cross-entropy loss. We would refer you to the SimCLR paper for more details about the chosen loss functions and other alternatives considered. Once the desired test loss is achieved, the projection head optimizing non-convex functions, where multiple local minima might exist. Typical deep learning objective functions are non-convex too, and directly working with these functions might lead to the optimizer complexity you want to introduce in the training. Figure 6-12 shows multiple examples of pacing functions. The x-axis is the training iteration i.e. the variable described above, and the y-axis is the fraction

M 层和 Embedding 层都能被平稳地训练。 快速开始 19 auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out) 此时，我们将辅助输入数据与 LSTM 层的输出连接起来，输入到模型中： auxiliary_input = Input(shape=(5,), name='aux_input') name='aux_input') x = keras.layers.concatenate([lstm_out, auxiliary_input]) # 堆叠多个全连接网络层 x = Dense(64, activation='relu')(x) x = Dense(64, activation='relu')(x) x = Dense(64, activation='relu')(x) # 最后添加主要的逻辑回归层 name='main_output')(x) 然后定义一个具有两个输入和两个输出的模型： model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output]) 现在编译模型，并给辅助损失分配一个 0.2 的权重。如果要为不同的输出指定不同的 loss_weights 或 loss，可以使用列表或字典。在这里，我们给

取 pd.read_sql() | 从 SQL 表 或 数 据 库 读 取 pd.read_json() | 从JSON格式的URL或文件读取 pd.read_clipboard() | 从剪切板读取 将DataFrame写入⽂件 df.to_csv() | 写入CSV文件 df.to_excel() | 写入Excel文件 df.to_sql() | 写入SQL表或数据库 df.to_json()

取 pd.read_sql() | 从 SQL 表 或 数 据 库 读 取 pd.read_json() | 从JSON格式的URL或文件读取 pd.read_clipboard() | 从剪切板读取 将DataFrame写入⽂件 df.to_csv() | 写入CSV文件 df.to_excel() | 写入Excel文件 df.to_sql() | 写入SQL表或数据库 df.to_json()

和整理 样本 训练 模型 模型评估 AVA深度学习平台 Caching IO Distributed System Docker Orchestration Storage HDFS SQL NoSQL Caffe MXNet Tensorflow Data Clean Iterative training Semi-supervised Labeling Incremental

方案复杂 图像 搜索 推荐 语音 视频理解 NLP 广告 CNN RNN GNN MLP Tensorflow PyTorch Parameter Server MPI TreeModel SQL MapReduce Blink  场景丰富: 图像/视频/推荐/搜索  大数据+大模型: Model Zoo  跨场景+跨模态  开箱即用: 封装复杂性  白盒化, 可扩展性强

configuration inside keras.json file. We can perform some pre-defined operations to know backend functions. 3. Keras ― Backend Configuration Keras 10 Theano Theano is an open source sub-classing Keras models. Core Modules Keras also provides a lot of built-in neural network related functions to properly create the Keras model and Keras layers. Some of the function are as follows:  many activation function like softmax, relu, etc.,  Loss module - Loss module provides loss functions like mean_squared_error, mean_absolute_error, poisson, etc.,  Optimizer module - Optimizer

jhj(ω) (12) In fact, L(ω, α, β ) can be treated as a weighted sum of the objective and constraint functions. αi is the so-called Lagrange multiplier associated with gi(ω) ≤ 0, while β i is the one associated supposed to the original constrained minimization problem); ii) G is an infimum of a set of affine functions and thus is a concave function regardless of the original problem; iii) G can be −∞ for some α and Karush-Kuhn-Tucker (KKT) Conditions We assume that the objective function and the inequality constraint functions are differentiable. Again, let ω∗ and (α∗, β ∗) be any primal and dual optimal points, respectively

Testing Neural Networks in Pytorch ● Dataset & Dataloader ● Tensors ● torch.nn: Models, Loss Functions ● torch.optim: Optimization ● Save/load models Prerequisites ● We assume you are already familiar mean() ● Addition z = x + y ● Subtraction z = x - y ● Power y = x.pow(2) Common arithmetic functions are supported, such as: Tensors – Common Operations Tensors – Common Operations ● Transpose: official documentation for more information on data types. Tensors – PyTorch v.s. NumPy ● Many functions have the same names as well PyTorch NumPy x.reshape / x.view x.reshape x.squeeze() x.squeeze()

1: send the conversation and available functions to the model messages = [{ 'role': 'user', 'content': "What's the weather like in San Francisco?" }] functions = [{ (续下页) 38 Chapter 1. 文档 Qwen (接上页) print('# Assistant Response 1:') responses = [] for responses in llm.chat(messages=messages, functions=functions, stream=True): print(responses) messages.extend(responses) # extend conversation with assistant's function # Note: the JSON response may not always be valid; be sure to handle errors available_functions = { 'get_current_weather': get_current_weather, } # only one function in this example, but you