def train(model, data, target, optimizer, max_iterations, loss_func_args):
start_time = time.time()
number_of_terms = [coeff_vec.shape[0] for coeff_vec in model(data)[3]]
board = Tensorboard(number_of_terms)
# Training
print(
'| Iteration | Progress | Time remaining | Cost | MSE | Reg | LL |'
)
for iteration in torch.arange(0, max_iterations + 1):
# Calculating prediction and library and scaling
prediction, time_deriv_list, sparse_theta_list, coeff_vector_list, theta = model(
data)
coeff_vector_scaled_list = scaling(coeff_vector_list,
sparse_theta_list, time_deriv_list)
# Calculating loss
loss_mse = mse_loss(prediction, target)
loss_reg = reg_loss(time_deriv_list, sparse_theta_list,
coeff_vector_list)
loss = torch.sum(loss_mse) + torch.sum(loss_reg)
# Writing
if iteration % 100 == 0:
# Write progress to command line
progress(iteration, start_time, max_iterations, loss.item(),
torch.sum(loss_mse).item(),
torch.sum(loss_reg).item(),
torch.sum(loss_reg).item())
lstsq_solution = torch.inverse(
theta.T @ theta) @ theta.T @ time_deriv_list[0]
# Calculate error for theta
theta_true = loss_func_args['library']
dt_true = loss_func_args['time_deriv']
mae_library = torch.mean(torch.abs(theta - theta_true), dim=0)
mae_dt = torch.mean(torch.abs(dt_true - time_deriv_list[0]), dim=0)
# Write to tensorboard
board.write(iteration,
loss,
loss_mse,
loss_reg,
loss_reg,
coeff_vector_list,
coeff_vector_scaled_list,
lstsq_solution=lstsq_solution.squeeze(),
mae_library=mae_library,
mae_time_deriv=mae_dt)
# Optimizer step
optimizer.zero_grad()
loss.backward()
optimizer.step()
board.close()
def train(model, data, target, optimizer, max_iterations, loss_func_args):
'''Trains the deepmod model with MSE, regression and l1 cost function. Updates model in-place.'''
start_time = time.time()
number_of_terms = [coeff_vec.shape[0] for coeff_vec in model(data)[3]]
board = Tensorboard(number_of_terms)
# Training
print(
'| Iteration | Progress | Time remaining | Cost | MSE | Reg | LL |'
)
for iteration in torch.arange(0, max_iterations + 1):
# Calculating prediction and library and scaling
prediction, time_deriv_list, sparse_theta_list, coeff_vector_list, theta = model(
data)
coeff_vector_scaled_list = scaling(coeff_vector_list,
sparse_theta_list, time_deriv_list)
# Calculating loss
loss_mse = mse_loss(prediction, target)
loss_reg = reg_loss(time_deriv_list, sparse_theta_list,
coeff_vector_list)
loss_l1 = l1_loss(coeff_vector_scaled_list, loss_func_args['l1'])
loss = torch.sum(1 + 2 * torch.log(2 * pi * loss_mse) +
loss_reg / loss_mse)
# Writing
if iteration % 100 == 0:
# Write progress to command line
progress(iteration, start_time, max_iterations, loss.item(),
torch.sum(loss_mse).item(),
torch.sum(loss_reg).item(),
torch.sum(loss_reg).item())
# Calculate error for theta
theta_true = loss_func_args['library']
dt_true = loss_func_args['time_deriv']
mae_library = torch.mean(torch.abs(theta - theta_true), dim=0)
mae_dt = torch.mean(torch.abs(dt_true - time_deriv_list[0]), dim=0)
# Write to tensorboard
board.write(iteration,
loss,
loss_mse,
loss_reg,
loss_l1,
coeff_vector_list,
coeff_vector_scaled_list,
mae_library=mae_library,
mae_time_deriv=mae_dt)
# Optimizer step
optimizer.zero_grad()
loss.backward()
optimizer.step()
board.close()
def train(model,
data,
target,
optimizer,
max_iterations,
loss_func_args,
log_dir=None):
start_time = time.time()
number_of_terms = [coeff_vec.shape[0] for coeff_vec in model(data)[3]]
board = Tensorboard(number_of_terms, log_dir)
# Training
print(
'| Iteration | Progress | Time remaining | Cost | MSE | Reg | LL |'
)
for iteration in torch.arange(0, max_iterations + 1):
# Calculating prediction and library and scaling
prediction, time_deriv_list, sparse_theta_list, coeff_vector_list, theta = model(
data)
coeff_vector_scaled_list = scaling(coeff_vector_list,
sparse_theta_list, time_deriv_list)
# Calculating loss
loss_mse = mse_loss(prediction, target)
loss_reg = reg_loss(time_deriv_list, sparse_theta_list,
coeff_vector_list)
loss_ll = torch.log(2 * pi * loss_mse)
loss_ll_fit = torch.log(2 * pi * loss_mse) + loss_reg / loss_mse
loss = torch.sum(loss_ll) + torch.sum(loss_ll_fit)
# Writing
if iteration % 100 == 0:
# Write progress to command line
progress(iteration, start_time, max_iterations, loss.item(),
torch.sum(loss_mse).item(),
torch.sum(loss_reg).item(),
torch.sum(loss_ll).item())
# Calculate error for theta
# Write to tensorboard
board.write(iteration, loss, loss_mse, loss_reg, loss_ll,
coeff_vector_list, coeff_vector_scaled_list)
# Optimizer step
optimizer.zero_grad()
loss.backward()
optimizer.step()
board.close()
def train_dynamic_logprob_scaled_double(model, data, target, optimizer, max_iterations, loss_func_args={'sparsity_update_period': 200, 'start_sparsity_update': 5000}):
'''Trains the deepmod model with MSE, regression and l1 cost function. Updates model in-place.'''
start_time = time.time()
number_of_terms = [coeff_vec.shape[0] for coeff_vec in model(data)[3]]
board = Tensorboard(number_of_terms)
#sigma = loss_func_args['noise'] # noise parameter
# Training
print('| Iteration | Progress | Time remaining | Cost | MSE | Reg | LL |')
for iteration in torch.arange(0, max_iterations + 1):
# Calculating prediction and library and scaling
prediction, time_deriv_list, sparse_theta_list, coeff_vector_list, theta = model(data)
coeff_vector_scaled_list = scaling(coeff_vector_list, sparse_theta_list, time_deriv_list)
# Calculating loss
loss_mse = mse_loss(prediction, target)
loss_reg = reg_loss(time_deriv_list, sparse_theta_list, coeff_vector_list)
loss_ll_fit = torch.log(2 * pi * loss_reg) #optimal sigma
loss_ll = torch.log(2 * pi * loss_mse) #optimal sigma
loss = torch.sum(loss_ll_fit) + torch.sum(loss_ll)
# Writing
if iteration % 100 == 0:
progress(iteration, start_time, max_iterations, loss.item(), torch.sum(loss_mse).item(), torch.sum(loss_reg).item(), torch.sum(loss_ll).item())
# Before writing to tensorboard, we need to fill the missing values with 0
coeff_vectors_padded = [torch.zeros(mask.size()).masked_scatter_(mask, coeff_vector.squeeze()) for mask, coeff_vector in zip(model.constraints.sparsity_mask, coeff_vector_list)]
scaled_coeff_vectors_padded = [torch.zeros(mask.size()).masked_scatter_(mask, coeff_vector.squeeze()) for mask, coeff_vector in zip(model.constraints.sparsity_mask, coeff_vector_scaled_list)]
board.write(iteration, loss, loss_mse, loss_reg, loss_ll, coeff_vectors_padded, scaled_coeff_vectors_padded)
# Optimizer step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Updating sparsity pattern
if (iteration >= loss_func_args['start_sparsity_update']) and (iteration % loss_func_args['sparsity_update_period'] == 0):
with torch.no_grad():
model.constraints.sparsity_mask = model.calculate_sparsity_mask(theta, time_deriv_list)
board.close()
