variable depth child networks. Figure 7-4 shows a sketch of their search procedure. It involves a controller which samples the search space to generate candidate architectures. The candidates are used as to the controller as reward signals. The controller incorporates the rewards signals in its gradient updates. Zoph et. al. modeled NAS as a reinforcement learning (RL) problem where the controller is a recurrent networks are the players whose rewards are determined by their performance on the target dataset. The controller model learns to generate better architectures as the search game progresses. Figure 7-4: An overview

The controller can be thought of as a unit that generates candidate models. These candidate models are evaluated and is used to update the state, and generate better candidate models A controller unit needs to be optimized (accuracy, precision, recall, etc.), and the feedback is passed back to the controller to make better suggestions in the future. NAS has been used to generate State of the Art networks

anything related to the inner working of the layer. Once the custom functionality is done, we can call the base class build function. Our custom build function is as follows: 8. Keras ― Customized Layer  Line 2 creates the weight corresponding to input shape and set it in the kernel. It is our custom functionality of the layer. It creates the weight using ‘normal’ initializer.  Line 6 calls Implement call method call method does the exact working of the layer during training process. Our custom call method is as follows: def call(self, input_data): return K.dot(input_data, self.kernel)

Transfer Learning Step1.Load data ▪ Inherit from torch.utils.data.Dataset ▪ __len__ ▪ __getitem__ Custom Dataset Preprocessing ▪ Image Resize ▪ 224x224 for ResNet18 ▪ Data Argumentation ▪ Rotate ▪ details https://indico.io/blog/exploring-computer-vision-transfer-learning/ In Conclusion ▪ Load custom data ▪ Train from scratch ▪ Transfer learning 下一课时 Thank You.

如果要加载的模型包含自定义层或其他自定义类或函数，则可以通过 custom_objects 参数将 它们传递给加载机制： from keras.models import load_model # 假设你的模型包含一个 AttentionLayer 类的实例 model = load_model('my_model.h5', custom_objects={'AttentionLayer': AttentionLayer}) model_from_yaml 的工作方式相同： from keras.models import model_from_json model = model_from_json(json_string, custom_objects={'AttentionLayer': AttentionLayer}) 3.3.7 为什么训练误差比测试误差高很多？ Keras 模型有两种模式：训练和测试。正则化机制，如 MobileNet 模 型， 你 需 要 导 入 自 定 义 对 象 relu6 和 DepthwiseConv2D 并通过 custom_objects 传参。 下面是示例代码： model = load_model('mobilenet.h5', custom_objects={ 'relu6': mobilenet.relu6, 'DepthwiseConv2D': mobilenet

be expensive when using very large models. def distillation_loss_fn(y_true_combined, y_pred): """Custom distillation loss function.""" # We will split the y tensor to extract the ground-truth and the model_pred) opt = keras.optimizers.Adam(learning_rate=learning_rate) # Compile the model with the custom loss function and metric. model.compile( loss=distillation_loss_fn, metrics=[categorical_accuracy]

support Experimental support Experimental support Supported planned post 2.0 Supported Custom training loop Experimental support Experimental support Support planned post 2.0 Support

GitlabCI) • 容器系统调用栈深，需要仔细验证操作系统，内核及异构设备驱动的兼容性 • Kubernetes对NUMA、异构计算、存储设备的调度能力待加强 1.6 nvidia/gpu custom scheduler 1.8 local-volume 1.10 CPU manager Device plugin 1.9 volume-awared scheduling Go语言在高性能系统中的实践经验

Amazon API Gateway AWS Lambda 3: Translate REST response into natural language Mobile Hub Custom Connector 2: Invoke a SaaS application or an existing business application Business Application

be converted during inference. Because this lookup operation is very simple, it is easy to create custom kernels for them, such as demonstrated here for Tensorflow and here for TFLite. We would also encourage