def __init__(self, input_dim, hidden_dim, diag_dim, nonlinearity='tanh'):
super(PSD, self).__init__()
self.diag_dim = diag_dim
if diag_dim == 1:
self.linear1 = torch.nn.Linear(input_dim, hidden_dim)
self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear3 = torch.nn.Linear(hidden_dim, diag_dim)
for l in [self.linear1, self.linear2, self.linear3]:
torch.nn.init.orthogonal_(
l.weight) # use a principled initialization
self.nonlinearity = choose_nonlinearity(nonlinearity)
else:
assert diag_dim > 1
self.diag_dim = diag_dim
self.off_diag_dim = int(diag_dim * (diag_dim - 1) / 2)
self.linear1 = torch.nn.Linear(input_dim, hidden_dim)
self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear3 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear4 = torch.nn.Linear(hidden_dim,
self.diag_dim + self.off_diag_dim)
for l in [self.linear1, self.linear2, self.linear3, self.linear4]:
torch.nn.init.orthogonal_(
l.weight) # use a principled initialization
self.nonlinearity = choose_nonlinearity(nonlinearity)
def __init__(self,
input_dim,
hidden_dim,
latent_dim,
nonlinearity='tanh',
dropout_rate=0.1):
super(MLPAutoencoder, self).__init__()
self.linear1 = torch.nn.Linear(input_dim, hidden_dim)
self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear3 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear4 = torch.nn.Linear(hidden_dim, latent_dim)
self.linear5 = torch.nn.Linear(latent_dim, hidden_dim)
self.linear6 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear7 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear8 = torch.nn.Linear(hidden_dim, input_dim)
self.dropout1 = torch.nn.Dropout(p=dropout_rate)
self.dropout2 = torch.nn.Dropout(p=dropout_rate)
self.dropout3 = torch.nn.Dropout(p=dropout_rate)
self.dropout4 = torch.nn.Dropout(p=dropout_rate)
self.dropout5 = torch.nn.Dropout(p=dropout_rate)
self.dropout6 = torch.nn.Dropout(p=dropout_rate)
for l in [self.linear1, self.linear2, self.linear3, self.linear4, \
self.linear5, self.linear6, self.linear7, self.linear8]:
torch.nn.init.orthogonal_(
l.weight) # use a principled initialization
self.nonlinearity = choose_nonlinearity(nonlinearity)
def __init__(self, input_dim, hidden_dim, output_dim, nonlinearity='tanh'):
super(MLP, self).__init__()
self.linear1 = torch.nn.Linear(input_dim, hidden_dim)
self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear3 = torch.nn.Linear(hidden_dim, output_dim, bias=None)
for l in [self.linear1, self.linear2, self.linear3]:
torch.nn.init.orthogonal_(
l.weight) # use a principled initialization
self.nonlinearity = choose_nonlinearity(nonlinearity)
def __init__(self,
input_dim,
hidden_dim,
diag_dim,
device,
nonlinearity='tanh'):
super(DampMatrix, self).__init__()
assert diag_dim > 1
self.linear1 = torch.nn.Linear(int(input_dim / 2), hidden_dim)
self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)
self.linear3 = torch.nn.Linear(hidden_dim, 1, bias=None)
for l in [self.linear1, self.linear2, self.linear3]:
torch.nn.init.orthogonal_(
l.weight) # use a principled initialization
self.nonlinearity = choose_nonlinearity(nonlinearity)
self.device = device