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
Pre-training with Stacked De-noising Auto-encoders
(Stacked) Denoising Auto-encoders
Experiment Configuration
Pre-training
Fine Tuning
Comparison with Random Initialization
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
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
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
Mocha
Docs
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Tools
Edit on GitHub
Tools
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Importing Trained Model from Caffe
Overview
Exporting Caffe’s Snapshot to HDF5
Importing HDF5 Snapshot to Mocha
Mocha’s HDF5 Snapshot Format
Export Caffe’s Mean File
Image Classifier
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