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MetaTF 2.3.0
  • Overview
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  • User guide
    • Getting started
      • For beginners
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    • Akida user guide
      • Introduction
        • Akida layers
        • Input Format
        • A versatile machine learning framework
      • The Sequential model
        • Specifying the model
        • Accessing layer parameters and weights
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        • Input layer types
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      • Model Hardware Mapping
        • Devices
        • Model mapping
        • Advanced Mapping Details and Hardware Devices Usage
        • Performances measurement
      • Using Akida Edge learning
        • Learning constraints
        • Compiling a layer
    • CNN2SNN toolkit
      • Overview
        • Conversion workflow
        • Typical training scenario
        • Design compatibility constraints
        • Quantization compatibility constraints
        • Command-line interface
      • Layers Considerations
        • Supported layer types
        • CNN2SNN Quantization-aware layers
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        • First Layers
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      • Tips and Tricks
    • Akida models zoo
      • Overview
      • Command-line interface for model creation
      • Command-line interface for model training
        • UTK Face training
        • KWS training
        • YOLO training
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      • Command-line interface for model evaluation
      • Command-line interface to evaluate model MACS
      • Layer Blocks
        • conv_block
        • dense_block
        • separable_conv_block
    • Hardware constraints
      • InputConvolutional
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      • Upgrading models with legacy quantizers
  • API reference
    • Akida runtime
      • Model
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      • Tools
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        • Compatibility
    • CNN2SNN
      • Tool functions
        • quantize
        • quantize_layer
        • convert
        • check_model_compatibility
        • load_quantized_model
        • Transforms
        • Calibration
      • Quantizers
        • WeightQuantizer
        • LinearWeightQuantizer
        • StdWeightQuantizer
        • StdPerAxisQuantizer
        • MaxQuantizer
        • MaxPerAxisQuantizer
      • Quantized layers
        • QuantizedConv2D
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        • ActivationDiscreteRelu
        • QuantizedReLU
    • Akida models
      • Layer blocks
        • conv_block
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        • CNN conversion flow tutorial
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  • Model zoo performances
    • Image domain
      • Classification
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    • Point cloud
      • Classification
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Akida Examples
  • »
  • Overview: module code

All modules for which code is available

  • akida.compatibility.conversion
  • akida.core
    • akida.core.soc
  • akida.layers.convolutional
  • akida.layers.fully_connected
  • akida.layers.input_convolutional
  • akida.layers.input_data
  • akida.layers.separable_convolutional
  • akida.sparsity
  • akida.virtual_devices
  • akida_models.cwru.model_convtiny
  • akida_models.detection.generate_anchors
  • akida_models.detection.map_evaluation
  • akida_models.detection.model_yolo
  • akida_models.detection.processing
  • akida_models.distiller
  • akida_models.filter_pruning
  • akida_models.gamma_constraint
  • akida_models.imagenet.model_akidanet
  • akida_models.imagenet.model_akidanet_edge
  • akida_models.imagenet.model_mobilenet
  • akida_models.imagenet.model_mobilenet_edge
  • akida_models.imagenet.model_vgg
  • akida_models.imagenet.preprocessing
  • akida_models.kws.model_ds_cnn
  • akida_models.kws.preprocessing
  • akida_models.layer_blocks
  • akida_models.macs
  • akida_models.mnist.model_gxnor
  • akida_models.modelnet40.model_pointnet_plus
  • akida_models.modelnet40.preprocessing
  • akida_models.training
  • akida_models.utils
  • akida_models.utk_face.model_vgg
  • akida_models.utk_face.preprocessing
  • cnn2snn.calibration.adaround
  • cnn2snn.calibration.bias_correction
  • cnn2snn.calibration.calibration
  • cnn2snn.converter
  • cnn2snn.quantization
  • cnn2snn.quantization_layers
  • cnn2snn.quantization_ops
  • cnn2snn.transforms.batch_normalization
  • cnn2snn.transforms.equalization
  • cnn2snn.transforms.reshape
  • cnn2snn.transforms.sequential
  • cnn2snn.utils

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