事件2-XXXX 人物出现:id1, id2 场景二 … 用户行 为 用户数 据 推理结 果 推理服务 数据抽样 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

collaborative filtering with implicit feedback. The training data for this model should contain binary information about whether a user interacted with a specific item. NCF was first described by Xiangnan collaborative filtering with implicit feedback. The training data for this model should contain binary information about whether a user interacted with a specific item. NCF was first described by Xiangnan collaborative filtering with implicit feedback. The training data for this model should contain binary information about whether a user interacted with a specific item. NCF was first described by Xiangnan

Fraudulent (Yes/No)? Tumor: Malignant/Benign? The classification result can be represented by a binary variable y ∈ {0, 1} y = � 0 : “Negative Class” (e.g., benign tumor) 1 : “Positive Class” (e.g. but we would like to predict only a small number of discrete values (instead of continuous values) Binary classification problem: y ∈ {0, 1} where 0 represents negative class, while 1 denotes positive class categorized into Transformation to binary Extension from binary Hierarchical classification Feng Li (SDU) Logistic Regression September 20, 2023 24 / 29 Transformation to Binary One-vs.-rest (one-vs.-all,

2.10 sparse_categorical_crossentropy . . . . . . . . . . . . . . . . . . . . . . . . 135 7.2.11 binary_crossentropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.2.12 kullback_leibler_divergence 8.2 可使用的评价函数 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 8.2.1 binary_accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 8.2.2 categorical_accuracy loss='categorical_crossentropy', metrics=['accuracy']) # 二分类问题 model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) # 均方误差回归问题 model.compile(optimizer='rmsprop', loss='mse')

data = HDF5Matrix('data.hdf5', 'data') to_categorical It is used to convert class vector into binary class matrix. >>> from keras.utils import to_categorical >>> labels = [0, 1, 2, 3, 4, 5, 6, logcosh  huber_loss  categorical_crossentropy  sparse_categorical_crossentropy  binary_crossentropy  kullback_leibler_divergence  poisson  cosine_proximity  is_categorical_crossentropy Keras provides quite a few metrics as a module, metrics and they are as follows:  accuracy  binary_accuracy  categorical_accuracy  sparse_categorical_accuracy  top_k_categorical_accuracy

properties of the English language. The authors also found that fine-tuning such a pre-trained model for a binary classification problem (IMDb dataset) required only 100 labeled examples ( less labeled examples distillation might not be much better than using one-hot labels. This is particularly an issue with binary classification problems, which are very common. Subclass Distillation is a way of avoiding this faster convergence as well as improved accuracy on binary classification tasks, when compared to conventional distillation. For example on the natively binary classification task like Criteo’s ad click prediction

虽然 Categorical / Binary CE 是更常用的损失函数，不过他们都是 CE 的变体。 CE 定义如下： 对于二分类问题 (C‘=2) ，CE 定义如下： Categorical CE Loss（Softmax Loss） 常用于输出为 One-hot 向量的多类别分类（Multi-Class Classification）模型。 Binary CE Loss（Sigmoid Loss（Sigmoid CE Loss） 与 Softmax Loss 不同，Binary CE Loss 对于每个向量分量（class）都是独立 的，这意味着每个向量分量计算的损失不受其他分量的影响。 因此，它常被用于多标签分类（Multi-label classification）模型。 “Hello TensorFlow” Try it 模型训练过程分析 模型训练过程 学习率（Learning rate）

▪ for continuous: ? = ?? + ? ▪ for probability output: ? = ? ?? + ? ▪ ?: ??????? ?? ???????? Binary Classification ▪ interpret network as ?: ? → ? ? ?; ? ▪ output ∈ 0, 1 ▪ which is exactly what ▪ Controversial! ▪ MSE => regression ▪ Cross Entropy => classification 0.7 0.3 0.7 MSE CEL Binary Classification ▪ ?: ? → ? ? = 1 ? ▪ if ? ? = 1 ? > 0.5, predict as 1 ▪ else predict as 0 ▪ minimize

), ]) adam = optimizers.Adam(learning_rate=LEARNING_RATE) model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy']) return model model = create_model() model.summary() model the teacher models to generate soft-labels for the training samples. The soft labels replace the binary values in the ground-truth labels with the probabilities of the sample image belonging to each class generate `soft-labels' for the student, which gives the student more information than just hard binary labels. The student is trained using the regular cross-entropy loss with the hard labels, as well

activation='relu')) model.add(Dense(1, activation='sigmoid')) # 创建最末层 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # 模型装配与训练 history = model.fit(X_train Dropout(rate=0.5)) model.add(Dense(1, activation='sigmoid')) # 输出层 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # 模型装配 # 训练 history = model l2(_lambda))) # 输出层 model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # 模型装配 return model 在保持网络结构不变的条件下，我们通过调节正则化系数