def train(self):
self.check_data()
self.init_paths()
train_inputs, train_outputs = self.prepare_data(
self.train_inputs, self.train_outputs)
other_inputs, other_outputs = {}, {}
for k in self.other_inputs:
i, o = self.prepare_data(self.other_inputs[k],
self.other_outputs[k])
other_inputs[k] = i
other_outputs[k] = o
optimizer = optim.Adam(self.model.parameters(),
lr=self.learning_rate,
weight_decay=self.weight_decay)
closure = self.make_closure(optimizer, train_inputs, train_outputs)
# for i in tqdm(range(self.epochs)):
for i in range(self.epochs):
start_time_str = utils.now()
start_time = time.time()
train_loss = optimizer.step(closure)
run_validate = i == 0 or (i + 1) % self.save_epochs == 0
if run_validate:
for name in other_inputs:
loss, accuracy = get_performance(model=self.model,
x=other_inputs[name],
y=other_outputs[name],
num_iters=self.num_iters,
no_grad=True)
self.other_losses[name].append(float(loss))
self.other_accuracies[name].append(accuracy)
if (i + 1) % self.save_epochs == 0:
filename = f"{self.filepath}/checkpoints/epoch_{i+1}.json"
utils.save(filename, self)
end_time_str = datetime.now().strftime("%m/%d/%Y %H:%M:%S")
end_time = time.time()
runtime = end_time - start_time
self.times.append({
'start_time': start_time_str,
'end_time': end_time_str,
'runtime': runtime
})
log_performance('train', i, train_loss,
np.mean(self.train_accuracies[-1][:, -1]))
if run_validate:
for name in sorted(self.other_inputs):
log_performance(
name, i, self.other_losses[name][-1],
np.mean(self.other_accuracies[name][-1][:, -1]))
filename = f"{self.filepath}/train_log.json"
utils.save(filename, self)
x_grid=x_grid_batch,
x_prob=x_prob_batch,
y=y_batch,
no_grad=False,
num_iters=hp['num_iters'])
loss.backward()
total_loss += loss
train_losses.append(float(total_loss))
epoch_accuracies.append(accuracies)
train_accuracies.append(np.concatenate(epoch_accuracies))
return total_loss
for i in tqdm(range(hp['epochs'])):
start_time_str = utils.now()
start_time = time.time()
train_loss = optimizer.step(closure)
run_validate = i == 0 or (i + 1) % hp['valid_epochs'] == 0
if run_validate:
for name in other_x_grid:
loss, accuracy = get_performance(model=model,
x_grid=other_x_grid[name],
x_prob=other_x_prob[name],
y=other_y[name],
num_iters=hp['num_iters'],
no_grad=True)
other_losses[name].append(float(loss))
other_accuracies[name].append(accuracy)
