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Limited AI
  • Machine Learning
    • Linear Model Cheating Sheet
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    • General Linear Model 1
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    • Tree Based Methods Supplement
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    • Summary v2
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      • Layers
      • Extra:From Fully Connected Layers to Convolutions
      • Extra: Multiple Input and Multiple Output Channels
    • Advance CNN
      • Convolutional Neural Networks(LeNet)
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      • Densely Connected Networks(DenseNet)
      • Batch Normalization
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      • Seq Model
      • Raw Text to Seq
      • Language Models
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      • Backpropagation Through Time
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      • Gated Recurrent Units(GRU)
      • Long Short-Term Memory(LSTM)
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      • Encoder-Decoder Architecture
      • Seuqence to Sequence Learning(Seq2Seq)
    • Attention Mechanisms and Transformers
      • Queries, Keys, and Values
      • Attention is all you need
        • Attention and Kernel
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        • The Bahdanau Attention Mechanism
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        • Attention的实现
      • The Transformer Architecture
        • Extra Reading
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      • Large-Scaling Pretraning with Transformers
        • BERT vs OpenAI GPT vs ELMo
        • Decoder Model框架
        • Bert vs XLNet
        • T5& GPT& Bert比较
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        • Encoder vs Decoder Reference
      • Transformers for Vision
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    • NLP Pretraining
      • Word Embedding(word2vec)
        • Extra Reading
      • Approximate Training
      • Word Embedding with Global Vectors(GloVe)
        • Extra Reading
        • Supplement
      • Encoder(BERT)
        • BERT
        • Extra Reading
      • Decoder(GPT&XLNet&Lamma)
        • GPT
        • XLNet
          • XLNet架构
          • XLNet特点与其他比较
      • Encoder-Decoder(BART& T5)
        • BART
        • T5
  • GenAI
    • Introduction
      • GenAI Paper Must Read
      • GenAI六个阶段
    • Language Models Pre-training
      • Encoder-Decoder Architecture
      • Encoder Deep Dive
      • Decoder Deep Dive
      • Encoder VS Decoder
      • Attention Mechanism
      • Transformers
    • Example: Llama 3 8B架构
    • Fine-Tuning Generation Models
    • RAG and Adavance RAG
    • AI Agent
  • Statistics and Optimization
    • A/B testing
    • Sampling/ABtesting/GradientMethod
    • Gradient Decent Deep Dive
  • Machine Learning System Design
    • Extra Reading
    • Introduction
  • Responsible AI
    • AI Risk and Uncertainty
      • What is AI risk
      • General Intro for Uncertainty Quantification
      • Calibration
      • Conformal Prediction
        • Review the linear regression
        • Exchangeability
        • Split Conformal Prediction
        • Conformalized Quantile Regression
        • Beyond marginal coverage
        • Split Conformal Classification
        • Full Conformal Coverage
        • Cross-Validation +
        • Conformal Histgram Regression
    • xAI
      • SHAP value
  • Extra Research
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  1. Deep Learning
  2. Basic CNN

Extra: Multiple Input and Multiple Output Channels

Channel

  • 当我们添加通道时，我们的输入和隐藏的表示都变成了三维张量。例如，每个RGB输入图像具有3×h×w3\times h \times w3×h×w的形状。我们将这个大小为333的轴称为通道（channel）维度。本节将更深入地研究具有多输入和多输出通道的卷积核。

  • 用cic_ici​和c0c_0c0​分别表示输入和输出通道的数目，并让khk_hkh​和kwk_wkw​为卷积核的高度和宽度。为了获得多个通道的输出，我们可以为每个输出通道创建一个形状为ci×kh×kwc_i \times k_h \times k_wci​×kh​×kw​的卷积核张量，这样卷积核的形状是co×ci×kh×kwc_o \times c_i \times k_h \times k_wco​×ci​×kh​×kw​。在互相关运算中，每个输出通道先获取所有输入通道，再以对应该输出通道的卷积核计算出结果。

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Last updated 8 months ago