--- a
+++ b/ipynb/KlsAutoencoder.py
@@ -0,0 +1,63 @@
+
+from fastai import *
+from fastai.basic_data import *
+from fastai.basic_train import *
+from fastai.tabular import *
+from torch import nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+
+class KlsDataset(Dataset):
+    """Kmer latent representation dataset"""
+    def __init__(self, data,noise=0.):
+        super().__init__()
+        self.items = data.values if isinstance(data, pd.DataFrame) else data
+        self.noise = noise
+
+    def __len__(self):
+        return len(self.items)
+    def __getitem__(self, idx):
+        item = self.items[idx,:]
+        return (item if self.noise == 0. else self.mix_noise(item), item)
+
+def wing(dims):
+    layer_dims = list(zip(dims[:-1],dims[1:]))
+    fcl = [nn.Linear(*x, bias=False) for x in layer_dims]
+    relu = [nn.ReLU() for _ in range(len(fcl))]
+    layers = np.asarray(list(zip(fcl, relu))).ravel()[:-1]
+    return nn.Sequential(*layers)
+
+def init_weights(m):
+    if type(m) == nn.Linear:
+        nn.init.xavier_uniform_(m.weight)
+
+
+def print_weights(nlayer):
+    print(list(net.encoder.modules())[nlayer].weight)
+
+class KlsAutoEncoder (nn.Module):
+    """Generic autoencoder"""
+    def __init__(self, encoder_dims, decoder_dims):
+        super().__init__(self)
+        self.encoder = wing(encoder_dims)
+        self.decoder = wing(decoder_dims)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        return self.decoder(x)
+
+    def save_encoder(self,file:PathOrStr):
+        torch.save(self.encoder.state_dict(), path)
+
+class Encoder():
+    """Encoder part of KlsAutoeEncoder ready for inference"""
+
+    def __init__(self,file:PathOrStr,dims:Collection=[100,50,3]):
+        e = wing(dims).double()
+        e.load_state_dict(torch.load(file))
+        e.eval()
+        self.e = e
+
+    def transform(self,data:Collection):
+        """transform ```data``` to latent representaion"""
+        return self.e.forward(tensor(data).double()).cpu().detach().numpy()