The differences between some patterns.

抽象工厂与工厂方法模式介绍
博客介绍了抽象工厂模式和工厂方法模式。抽象工厂模式用于创建相互依赖的多个对象,工厂方法模式则提供创建对象的接口,并将创建工作推迟到子类。
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Abstract Factory and Factory Method

The Abstract factory pattern is used for creating many objects that are dependent on each other. The Factory Method pattern provides an interface for creating an object defers creation to either sub classes

 

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Anything-LLM

Anything-LLM

AI应用

AnythingLLM是一个全栈应用程序,可以使用商用或开源的LLM/嵌入器/语义向量数据库模型,帮助用户在本地或云端搭建个性化的聊天机器人系统,且无需复杂设置

2.2 Multi-Class CNN This section describes the multi-class implementation used to differentiate and classify four histopathological categories (ductal, lobular, mucinous, and papillary). The implementation followed the same engineering principles as the binary pipeline but used a categorical output and class-aware design choices to address the increased label complexity. 2.2.1 Data and Preprocessing The multi-class dataset was found on “BREAST CANCER HISTOPATHOLOGICAL DATABASE (BreakHisv1).” This dataset is modified to only include four types of malignant breast tumors, which are the four types of breast cancer used in training the model. The data structure is organized by breast cancer types, individual patient histopathology files, and the zoom factor of tissue screening. It is then assembled into a data frame of file paths and labels (class mapping: ductal carcinoma = 0, lobular carcinoma = 1, mucinous carcinoma = 2, papillary carcinoma = 3). The dataset contained 5,429 images distributed unevenly across classes (ductal: 3,451; mucinous: 792; lobular: 626; papillary: 560). An 80/20 train/validation split yielded 4,343 training and 1,086 validation samples. Images were loaded on-the-fly via ImageDataGenerator with rescaling (1./255) and light augmentation (rotation range=15, width/height shifts up to 0.1, horizontal flip). This on-the-fly strategy reduces memory pressure and preserves I/O efficiency for large image collections. Model architecture and training The multi-class CNN uses four convolutional blocks with progressively larger filters (32 → 64 → 128 → 256), each block containing Conv2D→BatchNormalization→MaxPooling2D→Dropout (0.25). After flattening, dense layers of 512 and 256 units with batch normalization and 0.5 dropout are applied; the final layer is Dense. The model is compiled with the Adam optimizer and categorical cross entropy loss to support mutually exclusive class prediction. Two experiment configurations exist in the codebase: (a) the original configuration (Batch Size = 32, Epochs = 30, LR = 0.001) that produced the run log included with this submission, and (b) a speed-optimized variant (Batch Size = 64, Epochs = 15, LR = 0.002) for faster iteration. Evaluation and Diagnostics Model evaluation uses a held-out generator and sklearn.metrics.classification_report and confusion_matrix to report per-class precision, recall, and F1-score; a seaborn heatmap visualizes the confusion matrix and matplotlib plots training/validation accuracy and loss. The run log for the original configuration reports a peak validation accuracy of 0.6529 (epoch 4) and a recorded training accuracy of 0.6967 (epoch 5) during the captured epochs; the full evaluation (per-class metrics and confusion-matrix counts) is then produced and included in the presentation. 2.2.4 Practical observations and improvements The dataset’s class imbalance (ductal dominance) is the main challenge for multi-class discrimination. Remedies to consider include class-weighted loss or oversampling of minority classes, focal loss to mitigate easy negative dominance, stronger augmentation targeted at minority classes, or stratified patch sampling. The model’s dense head (flatten → 512 units) yields ~19.4M parameters and can be prone to overfitting; replacing the flatten + dense stack with a GlobalAveragePooling2D followed by a small dense head or applying more aggressive dropout or L2 regularization may reduce overfitting. The literature suggests further gains from transfer learning (pretrained EfficientNet/ResNet backbones or DenTnet) and patch-level context models (BiLSTM on ordered patches) for cases where contextual arrangement of tissue is diagnostic. Step-by-step processing pipeline: Assemble DataFrame of filepaths and labels and report class counts. Create ImageDataGenerator instances for training (rescale + augmentation) and validation (rescale). Split into train/validation via train_test_split(..., stratify=df['label']) and create flow_from_dataframe generators. Build CNN (Conv blocks → Flatten → Dense → Softmax), compile with Adam + categorical_crossentropy. Train with model.fit using EarlyStopping and ReduceLROnPlateau callbacks; save the best model. Evaluate with a test generator: compute predicted classes, print classification_report, plot confusion matrix, and save accuracy/loss curves.分析multi-CNN模型的作用英文分析优点
最新发布
09-25
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