def run(model_type, input_length, input_dim, num_classes, num_hidden,
batch_size, learning_rate, train_steps, max_norm, device):
assert model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(device)
# Initialize the model that we are going to use
model_pars = [
input_length, input_dim, num_hidden, num_classes, batch_size, device
]
model = LSTM(*model_pars) \
if model_type == 'LSTM' \
else VanillaRNN(*model_pars)
model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(input_length + 1)
data_loader = DataLoader(dataset, batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate)
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# Add more code here ...
optimizer.zero_grad()
ys = model.forward(batch_inputs)
############################################################################
# QUESTION: what happens here and why?
############################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=max_norm)
############################################################################
# Add more code here ...
predictions = ys.argmax(dim=-1)
loss = criterion(ys, batch_targets)
loss.backward()
optimizer.step()
accuracy = (batch_targets == predictions).float().mean()
# Just for time measurement
t2 = time.time()
examples_per_second = batch_size / float(t2 - t1)
stats = {'loss': loss, 'accuracy': accuracy}
if step % 10 == 0:
print(
"[{}] Train Step {:04d}/{:04d}, Batch Size = {}, Examples/Sec = {:.2f}, "
"Accuracy = {:.2f}, Loss = {:.3f}".format(
datetime.now().strftime("%Y-%m-%d %H:%M"), step,
train_steps, batch_size, examples_per_second, accuracy,
loss))
if step == train_steps:
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
break
print('Done training.')
return (accuracy.item())
class PalindromeExperiment(PytorchExperiment):
def setup(self):
self.save_checkpoint(name='setup')
(model_type, input_length, input_dim, num_classes, num_hidden, batch_size, learning_rate, train_steps, max_norm, wanted_device) = itemgetter(*flags)(vars(self.config))
assert model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
# TODO: debug CUDA issues
device = torch.device(wanted_device)
# device = torch.device(device if torch.cuda.is_available() else 'cpu')
# Initialize the model that we are going to use
model_pars = [input_length, input_dim, num_hidden, num_classes, batch_size, device]
self.model = LSTM(*model_pars) \
if model_type == 'LSTM' \
else VanillaRNN(*model_pars)
self.model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(input_length+1)
self.data_loader = DataLoader(dataset, batch_size, num_workers=1)
# Setup the loss and optimizer
self.criterion = torch.nn.CrossEntropyLoss()
self.optimizer = torch.optim.RMSprop(self.model.parameters(), lr=learning_rate)
# TODO: plot accuracy over input_length
# TODO: increase learning_rate over input_length
# TODO: compare result with RNN
def train(self, epoch):
(model_type, input_length, input_dim, num_classes, num_hidden, batch_size, learning_rate, train_steps, max_norm, device) = itemgetter(*flags)(vars(self.config))
with SummaryWriter('part1/train') as w:
results = []
for step, (batch_inputs, batch_targets) in enumerate(self.data_loader):
# Only for time measurement of step through network
t1 = time.time()
# Add more code here ...
self.optimizer.zero_grad()
# move to device
inputs = torch.tensor(batch_inputs, dtype=torch.float).to(device)
targets = torch.tensor(batch_targets, dtype=torch.long ).to(device)
ys = self.model.forward(inputs)
# clip the gradients so gradient explosion won't let us overshoot the minimum
# https://www.quora.com/What-is-gradient-clipping-and-why-is-it-necessary
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=max_norm)
# Add more code here ...
predictions = ys.argmax(dim=-1)
loss = self.criterion(ys, targets)
loss.backward()
self.optimizer.step()
accuracy = (targets == predictions).float().mean()
# Just for time measurement
t2 = time.time()
examples_per_second = batch_size/float(t2-t1)
stats = {'loss':loss, 'accuracy':accuracy}
results.append({'step': step, **{k:v.item() for k,v in stats.items()}})
if step % 100 == 0:
w.add_scalars('metrics', stats, int(step/10))
# # TODO: check why this is slow!
# for k, v in stats.items():
# self.add_result(value=v.item(), name=f'train_{k}', counter=step / train_steps, label=k)
self.elog.print("elog [{}] Train Step {:04d}/{:04d}, Batch Size = {}, Examples/Sec = {:.2f}, "
"Accuracy = {:.2f}, Loss = {:.3f}".format(
datetime.now().strftime("%Y-%m-%d %H:%M"), step,
train_steps, batch_size, examples_per_second,
accuracy, loss
))
self.save_checkpoint(name='train', n_iter=step)
if step % 100 == 0:
results = write_csv(results, self.config)
if step == train_steps:
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
break
print('Done training.')
results = write_csv(results, self.config)
def validate(self, epoch):
pass
