Mocha
v0.2.0
  • Training LeNet on MNIST
  • Alex’s CIFAR-10 tutorial in Mocha
  • Image Classification with Pre-trained Model
  • Pre-training with Stacked De-noising Auto-encoders
  • Mocha in the Cloud
  • Networks
  • Layers
  • Neurons (Activation Functions)
  • Initializers
  • Regularizers
  • Norm Constraints
  • Data Transformers
  • Solvers
  • Mocha Backends
  • Tools
  • Blob
  • Layer
Mocha
  • Docs »
  • Mocha Documentation
  • Edit on GitHub

Mocha Documentation¶

Mocha is a Deep Learning framework for Julia.

Tutorials¶

  • Training LeNet on MNIST
  • Alex’s CIFAR-10 tutorial in Mocha
  • Image Classification with Pre-trained Model
  • Pre-training with Stacked De-noising Auto-encoders
  • Mocha in the Cloud

User’s Guide¶

  • Networks
    • Overview
    • Network Architecture
    • Layer Implementation
    • Mocha Network Topology Tips
    • Debugging
  • Layers
    • Overview
    • Data Layers
    • Computation Layers
    • Loss Layers
    • Statistics Layers
    • Utility Layers
  • Neurons (Activation Functions)
  • Initializers
  • Regularizers
  • Norm Constraints
  • 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

Developer’s Guide¶

  • Blob
    • Constructors and Destructors
    • Accessing Properties of a Blob
    • Accessing Data of a Blob
  • Layer
    • Defining a Layer
    • Characterizing a Layer
    • Layer Computation API
    • Layer Parameters
    • Layer Activation Function

Indices and tables¶

  • Index
  • Module Index
  • Search Page
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