python/methylnet/models.py
def decode(self, z):
		"""Decode latent embeddings back into reconstructed input.

		Parameters
		----------
		z : type
			Reparameterized latent embedding.

		Returns
		-------
		torch.tensor
			Reconstructed input.

		"""
		#out = self.z_develop(z)
		#print('out',out.size())
		#out = out.view(z.size(0), 64, self.z_dim, self.z_dim)
		out = self.decoder(z)
		#print(out)
		return out
Decode latent embeddings back into reconstructed input.

		Parameters
		----------
		z : type
			Reparameterized latent embedding.

		Returns
		-------
		torch.tensor
			Reconstructed input.

		
Decode latent embeddings back into reconstructed input.
Parameters

z : type
Reparameterized latent embedding.

Returns


Decode latent embeddings back into reconstructed input . Parameters z : type Reparameterized latent embedding . Returns
def decode(self, z):
		out = self.decoder(z)
		return out
def decode ( self , z ) : out = self . decoder ( z ) return out
Decode latent embeddings back into reconstructed input.
Decode latent embeddings back into reconstructed input .
"""Decode latent embeddings back into reconstructed input.

		Parameters
		----------
		z : type
			Reparameterized latent embedding.

		Returns
		-------
		torch.tensor
			Reconstructed input.

		""" #out = self.z_develop(z) #print('out',out.size()) #out = out.view(z.size(0), 64, self.z_dim, self.z_dim) #print(out)
self
z