Mocha
  • Training LeNet on MNIST
    • Preparing the Data
    • Defining the Network Architecture
    • Configuring Backend and Building Network
    • Configuring Solver
    • Coffee Breaks for the Solver
    • Training
    • Remarks
  • Alex’s CIFAR-10 tutorial in Mocha
    • Caffe’s Tutorial and Code
    • Preparing the Data
    • Computation and Loss Layers
    • Constructing the Network
    • Configuring the Solver
    • Training
  • Image Classification with Pre-trained Model
  • Networks
    • Overview
    • Network Architecture
    • Layer Implementation
    • Mocha Network Topology Tips
  • Layers
    • Overview
    • Data Layers
    • Computation Layers
    • Loss Layers
    • Statistics Layers
    • Utility Layers
  • Neurons (Activation Functions)
  • Initializers
  • Regularizers
  • Data Transformers
  • Solvers
    • General Solver Parameters
    • Solver Algorithms
    • Solver Coffee Breaks
  • Mocha Backends
    • Pure Julia CPU Backend
    • CPU Backend with Native Extension
    • CUDA Backend
  • Tools
    • Importing Trained Model from Caffe
    • Image Classifier
  • Blob
 
Mocha
  • Docs »
  • Layers
  • Edit on GitHub

Layers¶

  • Overview
  • Data Layers
  • Computation Layers
  • Loss Layers
  • Statistics Layers
  • Utility Layers
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