Mocha
v0.3.0
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
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Mocha Documentation
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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
Read the Docs
v: v0.3.0
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