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NN.API.Models.Cnn

CNN Model Helpers (API) #

Small config-style CNN constructors for runnable examples.

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structure NN.API.nn.models.CnnConfig :
Type

Configuration for a compact convolutional image classifier.

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    def NN.API.nn.models.CnnConfig.outH1 (cfg : CnnConfig) :
    ℕ

    Height after the convolution stage.

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      def NN.API.nn.models.CnnConfig.outW1 (cfg : CnnConfig) :
      ℕ

      Width after the convolution stage.

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        def NN.API.nn.models.CnnConfig.outH2 (cfg : CnnConfig) :
        ℕ

        Height after the pooling stage.

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          def NN.API.nn.models.CnnConfig.outW2 (cfg : CnnConfig) :
          ℕ

          Width after the pooling stage.

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            def NN.API.nn.models.CnnConfig.featSize (cfg : CnnConfig) :
            ℕ

            Flattened feature size entering the classifier head.

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              @[reducible, inline]
              abbrev NN.API.nn.models.cnnInShape (cfg : CnnConfig) :
              Shape

              Batched image input shape for cnn.

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                @[reducible, inline]
                abbrev NN.API.nn.models.cnnOutShape (cfg : CnnConfig) :
                Shape

                Batched logits output shape for cnn.

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                  def NN.API.nn.models.cnn (cfg : CnnConfig) (h_inC : cfg.inC ≠ 0 := by decide) (h_kH : cfg.conv.kH ≠ 0 := by decide) (h_kW : cfg.conv.kW ≠ 0 := by decide) (h_poolKH : cfg.pool.kH ≠ 0 := by decide) (h_poolKW : cfg.pool.kW ≠ 0 := by decide) :
                  M (Sequential (cnnInShape cfg) (cnnOutShape cfg))

                  Build a compact CNN classifier: conv -> relu -> maxPool -> flatten -> linear.

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