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 ...