def __init__(self, args):
super(Decoder, self).__init__()
self.dim_h = args.dim_h
self.n_z = args.n_z
self.output = args.n_input
self.dec1 = MLPLayer(self.n_z, self.dim_h, args.sigma_prior)
#self.bn1 = nn.BatchNorm1d(self.dim_h)
self.dec1_act = nn.Tanh()
self.dec2 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
#self.bn2 = nn.BatchNorm1d(self.dim_h)
self.dec2_act = nn.Tanh()
self.dec3 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
#self.bn3 = nn.BatchNorm1d(self.dim_h)
self.dec3_act = nn.Tanh()
self.dec4 = MLPLayer(self.dim_h, self.output, args.sigma_prior)
def __init__(self, args):
super(Encoder, self).__init__()
self.dim_h = args.dim_h
self.n_z = args.n_z
self.input = args.n_input
self.l1 = MLPLayer(self.input, self.dim_h, args.sigma_prior)
self.l1_act = nn.ReLU()
self.l2 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
self.l2_act = nn.ReLU()
self.l3 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
self.l3_act = nn.ReLU()
self.l4 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
self.l4_act = nn.ReLU()
self.l5 = MLPLayer(self.dim_h, self.n_z, args.sigma_prior)
def __init__(self, args):
super(Encoder, self).__init__()
self.dim_h = args.dim_h
self.n_z = args.n_z
self.input = args.n_input
self.enc1 = MLPLayer(self.input, self.dim_h * 4, args.sigma_prior)
self.bn1 = nn.BatchNorm1d(self.dim_h * 4)
self.enc1_act = nn.ReLU()
self.enc2 = MLPLayer(self.dim_h * 4, self.dim_h * 2, args.sigma_prior)
self.bn2 = nn.BatchNorm1d(self.dim_h * 2)
self.enc2_act = nn.ReLU()
self.enc3 = MLPLayer(self.dim_h * 2, self.dim_h, args.sigma_prior)
self.bn3 = nn.BatchNorm1d(self.dim_h)
self.enc3_act = nn.ReLU()
self.enc4 = MLPLayer(self.dim_h, self.n_z, args.sigma_prior)
self.enc5 = MLPLayer(self.dim_h, self.n_z, args.sigma_prior)
def __init__(self, args):
super(Decoder, self).__init__()
self.dim_h = args.dim_h
self.n_z = args.n_z
self.output = args.n_input
self.label_emb = nn.Embedding(y_dim, args.n_z)
self.dec1 = MLPLayer(self.n_z, self.dim_h * 2, args.sigma_prior)
self.bn1 = nn.BatchNorm1d(self.dim_h * 2)
self.dec1_act = nn.ReLU()
self.dec2 = MLPLayer(self.dim_h * 2, self.dim_h * 2, args.sigma_prior)
self.bn2 = nn.BatchNorm1d(self.dim_h * 2)
self.dec2_act = nn.ReLU()
self.dec3 = MLPLayer(self.dim_h * 2, self.dim_h * 2, args.sigma_prior)
self.bn3 = nn.BatchNorm1d(self.dim_h * 2)
self.dec3_act = nn.ReLU()
self.dec3_1 = MLPLayer(self.dim_h * 2, self.dim_h * 2,
args.sigma_prior)
self.bn3_1 = nn.BatchNorm1d(self.dim_h * 2)
self.dec3_1_act = nn.ReLU()
self.dec4 = MLPLayer(self.dim_h * 2, self.output, args.sigma_prior)
# self.bn4 = nn.BatchNorm1d(self.output)
self.dec4_act = nn.Tanh()
def __init__(self, args):
super(Decoder, self).__init__()
self.output = args.n_input
self.dim_h = args.dim_h
self.n_z = args.n_z
self.l1 = MLPLayer(self.n_z, self.dim_h, args.sigma_prior)
self.l1_act = nn.Tanh()
self.l2 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
self.l2_act = nn.Tanh()
self.l3 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
self.l3_act = nn.Tanh()
self.l4 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
self.l4_act = nn.Tanh()
self.l5 = MLPLayer(self.dim_h, self.dim_h, args.sigma_prior)
self.l5_act = nn.Tanh()
self.l6 = MLPLayer(self.dim_h, self.output, args.sigma_prior)