'the', 'in', 'of', 'is', 'a', 'and', 'was', 'by'] Notice that the first two elements are an empty string (a reserved token for padding) and a 'UNK' token (a token reserved for words which are not found independently. The output of this layer is a vector with indices of tokens in the vocabulary. Our string of interest is ‘efficient deep learning’, but we will add a random token at the end that we are confident vectorization layer ensures that every input string is transformed into a sequence of integer ids. The maximum sequence length is 100. Therefore, for every input string, the embedding layer would receive 100

from_config(config)  to_json(): Returns the model as an json object. >>> json_string = model.to_json() >>> json_string '{"class_name": "Sequential", "config": {"name": "sequential_10", "layers": [{"class_name": model_from_json new_model = model_from_json(json_string)  to_yaml(): Returns the model as a yaml string. >>> yaml_string = model.to_yaml() >>> yaml_string 'backend: tensorflow\nclass_name: Sequential\nconfig:\n 58 from keras.models import model_from_yaml new_model = model_from_yaml(yaml_string) Summarise the model Understanding the model is very important phase to properly use it for training

3.3.6.2 只保存/加载模型的结构 如果您只需要保存模型的结构，而非其权重或训练配置项，则可以执行以下操作： # 保存为 JSON json_string = model.to_json() # 保存为 YAML yaml_string = model.to_yaml() 生成的 JSON/YAML 文件是人类可读的，如果需要还可以手动编辑。 你可以从这些数据建立一个新的模型： # import model_from_json model = model_from_json(json_string) # 从 YAML 重建模型： from keras.models import model_from_yaml model = model_from_yaml(yaml_string) 3.3.6.3 只保存/加载模型的权重 如果您只需要 模型的权重，可以使用下面的代码以 HDF5 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

(figure 2-1 bottom). Each leaf node in the tree is a symbol, and the path to that symbol is the bit-string assigned to it. This allows us to encode the given data in as few bits as possible, since the most the convert() method on the converter object generates a tflite model file content string. We referred to this string as model_content earlier. The converter object also supports weight and activation

Int array IntTensor of size [d1, d2 ,…] Float array FloatTensor of size [d1, d2, …] string -- How to denote string ▪ One – hot ▪ [0, 1, 0, 0, …] ▪ Embedding ▪ Word2vec ▪ glove Data type Type check

'object', 'properties': { 'location': { 'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA', }, 'unit': { 'type': 'string', 'enum': ['celsius', 'fahrenheit'] }, }, 'required': return the image URL drawn based on text information.' parameters = [{ 'name': 'prompt', 'type': 'string', 'description': 'Detailed description of the desired image content, in English �→', 'required':

that each string produces. Unlike the guitar which has a few knobs, the hyperparameter search space can be quite large. Moreover, unlike a guitar knob which is associated with a single string, hyperparameters

add/softmax/dropout/erosion2d ���� random_normal/random_shuffle/multinomial/random_gamma ����� string_to_hash_bucket/reduce_join/substr/encode_base64 ������ encode_png/resize_images/rot90/hsv_to_rgb/adjust_gamma

out几率，取值范围为 [0, 1]，默认为 0.。 emb_dropout：float类型参数，进行Embedding操作 时Dropout几率，取值范围为[0, 1]，默认为0。 pool：string类型参数，取值为 cls或者 mean 。 33 参考文献 1. https://jalammar.github.io/illustrated-transformer 2. https://www

03 YOLO算法 04 Faster RCNN算法 4 1.目标检测概述 分类（Classification） 即是将图像结构化为某一 类别的信息，用事先确定 好的类别(string)或实例ID 来描述图片。这一任务是 最简单、最基础的图像理 解任务，也是深度学习模 型最先取得突破和实现大 规模应用的任务。 检测（Detection） 分类任务关心整体，给出的 是整张图片的内容描述，而