name: 'tiny'
description: 'Incremental TinyImageNet - Supervized'

routing:
  depth: 3
  depthThr: 1.34e-4
  energyThr: 0.2
  basisSize: {{ basisSize }}
  embeddingSize: {{ embeddingSize }}
  primeOffset: 2000
  perUnitHashing: True
  decoder:
    denseParams:
      - out_features: 610
        act:
          type: 'ReLU'
          params:
        drpt: 0.6
      - out_features: 100
        act:
          type: 'ReLU'
          params:
        drpt: 0.4
      - out_features: 200
        act:
          type:
          params:
        drpt: 0.2

units:
  - N: 3
    expansionThr: 1e-2
    enableBasisUpdate: True
    counterInit: 5
    counterAgeFactor: 7
    gradUpdate: True
    coder:
      - conv:
          in_channels: 3
          out_channels: 63
          kernel_size: [3,3]
          stride: 1
          padding: 2
        mpool:
          kernel_size: [3,3]
        act:
          type: 'LeakyReLU'
          params:
        drpt: 0.35

  - N: 3
    expansionThr: 1e-2
    enableBasisUpdate: True
    counterInit: 5
    counterAgeFactor: 7
    gradUpdate: True
    coder:
      - conv:
          in_channels: 3
          out_channels: 54
          kernel_size: [3,3]
          stride: 1
          padding: 2
        mpool:
          kernel_size: [2,2]
        act:
          type: 'LeakyReLU'
          params:
        drpt: 0.35

  - N: 3
    expansionThr: 1e-2
    enableBasisUpdate: True
    counterInit: 5
    counterAgeFactor: 7
    gradUpdate: True
    coder:
      - conv:
          in_channels: 3
          out_channels: 48
          kernel_size: [2,2]
          stride: 1
          padding: 2
        mpool:
          kernel_size: [3,3]
        act:
          type: 'ReLU'
          params:
        drpt: 0.35

  - N: 3
    expansionThr: 1e-2
    enableBasisUpdate: True
    counterInit: 5
    counterAgeFactor: 7
    gradUpdate: True
    coder:
      - conv:
          in_channels: 3
          out_channels: 48
          kernel_size: [4,4]
          stride: 1
          padding: 2
        mpool:
          kernel_size: [2,2]
        act:
          type: 'LeakyReLU'
          params:
        drpt: 0.35

training:
  seed:
  datasets: ['TIN_0']

  epochs: 3
  extraUnits: 0 # always start with 0: no need to add
  # units at first
  batchSize: 16
  inputShape: [-1,3,32,32]
  learningRate: 1.35e-3
  fullLossCoeff: 0.5
  l1_residuCoeff: {{ l1_residuCoeff }}
  l2_residuCoeff: 0


