component Alexnet{
    ports in Z(0:255)^{3, 224, 224} image,
         out Q(0:1)^{10,1,1} predictions;

    implementation CNN {

        def split1(i){
            [i] ->
            Convolution(kernel=(5,5), channels=128) ->
            Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
            Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
            Relu()
        }
        def split2(i){
            [i] ->
            Convolution(kernel=(3,3), channels=192) ->
            Relu() ->
            Convolution(kernel=(3,3), channels=128) ->
            Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
            Relu()
        }
        def fc(){
            FullyConnected(units=4096) ->
            Relu() ->
            Dropout()
        }

        image ->
        Convolution(kernel=(11,11), channels=96, stride=(4,4), padding="no_loss") ->
        Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
        Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
        Relu() ->
        Split(n=2) ->
        split1(i=[0|1]) ->
        Concatenate() ->
        Convolution(kernel=(3,3), channels=384) ->
        Relu() ->
        Split(n=2) ->
        split2(i=[0|1]) ->
        Concatenate() ->
        fc(->=2) ->
        FullyConnected(units=10) ->
        Softmax() ->
        predictions

    }
}