go-ml-dev/nn

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```golang

import (
    "fmt"
    "go4ml.xyz/base/model"
    "go4ml.xyz/dataset/mnist"
    "go4ml.xyz/iokit"
    "go4ml.xyz/nn"
    "go4ml.xyz/nn/mx"
    "gotest.tools/assert"
    "testing"
)

var mnistConv0 = nn.Sequence(
    nn.Convolution{Channels: 24, Kernel: mx.Dim(3, 3), Activation: nn.ReLU},
    nn.MaxPool{Kernel: mx.Dim(2, 2), Stride: mx.Dim(2, 2)},
    nn.Convolution{Channels: 32, Kernel: mx.Dim(5, 5), Activation: nn.ReLU, BatchNorm: true},
    nn.MaxPool{Kernel: mx.Dim(2, 2), Stride: mx.Dim(2, 2)},
    nn.FullyConnected{Size: 32, Activation: nn.Swish, BatchNorm: true, Dropout: 0.33},
    nn.FullyConnected{Size: 10, Activation: nn.Softmax})

func Test_mnistConv0(t *testing.T) {
    modelFile := iokit.File(model.Path("mnist_test_conv0.zip"))

    report := nn.Model{
        Network:   mnistConv0,
        Optimizer: nn.Adam{Lr: .001},
        Loss:      nn.CrossEntropyLoss{},
        Input:     mx.Dim(1, 28, 28),
        Seed:      42,
        BatchSize: 32,
        //Context:   mx.GPU,
    }.Feed(model.Dataset{
        Source:   mnist.Data.RandomFlag(model.TestCol, 42, 0.2),
        Label:    model.LabelCol,
        Test:     model.TestCol,
        Features: []string{"Image"},
    }).LuckyTrain(model.Training{
        Iterations: 15,
        ModelFile:  modelFile,
        Metrics:    model.Classification{Accuracy: 0.983},
        Score:      model.ErrorScore,
    })

    fmt.Println(report.TheBest, report.Score)
    fmt.Println(report.History.Round(5))
    assert.Assert(t, model.Accuracy(report.Test) >= 0.98)

    net1 := nn.LuckyObjectify(modelFile) //.Gpu()
    lr := model.LuckyEvaluate(mnist.T10k, model.LabelCol, net1, 32, model.Classification{})
    fmt.Println(lr.Round(5))
    assert.Assert(t, model.Accuracy(lr) >= 0.98)
}
```