_channels: Kernel_channels: 2 ch Kernel_size: