def objective(trial):
# Generate the model.
# main training loop
h_net = define_model(neurons=trial.suggest_int("n_neurons", 8, 128))
parameters = h_net.parameters()
lr = trial.suggest_loguniform("lr", 1e-5, 1e-1)
optimizer = torch.optim.Adam(parameters, lr=lr)
startEpoches = time.time()
for i in range(int(richards_celia.EPOCHS)):
optimizer.zero_grad() # clear gradients for next train
loss = loss_func(h_net, tz_pairs)
with torch.no_grad():
if i % richards_celia.LOGSTEPS == 0:
print("step " + str(i) + ": ")
print(loss)
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
# check if has converged, break early
with torch.no_grad():
if i % 10 == 0:
if len(h_net.losses) >= 11:
last_loss_changes = [
torch.abs(a_i - b_i)
for a_i, b_i in zip(h_net.losses[-10:], h_net.losses[-11:-1])
]
if all(llc <= torch.finfo(richards_celia.DTYPE).eps for llc in last_loss_changes):
# or: use max instead of all
break
endEpoches = time.time()
last_losses = h_net.losses[:-100]
accuracy = sum(last_losses) / len(last_losses)
return accuracy
def train():
# main training loop
h_net = define_model(n_hidden=richards_celia.HIDDEN,
n_neurons=richards_celia.NEURONS)
parameters = h_net.parameters()
optimizer = torch.optim.Adam(parameters, lr=richards_celia.LR)
startEpoches = time.time()
for i in range(int(richards_celia.EPOCHS)):
optimizer.zero_grad() # clear gradients for next train
loss = loss_func(h_net=h_net, input=tz_pairs)
with torch.no_grad():
if i % richards_celia.LOGSTEPS == 0:
print("step " + str(i) + ": ")
print(loss)
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
# check if has converged, break early
with torch.no_grad():
if i % 10 == 0:
if len(h_net.losses) >= 11:
last_loss_changes = [
torch.abs(a_i - b_i) for a_i, b_i in zip(
h_net.losses[-10:], h_net.losses[-11:-1])
]
if all(llc <= torch.finfo(richards_celia.DTYPE).eps
for llc in last_loss_changes):
# or: use max instead of all
break
endEpoches = time.time()
torch.save(h_net, "./h_model.pt")
print("Runtime of training loop: " + str(endEpoches - startEpoches) + " s")
plt.plot([i for i in range(len(h_net.losses))], [i for i in h_net.losses],
label="total loss")
plt.plot([i for i in range(len(h_net.pde_loss))],
[i for i in h_net.pde_loss],
label="pde loss")
plt.plot([i for i in range(len(h_net.bc_initial_losses))],
[i for i in h_net.bc_initial_losses],
label="bc initial loss")
plt.plot([i for i in range(len(h_net.bc_top_losses))],
[i for i in h_net.bc_top_losses],
label="bc top loss")
plt.plot([i for i in range(len(h_net.bc_bottom_losses))],
[i for i in h_net.bc_bottom_losses],
label="bc bottom loss")
plt.legend()
plt.savefig('loss.pdf')
plt.savefig('loss.png')
plt.show()