def create_ODERNN_model():
obsrv_std = torch.Tensor([0.1]).to(device)
z0_prior = Normal(
torch.Tensor([0.0]).to(device),
torch.Tensor([1.]).to(device))
gru_units = 40
n_ode_gru_dims = latent_dim
ode_func_net = utils.create_net(n_ode_gru_dims,
n_ode_gru_dims,
n_layers=2,
n_units=100,
nonlinear=nn.Tanh)
rec_ode_func = ODEFunc(input_dim=input_dim,
latent_dim=n_ode_gru_dims,
ode_func_net=ode_func_net,
device=device).to(device)
z0_diffeq_solver = DiffeqSolver(input_dim,
rec_ode_func,
"dopri5",
latent_dim,
odeint_rtol=1e-3,
odeint_atol=1e-4,
device=device)
model = ODE_RNN(input_dim=input_dim,
latent_dim=latent_dim,
n_gru_units=50,
n_units=50,
device=device,
z0_diffeq_solver=z0_diffeq_solver,
concat_mask=True,
obsrv_std=obsrv_std,
use_binary_classif=False,
classif_per_tp=False,
n_labels=1,
train_classif_w_reconstr=False).to(device)
disable_bias = True
if disable_bias:
for module in model.modules():
if hasattr(module, 'bias'):
module.bias = None
return model
classif_per_tp = False
n_labels = 1
obsrv_std = 0.01
niters = 1
status_properties = ['loss']
latent_dim = 25
##################################################################
# Model
obsrv_std = torch.Tensor([0.1]).to(device)
z0_prior = Normal(torch.Tensor([0.0]).to(device), torch.Tensor([1.]).to(device))
gru_units = 40
n_ode_gru_dims = latent_dim
ode_func_net = utils.create_net(n_ode_gru_dims, n_ode_gru_dims,
n_layers = 2, n_units = 50, nonlinear = nn.Tanh)
rec_ode_func = ODEFunc(
input_dim = input_dim,
latent_dim = n_ode_gru_dims,
ode_func_net = ode_func_net,
device = device).to(device)
z0_diffeq_solver = DiffeqSolver(input_dim, rec_ode_func, "dopri5", latent_dim,
odeint_rtol = 1e-3, odeint_atol = 1e-4, device = device)
model = ODE_RNN(input_dim=input_dim, latent_dim=latent_dim,
n_gru_units = 50, n_units = 50, device = device,
z0_diffeq_solver = z0_diffeq_solver,
concat_mask = True, obsrv_std = obsrv_std,
use_binary_classif = False,
def create_LatentODE_model(args,
input_dim,
z0_prior,
obsrv_std,
device,
classif_per_tp=False,
n_labels=1):
dim = args.latents
if args.poisson:
lambda_net = utils.create_net(dim,
input_dim,
n_layers=1,
n_units=args.units,
nonlinear=nn.Tanh)
# ODE function produces the gradient for latent state and for poisson rate
ode_func_net = utils.create_net(dim * 2,
args.latents * 2,
n_layers=args.gen_layers,
n_units=args.units,
nonlinear=nn.Tanh)
gen_ode_func = ODEFunc_w_Poisson(input_dim=input_dim,
latent_dim=args.latents * 2,
ode_func_net=ode_func_net,
lambda_net=lambda_net,
device=device).to(device)
else:
dim = args.latents
ode_func_net = utils.create_net(dim,
args.latents,
n_layers=args.gen_layers,
n_units=args.units,
nonlinear=nn.Tanh)
gen_ode_func = ODEFunc(input_dim=input_dim,
latent_dim=args.latents,
ode_func_net=ode_func_net,
device=device).to(device)
z0_diffeq_solver = None
n_rec_dims = args.rec_dims
enc_input_dim = int(input_dim) * 2 # we concatenate the mask
gen_data_dim = input_dim
z0_dim = args.latents
if args.poisson:
z0_dim += args.latents # predict the initial poisson rate
if args.z0_encoder == "odernn":
ode_func_net = utils.create_net(n_rec_dims,
n_rec_dims,
n_layers=args.rec_layers,
n_units=args.units,
nonlinear=nn.Tanh)
rec_ode_func = ODEFunc(input_dim=enc_input_dim,
latent_dim=n_rec_dims,
ode_func_net=ode_func_net,
device=device).to(device)
z0_diffeq_solver = DiffeqSolver(enc_input_dim,
rec_ode_func,
"euler",
args.latents,
odeint_rtol=1e-3,
odeint_atol=1e-4,
device=device)
encoder_z0 = Encoder_z0_ODE_RNN(n_rec_dims,
enc_input_dim,
z0_diffeq_solver,
z0_dim=z0_dim,
n_gru_units=args.gru_units,
device=device).to(device)
elif args.z0_encoder == "rnn":
encoder_z0 = Encoder_z0_RNN(z0_dim,
enc_input_dim,
lstm_output_size=n_rec_dims,
device=device).to(device)
else:
raise Exception("Unknown encoder for Latent ODE model: " +
args.z0_encoder)
decoder = Decoder(args.latents, gen_data_dim).to(device)
diffeq_solver = DiffeqSolver(gen_data_dim,
gen_ode_func,
'dopri5',
args.latents,
odeint_rtol=1e-3,
odeint_atol=1e-4,
device=device)
model = LatentODE(
input_dim=gen_data_dim,
latent_dim=args.latents,
encoder_z0=encoder_z0,
decoder=decoder,
diffeq_solver=diffeq_solver,
z0_prior=z0_prior,
device=device,
obsrv_std=obsrv_std,
use_poisson_proc=args.poisson,
use_binary_classif=args.classif,
linear_classifier=args.linear_classif,
classif_per_tp=classif_per_tp,
n_labels=n_labels,
train_classif_w_reconstr=(args.dataset == "physionet")).to(device)
return model
def create_LatentODE_model(input_dim, z0_prior, obsrv_std, device = device, classif_per_tp = False, n_labels = 1, latents=latent_dim, disable_bias=True):
dim = latent_dim
ode_func_net = utils.create_net(dim, latents,
n_layers = 2, n_units = 100, nonlinear = nn.Tanh)
gen_ode_func = ODEFunc(
input_dim = input_dim,
latent_dim = latent_dim,
ode_func_net = ode_func_net,
device = device).to(device)
z0_diffeq_solver = None
n_rec_dims = 50 # rec_dims: default 20
enc_input_dim = int(input_dim) * 2 # we concatenate the mask
gen_data_dim = input_dim
z0_dim = latent_dim
ode_func_net = utils.create_net(n_rec_dims, n_rec_dims,
n_layers = 2, n_units = 100, nonlinear = nn.Tanh)
rec_ode_func = ODEFunc(
input_dim = enc_input_dim,
latent_dim = n_rec_dims,
ode_func_net = ode_func_net,
device = device).to(device)
z0_diffeq_solver = DiffeqSolver(enc_input_dim, rec_ode_func, "dopri5", latents,
odeint_rtol = 1e-3, odeint_atol = 1e-4, device = device)
encoder_z0 = Encoder_z0_ODE_RNN(n_rec_dims, enc_input_dim, z0_diffeq_solver,
z0_dim = z0_dim, n_gru_units = 100, device = device).to(device)
decoder = Decoder(latents, input_dim=gen_data_dim).to(device)
diffeq_solver = DiffeqSolver(gen_data_dim, gen_ode_func, 'dopri5', latents,
odeint_rtol = 1e-5, odeint_atol = 1e-6, device = device)
model = LatentODE(
input_dim = gen_data_dim,
latent_dim = latents,
encoder_z0 = encoder_z0,
decoder = decoder,
diffeq_solver = diffeq_solver,
z0_prior = z0_prior,
device = device,
obsrv_std = obsrv_std,
use_poisson_proc = False,
use_binary_classif = False,
linear_classifier = False,
classif_per_tp = False,
n_labels = n_labels,
train_classif_w_reconstr = False
).to(device)
if disable_bias:
for module in model.modules():
if hasattr(module, 'bias'):
module.bias = None
return model
input_space_decay = args.input_decay,
cell = args.rnn_cell,
n_labels = n_labels,
train_classif_w_reconstr = (args.dataset == "physionet")
).to(device)
elif args.ode_rnn:
# Create ODE-GRU model
n_ode_gru_dims = args.latents
if args.poisson:
print("Poisson process likelihood not implemented for ODE-RNN: ignoring --poisson")
if args.extrap:
raise Exception("Extrapolation for ODE-RNN not implemented")
ode_func_net = utils.create_net(n_ode_gru_dims, n_ode_gru_dims,
n_layers = args.rec_layers, n_units = args.units, nonlinear = nn.Tanh)
rec_ode_func = ODEFunc(
input_dim = input_dim,
latent_dim = n_ode_gru_dims,
ode_func_net = ode_func_net,
device = device).to(device)
z0_diffeq_solver = DiffeqSolver(input_dim, rec_ode_func, "euler", args.latents,
odeint_rtol = 1e-3, odeint_atol = 1e-4, device = device)
model = ODE_RNN(input_dim, n_ode_gru_dims, device = device,
z0_diffeq_solver = z0_diffeq_solver, n_gru_units = args.gru_units,
concat_mask = True, obsrv_std = obsrv_std,
use_binary_classif = args.classif,
classif_per_tp = classif_per_tp,
