def __init__(self, input_size, hidden_size, latent_size):
super().__init__()
self.samplelayer = NormalDistributed(latent_size=latent_size)
self.linear = nn.Linear(input_size, hidden_size)
self.batch_norm = nn.BatchNorm1d(hidden_size, momentum=0.1)
self.h2z = nn.Linear(hidden_size, self.samplelayer.inputShape()[-1])
self.elu = nn.ELU()
def __init__(self, input_size, hidden_size, latent_size):
super().__init__()
self.samplelayer = NormalDistributed(latent_size=latent_size)
self.conv = nn.Conv1d(in_channels=input_size,
out_channels=hidden_size,
kernel_size=3)
self.batch_norm = nn.BatchNorm1d(hidden_size, momentum=0.1)
self.h2z = nn.Linear(hidden_size, self.samplelayer.inputShape()[-1])
self.elu = nn.ELU()
def __init__(self, input_size, hidden_size, latent_size):
super().__init__()
self.samplelayer1 = GaussianMerge(latent_size=latent_size)
self.linear1 = nn.Linear(input_size, hidden_size)
self.batch_norm1 = nn.BatchNorm1d(hidden_size, momentum=0.1)
self.h2z1 = nn.Linear(hidden_size, self.samplelayer1.inputShape()[-1])
self.samplelayer2 = NormalDistributed(latent_size=latent_size)
self.linear2 = nn.Linear(input_size, hidden_size)
self.batch_norm2 = nn.BatchNorm1d(hidden_size, momentum=0.1)
self.h2z2 = nn.Linear(hidden_size, self.samplelayer2.inputShape()[-1])
self.elu = nn.ELU()
def __init__(self, input_size, hidden_size, latent_size):
super().__init__()
self.samplelayer1 = GaussianMerge(latent_size=latent_size)
self.conv1 = nn.ConvTranspose1d(in_channels=input_size,
out_channels=hidden_size,
kernel_size=3)
self.batch_norm1 = nn.BatchNorm1d(hidden_size, momentum=0.1)
self.h2z1 = nn.Linear(hidden_size, self.samplelayer1.inputShape()[-1])
self.samplelayer2 = NormalDistributed(latent_size=latent_size)
self.conv2 = nn.ConvTranspose1d(in_channels=input_size,
out_channels=hidden_size,
kernel_size=3)
self.batch_norm2 = nn.BatchNorm1d(hidden_size, momentum=0.1)
self.h2z2 = nn.Linear(hidden_size, self.samplelayer2.inputShape()[-1])
self.elu = nn.ELU()
def __init__(self,input_size,rnn_size,latent_size,output_size,use_softmax=False): super().__init__() """ Layer definitions """ self.aux_loss = False self.kl_loss = False # sample layer with normal distribution self.samplelayer = NormalDistributed(latent_size=latent_size) # encode from input space to hidden space self.encoder = RNNEncoder(input_size=input_size,rnn_size=rnn_size) # encoded to latent layer self.h2z = nn.Sequential( nn.Linear(rnn_size,self.samplelayer.inputShape()[-1]), nn.ELU() ) # latent to decoded layer self.z2h = nn.Sequential( nn.Linear(self.samplelayer.outputShape()[-1],rnn_size), nn.ELU() ) # decode from hidden space to input space self.decoder = RNNDecoder(input_size=input_size,rnn_size=rnn_size,output_size=output_size,use_softmax=use_softmax)