def train(args):
# set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# init model and optimizer
if args.verbose:
print("Training baseline model:" if args.baseline else "Training HNN model:")
output_dim = args.input_dim if args.baseline else 2
nn_model = MLP(args.input_dim, args.hidden_dim, output_dim, args.nonlinearity)
model = HNN(args.input_dim, differentiable_model=nn_model,
field_type=args.field_type, baseline=args.baseline)
optim = torch.optim.Adam(model.parameters(), args.learn_rate, weight_decay=0)
# arrange data
data = get_dataset(args.name, args.save_dir, verbose=True)
x = torch.tensor( data['coords'], requires_grad=True, dtype=torch.float32)
test_x = torch.tensor( data['test_coords'], requires_grad=True, dtype=torch.float32)
dxdt = torch.Tensor(data['dcoords'])
test_dxdt = torch.Tensor(data['test_dcoords'])
# vanilla train loop
stats = {'train_loss': [], 'test_loss': []}
for step in range(args.total_steps+1):
# train step
ixs = torch.randperm(x.shape[0])[:args.batch_size]
dxdt_hat = model.time_derivative(x[ixs])
dxdt_hat += args.input_noise * torch.randn(*x[ixs].shape) # add noise, maybe
loss = L2_loss(dxdt[ixs], dxdt_hat)
loss.backward()
grad = torch.cat([p.grad.flatten() for p in model.parameters()]).clone()
optim.step() ; optim.zero_grad()
# run test data
test_ixs = torch.randperm(test_x.shape[0])[:args.batch_size]
test_dxdt_hat = model.time_derivative(test_x[test_ixs])
test_dxdt_hat += args.input_noise * torch.randn(*test_x[test_ixs].shape) # add noise, maybe
test_loss = L2_loss(test_dxdt[test_ixs], test_dxdt_hat)
# logging
stats['train_loss'].append(loss.item())
stats['test_loss'].append(test_loss.item())
if args.verbose and step % args.print_every == 0:
print("step {}, train_loss {:.4e}, test_loss {:.4e}, grad norm {:.4e}, grad std {:.4e}"
.format(step, loss.item(), test_loss.item(), grad@grad, grad.std()))
train_dxdt_hat = model.time_derivative(x)
train_dist = (dxdt - train_dxdt_hat)**2
test_dxdt_hat = model.time_derivative(test_x)
test_dist = (test_dxdt - test_dxdt_hat)**2
print('Final train loss {:.4e} +/- {:.4e}\nFinal test loss {:.4e} +/- {:.4e}'
.format(train_dist.mean().item(), train_dist.std().item()/np.sqrt(train_dist.shape[0]),
test_dist.mean().item(), test_dist.std().item()/np.sqrt(test_dist.shape[0])))
return model, stats
def train(args):
# set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# init model and optimizer
if args.verbose:
print("Training baseline model:" if args.baseline else "Training HNN model:")
output_dim = args.input_dim if args.baseline else 2
nn_model = MLP(args.input_dim, 400, output_dim, args.nonlinearity)
model = HNN(args.input_dim, differentiable_model=nn_model,
field_type=args.field_type, baseline=args.baseline)
optim = torch.optim.Adam(model.parameters(), args.learn_rate, weight_decay=1e-4)
# the data API is different
# make sure it is a fair comparison
# generate the data the same way as in the SymODEN
# compute the time derivative based on the generated data
us = [0.0]
data = get_dataset(seed=args.seed, save_dir=args.save_dir,
rad=args.rad, us=us, samples=50, timesteps=45)
# arrange data
train_x, t_eval = data['x'][0,:,:,0:2], data['t']
test_x, t_eval = data['test_x'][0,:,:,0:2], data['t']
train_dxdt = (train_x[1:,:,:] - train_x[:-1,:,:]) / (t_eval[1] - t_eval[0])
test_dxdt = (test_x[1:,:,:] - test_x[:-1,:,:]) / (t_eval[1] - t_eval[0])
train_x = train_x[0:-1,:,:].reshape((-1,2))
test_x = test_x[0:-1,:,:].reshape((-1,2))
test_dxdt = test_dxdt.reshape((-1,2))
train_dxdt = train_dxdt.reshape((-1,2))
x = torch.tensor( train_x, requires_grad=True, dtype=torch.float32)
test_x = torch.tensor( test_x, requires_grad=True, dtype=torch.float32)
dxdt = torch.Tensor(train_dxdt)
test_dxdt = torch.Tensor(test_dxdt)
# vanilla train loop
stats = {'train_loss': [], 'test_loss': []}
for step in range(args.total_steps+1):
# train step
dxdt_hat = model.rk4_time_derivative(x) if args.use_rk4 else model.time_derivative(x)
loss = L2_loss(dxdt, dxdt_hat)
loss.backward() ; optim.step() ; optim.zero_grad()
# run test data
test_dxdt_hat = model.rk4_time_derivative(test_x) if args.use_rk4 else model.time_derivative(test_x)
test_loss = L2_loss(test_dxdt, test_dxdt_hat)
# logging
stats['train_loss'].append(loss.item())
stats['test_loss'].append(test_loss.item())
if args.verbose and step % args.print_every == 0:
print("step {}, train_loss {:.4e}, test_loss {:.4e}".format(step, loss.item(), test_loss.item()))
train_dxdt_hat = model.time_derivative(x)
train_dist = (dxdt - train_dxdt_hat)**2
test_dxdt_hat = model.time_derivative(test_x)
test_dist = (test_dxdt - test_dxdt_hat)**2
print('Final train loss {:.4e} +/- {:.4e}\nFinal test loss {:.4e} +/- {:.4e}'
.format(train_dist.mean().item(), train_dist.std().item()/np.sqrt(train_dist.shape[0]),
test_dist.mean().item(), test_dist.std().item()/np.sqrt(test_dist.shape[0])))
return model, stats
def train(args):
# set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# init model and optimizer
if args.verbose:
print("Training baseline model:" if args.
baseline else "Training HNN model:")
output_dim = args.input_dim if args.baseline else 2
nn_model = MLP(args.input_dim, args.hidden_dim, output_dim,
args.nonlinearity)
model = HNN(args.input_dim,
differentiable_model=nn_model,
field_type=args.field_type,
baseline=args.baseline)
optim = torch.optim.Adam(model.parameters(),
args.learn_rate,
weight_decay=1e-4)
# arrange data
data = get_dataset(seed=args.seed)
x = torch.tensor(data['x'], requires_grad=True, dtype=torch.float32)
test_x = torch.tensor(data['test_x'],
requires_grad=True,
dtype=torch.float32)
dxdt = torch.Tensor(data['dx'])
test_dxdt = torch.Tensor(data['test_dx'])
# vanilla train loop
stats = {'train_loss': [], 'test_loss': []}
for step in range(args.total_steps + 1):
# train step
dxdt_hat = model.rk4_time_derivative(
x) if args.use_rk4 else model.time_derivative(x)
loss = L2_loss(dxdt, dxdt_hat)
loss.backward()
optim.step()
optim.zero_grad()
# run test data
test_dxdt_hat = model.rk4_time_derivative(
test_x) if args.use_rk4 else model.time_derivative(test_x)
test_loss = L2_loss(test_dxdt, test_dxdt_hat)
# logging
stats['train_loss'].append(loss.item())
stats['test_loss'].append(test_loss.item())
if args.verbose and step % args.print_every == 0:
print("step {}, train_loss {:.4e}, test_loss {:.4e}".format(
step, loss.item(), test_loss.item()))
train_dxdt_hat = model.time_derivative(x)
train_dist = (dxdt - train_dxdt_hat)**2
test_dxdt_hat = model.time_derivative(test_x)
test_dist = (test_dxdt - test_dxdt_hat)**2
print(
'Final train loss {:.4e} +/- {:.4e}\nFinal test loss {:.4e} +/- {:.4e}'
.format(train_dist.mean().item(),
train_dist.std().item() / np.sqrt(train_dist.shape[0]),
test_dist.mean().item(),
test_dist.std().item() / np.sqrt(test_dist.shape[0])))
return model, stats
def train(args):
if torch.cuda.is_available() and not args.cpu:
device = torch.device("cuda:0")
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.empty_cache()
print("Running on the GPU")
else:
device = torch.device("cpu")
print("Running on the CPU")
# set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
print("{} {}".format(args.folder, args.speed))
print("Training scaled model:" if args.scaled else "Training noisy model:")
print('{} pairs of coords in latent space '.format(args.latent_dim))
#using universal autoencoder, pre-encode the training points
autoencoder = MLPAutoencoder(args.input_dim_ae,
args.hidden_dim,
args.latent_dim * 2,
nonlinearity='relu')
full_model = PixelHNN(args.latent_dim * 2,
args.hidden_dim,
autoencoder=autoencoder,
nonlinearity=args.nonlinearity,
baseline=args.baseline)
path = "{}/saved_models/{}.tar".format(args.save_dir, args.ae_path)
full_model.load_state_dict(torch.load(path))
full_model.eval()
autoencoder_model = full_model.autoencoder
# get dataset (no test data for now)
data = get_dataset(args.folder,
args.speed,
scaled=args.scaled,
split=args.split_data,
experiment_dir=args.experiment_dir,
tensor=True)
gcoords = autoencoder_model.encode(data).cpu().detach().numpy()
x = torch.tensor(gcoords, dtype=torch.float, requires_grad=True)
dx_np = full_model.time_derivative(
torch.tensor(gcoords, dtype=torch.float,
requires_grad=True)).cpu().detach().numpy()
dx = torch.tensor(dx_np, dtype=torch.float)
nnmodel = MLP(args.input_dim, args.hidden_dim, args.output_dim)
model = HNN(2, nnmodel)
model.to(device)
optim = torch.optim.Adam(model.parameters(),
args.learn_rate,
weight_decay=args.weight_decay)
# vanilla ae train loop
stats = {'train_loss': [], 'test_loss': []}
for step in range(args.total_steps + 1):
# train step
ixs = torch.randperm(x.shape[0])[:args.batch_size]
x_train, dxdt = x[ixs].to(device), dx[ixs].to(device)
dxdt_hat = model.time_derivative(x_train)
loss = L2_loss(dxdt, dxdt_hat)
loss.backward()
optim.step()
optim.zero_grad()
stats['train_loss'].append(loss.item())
if step % args.print_every == 0:
print("step {}, train_loss {:.4e}".format(step, loss.item()))
# train_dist = hnn_ae_loss(x, x_next, model, return_scalar=False)
# print('Final train loss {:.4e} +/- {:.4e}'
# .format(train_dist.mean().item(), train_dist.std().item() / np.sqrt(train_dist.shape[0])))
return model
def train(args):
# set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# init model and optimizer
if args.verbose:
print("Training baseline model:" if args.baseline else "Training HNN model:")
output_dim = args.input_dim if args.baseline else 2
nn_model = MLPAutoencoder(args.input_dim, args.hidden_dim, args.latent_dim, args.nonlinearity)
nn_model.to(device)
model = HNN(args.input_dim, differentiable_model=nn_model,
field_type=args.field_type, baseline=args.baseline, device=device)
model.to(device)
optim = torch.optim.Adam(model.parameters(), args.learn_rate, weight_decay=0)
# arrange data
X = np.load('statrectinputs.npy')
Y = np.load('statrectoutputs.npy')
Y[~np.isfinite(Y)] = 0
xm, xd = give_min_and_dist(X)
ym, yd= give_min_and_dist(Y)
X = scale(X, xm, xd)
Y = scale(Y, ym, yd)
n_egs = X.shape[0]
x = X[0:int(0.8*n_egs),:]
test_x = torch.tensor(X[:-int(0.2*n_egs),:], requires_grad=True, dtype=torch.float32)
dxdt = Y[0:int(0.8*n_egs),:]
test_dxdt = torch.tensor(Y[:-int(0.2*n_egs),:])
# vanilla train loop
stats = {'train_loss': [], 'test_loss': []}
for step in range(args.total_steps+1):
# train step
ixs = torch.randperm(x.shape[0])[:args.batch_size]
x = torch.tensor(x[ixs], requires_grad=True, dtype=torch.float32)
x.to(device)
dxdt_hat = model.time_derivative(x)
y = torch.tensor(dxdt[ixs])
y.to(device)
loss = L2_loss(y, dxdt_hat)
loss.backward()
grad = torch.cat([p.grad.flatten() for p in model.parameters()]).clone()
optim.step() ; optim.zero_grad()
# run test data
test_ixs = torch.randperm(test_x.shape[0])[:args.batch_size]
test_dxdt_hat = model.time_derivative(test_x[test_ixs])
#test_dxdt_hat += args.input_noise * torch.randn(*test_x[test_ixs].shape) # add noise, maybe
test_loss = L2_loss(test_dxdt[test_ixs], test_dxdt_hat)
# logging
stats['train_loss'].append(loss.item())
stats['test_loss'].append(test_loss.item())
if args.verbose and step % args.print_every == 0:
print("step {}, train_loss {:.4e}, test_loss {:.4e}, grad norm {:.4e}, grad std {:.4e}"
.format(step, loss.item(), test_loss.item(), grad@grad, grad.std()))
ixs = torch.randperm(x.shape[0])[:10000]
x = torch.tensor(x[ixs], requires_grad=True, dtype=torch.float32)
x.to(device)
enc = model.encoding(x).detach().numpy()
print(x.shape)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = x.detach().numpy()
img = ax.scatter(enc[:,0], enc[:,3], enc[:,2], c=enc[:,1], cmap=plt.hot())
fig.colorbar(img)
plt.savefig('lrep.png')
y0 = torch.tensor([0.4, 0.3, 1/np.sqrt(2), 1/np.sqrt(2)], dtype=torch.float32)
update_fn = lambda t, y0: model_update(t, y0, model)
orbit, settings = get_orbit(y0, t_points=10, t_span=[0, 10], update_fn=update_fn)
print(orbit)
plt.scatter(orbit[:,0], orbit[:, 1])
plt.savefig('orbit.png')
return model, stats
def train(args):
if torch.cuda.is_available() and not args.cpu:
device = torch.device("cuda:0")
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.empty_cache()
print("Running on the GPU")
else:
device = torch.device("cpu")
print("Running on the CPU")
# set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# get dataset (no test data for now)
angular_velo, acc_1, acc_2, sound = get_dataset_split(
args.folder,
args.speed,
scaled=args.scaled,
experiment_dir=args.experiment_dir,
tensor=True)
sub_col = {
0: [angular_velo, 1, 'v'],
1: [acc_1, 3, 'a1'],
2: [acc_2, 3, 'a2'],
3: [sound, 1, 's']
}
col2use = sub_col[args.sub_columns][0]
# using universal autoencoder, pre-encode the training points
autoencoder = MLPAutoencoder(sub_col[args.sub_columns][1],
args.hidden_dim,
args.latent_dim * 2,
dropout_rate=args.dropout_rate_ae)
full_model = PixelHNN(args.latent_dim * 2,
args.hidden_dim,
autoencoder=autoencoder,
nonlinearity=args.nonlinearity,
baseline=args.baseline,
dropout_rate=args.dropout_rate)
path = "{}/saved_models/{}-{}.tar".format(args.save_dir, args.ae_path,
sub_col[args.sub_columns][2])
full_model.load_state_dict(torch.load(path))
full_model.eval()
autoencoder_model = full_model.autoencoder
gcoords = autoencoder_model.encode(col2use).cpu().detach().numpy()
x = torch.tensor(gcoords, dtype=torch.float, requires_grad=True)
dx_np = full_model.time_derivative(
torch.tensor(gcoords, dtype=torch.float,
requires_grad=True)).cpu().detach().numpy()
dx = torch.tensor(dx_np, dtype=torch.float)
nnmodel = MLP(args.input_dim, args.hidden_dim, args.output_dim)
model = HNN(2, nnmodel)
model.to(device)
optim = torch.optim.Adam(model.parameters(),
args.learn_rate,
weight_decay=args.weight_decay)
print("Data from {} {}, column: {}".format(args.folder, args.speed,
sub_col[args.sub_columns][2]))
# x = torch.tensor(col2use[:-1], dtype=torch.float)
# x_next = torch.tensor(col2use[1:], dtype=torch.float)
#
# autoencoder = MLPAutoencoder(sub_col[args.sub_columns][1], args.hidden_dim, args.latent_dim * 2, dropout_rate=args.dropout_rate_ae)
# model = PixelHNN(args.latent_dim * 2, args.hidden_dim,
# autoencoder=autoencoder, nonlinearity=args.nonlinearity, baseline=args.baseline, dropout_rate=args.dropout_rate)
# model.to(device)
# optim = torch.optim.Adam(model.parameters(), args.learn_rate, weight_decay=args.weight_decay)
# vanilla ae train loop
stats = {'train_loss': []}
for step in range(args.total_steps + 1):
# train step
ixs = torch.randperm(x.shape[0])[:args.batch_size]
x_train, dxdt = x[ixs].to(device), dx[ixs].to(device)
dxdt_hat = model.time_derivative(x_train)
loss = L2_loss(dxdt, dxdt_hat)
loss.backward()
optim.step()
optim.zero_grad()
stats['train_loss'].append(loss.item())
if step % args.print_every == 0:
print("step {}, train_loss {:.4e}".format(step, loss.item()))
# train_dist = hnn_ae_loss(x, x_next, model, return_scalar=False)
# print('Final train loss {:.4e} +/- {:.4e}'
# .format(train_dist.mean().item(), train_dist.std().item() / np.sqrt(train_dist.shape[0])))
return model