- Deep Learning framework for Go from scratch
- pure Go implementation
- using only the standard library
make mnistdl
go run cmd/mnist/main.go --dir ./testdata*model.MLP
0: *layer.Affine: W(784, 50), B(1, 50): 39250
1: *layer.BatchNorm: G(1, 50), B(1, 50): 100
2: *layer.ReLU
3: *layer.Affine: W(50, 50), B(1, 50): 2550
4: *layer.BatchNorm: G(1, 50), B(1, 50): 100
5: *layer.ReLU
6: *layer.Affine: W(50, 50), B(1, 50): 2550
7: *layer.BatchNorm: G(1, 50), B(1, 50): 100
8: *layer.ReLU
9: *layer.Affine: W(50, 10), B(1, 10): 510
10: *layer.SoftmaxWithLoss
0: loss=[[2.2485]], train_acc=0.1800, test_acc=0.1300
predict: [9 4 1 4 8 3 2 4 2 4 9 4 4 3 4 2 6 8 4 8]
label : [0 6 8 7 8 2 0 1 3 1 8 7 7 7 5 6 1 8 8 8]
100: loss=[[0.4916]], train_acc=0.8800, test_acc=0.8900
predict: [4 3 3 6 4 8 3 1 6 7 2 9 8 1 8 7 3 0 5 6]
label : [4 3 3 6 4 8 3 1 6 7 2 9 8 1 8 3 8 0 5 6]
200: loss=[[0.4164]], train_acc=0.8900, test_acc=0.8600
predict: [4 4 3 1 0 0 3 3 7 6 1 9 8 2 9 5 2 6 6 6]
label : [9 4 3 1 0 0 3 3 7 6 1 9 8 2 9 9 2 6 6 6]
300: loss=[[0.3602]], train_acc=0.8800, test_acc=0.8800
predict: [7 9 8 5 2 6 3 6 2 6 9 4 9 8 2 1 5 7 0 9]
label : [7 9 8 6 2 6 3 6 2 6 9 4 9 8 2 1 5 7 0 9]
400: loss=[[0.2036]], train_acc=0.9400, test_acc=0.9500
predict: [4 1 2 6 7 1 8 7 5 5 7 6 7 5 8 4 7 4 3 2]
label : [4 1 2 6 7 1 8 7 5 5 7 6 7 5 8 4 7 4 3 2]
...make additiondl
go run cmd/seq2seq/main.go --dir ./testdata*model.PeekySeq2Seq
0: *model.Encoder
1: *layer.TimeEmbedding: W(13, 64): 832
2: *layer.TimeLSTM: Wx(64, 512), Wh(128, 512), B(1, 512): 98816
3: *model.PeekyDecoder
4: *layer.TimeEmbedding: W(13, 64): 832
5: *layer.TimeLSTM: Wx(192, 512), Wh(128, 512), B(1, 512): 164352
6: *layer.TimeAffine: W(256, 13), B(1, 13): 3341
7: *layer.TimeSoftmaxWithLoss
...
[7 + 9 0 6 ] [_ 9 1 3 ]; [9 9 1 ]
[5 4 4 + 4 2 ] [_ 5 8 6 ]; [2 1 ]
[7 2 + 9 2 5 ] [_ 9 9 7 ]; [9 9 ]
[7 8 8 + 2 2 7] [_ 1 0 1 5]; [5 4 ]
[7 8 + 4 6 8 ] [_ 5 4 6 ]; [9 1 ]
[1 + 6 8 0 ] [_ 6 8 1 ]; [2 9 1 ]
[4 + 6 7 0 ] [_ 6 7 4 ]; [9 9 ]
[3 5 4 + 5 7 9] [_ 9 3 3 ]; [2 9 1 ]
[4 3 6 + 8 7 4] [_ 1 3 1 0]; [9 1 ]
[2 6 6 + 9 8 3] [_ 1 2 4 9]; [9 9 ]
20, 0: loss=0.8230, train_acc=0.0000, test_acc=0.0000
[1 9 4 + 7 ] [_ 2 0 1 ]; [2 0 1 ]
[5 4 4 + 4 2 ] [_ 5 8 6 ]; [5 8 6 ]
[7 8 8 + 2 2 7] [_ 1 0 1 5]; [1 0 1 5]
[3 0 + 2 6 1 ] [_ 2 9 1 ]; [2 9 1 ]
[7 + 9 0 6 ] [_ 9 1 3 ]; [9 1 3 ]
[4 + 8 9 6 ] [_ 9 0 0 ]; [9 9 ]
[3 5 4 + 5 7 9] [_ 9 3 3 ]; [2 0 1 ]
[2 6 6 + 9 8 3] [_ 1 2 4 9]; [2 9 2 ]
[6 8 8 + 6 7 0] [_ 1 3 5 8]; [1 0 1 5]
[1 + 6 8 0 ] [_ 6 8 1 ]; [2 0 1 ]
40, 0: loss=0.0912, train_acc=1.0000, test_acc=0.0000
...