def train(config, seed=0, seq_length=0):
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if seq_length != 0:
config.input_length = seq_length
# Initialize tensorboard writer
# writer = SummaryWriter()
# Initialize the device which to run the model on
device = torch.device(config.device)
print(device)
# Load dataset
if config.dataset == 'randomcomb':
print('Load random combinations dataset ...')
# Initialize the dataset and data loader
config.num_classes = config.input_length
dataset = datasets.RandomCombinationsDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
elif config.dataset == 'bss':
print('Load bss dataset ...')
# Initialize the dataset and data loader
config.num_classes = 2
config.input_dim = 3
dataset = datasets.BaumSweetSequenceDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = 4 * config.input_length
elif config.dataset == 'bipalindrome':
print('Load binary palindrome dataset ...')
# Initialize the dataset and data loader
config.num_classes = config.input_length
dataset = datasets.BinaryPalindromeDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = config.input_length * 4 + 2 - 1
# Setup the model that we are going to use
if config.model_type == 'LSTM':
print("Initializing LSTM model ...")
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'biLSTM':
print("Initializing bidirectional LSTM model...")
model = biLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
elif config.model_type == 'GRU':
print("Initializing GRU model ...")
model = GRU(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'peepLSTM':
print("Initializing peephole LSTM model ...")
model = peepLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
# Setup the loss and optimizer
loss_function = torch.nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=config.learning_rate)
loss_history = []
acc_history = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# Move to GPU
batch_inputs = batch_inputs.to(device) # [batch_size, seq_length,1]
batch_targets = batch_targets.to(device) # [batch_size]
# Reset for next iteration
model.zero_grad()
# Forward pass
log_probs = model(batch_inputs)
# print('log', log_probs.size())
# print('batch', batch_targets.size)
# Compute the loss, gradients and update network parameters
loss = loss_function(log_probs, batch_targets)
loss.backward()
#######################################################################
# Check for yourself: what happens here and why?
#######################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
#######################################################################
optimizer.step()
predictions = torch.argmax(log_probs, dim=1)
correct = (predictions == batch_targets).sum().item()
accuracy = correct / log_probs.size(0)
loss_history.append(loss.item())
acc_history.append(accuracy)
if step % 200 == 0:
print('\nLoss:', loss.item())
print('Acc:', accuracy)
# writer.add_scalar("Loss", loss, step)
# writer.add_scalar("Accuracy", accuracy, step)
# print(predictions[0, ...], batch_targets[0, ...])
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % 60 == 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,
config.train_steps, config.batch_size,
examples_per_second, accuracy, loss))
# Check if training is finished
if step == config.train_steps:
# If you receive a PyTorch data-loader error, check this bug report
# https://github.com/pytorch/pytorch/pull/9655
break
# writer.flush()
# writer.close()
print(f'Done training with seed {seed} and seq_length {seq_length}')
print('Final loss:', loss_history[-1])
print('Final acc:', acc_history[-1])
return loss_history, acc_history
def train(config):
#np.random.seed(24)
#torch.manual_seed(24)
# Initialize the device which to run the model on
device = torch.device(config.device)
print(device)
# Load dataset
if config.dataset == 'randomcomb':
print('Load random combinations dataset ...')
# Initialize the dataset and data loader
config.num_classes = config.input_length
dataset = datasets.RandomCombinationsDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
elif config.dataset == 'bss':
print('Load bss dataset ...')
# Initialize the dataset and data loader
config.num_classes = 2
config.input_dim = 3
dataset = datasets.BaumSweetSequenceDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = 4 * config.input_length
elif config.dataset == 'bipalindrome':
print('Load binary palindrome dataset ...')
# Initialize the dataset and data loader
config.num_classes = config.input_length
dataset = datasets.BinaryPalindromeDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = config.input_length * 4 + 2 - 1
# Setup the model that we are going to use
if config.model_type == 'LSTM':
print("Initializing LSTM model ...")
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'biLSTM':
print("Initializing bidirectional LSTM model...")
model = biLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
elif config.model_type == 'GRU':
print("Initializing GRU model ...")
model = GRU(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'peepLSTM':
print("Initializing peephole LSTM model ...")
model = peepLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
# Setup the loss and optimizer
loss_function = torch.nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=config.learning_rate)
accuracy_list = []
loss_list = []
old_loss = 1.0
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# Move to GPU
batch_inputs = batch_inputs.to(device) # [batch_size, seq_length,1]
batch_targets = batch_targets.to(device) # [batch_size]
#print(batch_inputs[:,0,:].shape)
#embedding = nn.Embedding(3, config.input_dim)
#print(embedding(batch_inputs[:,0,:].long()).shape)
# Reset for next iteration
model.zero_grad()
# Forward pass
log_probs = model(batch_inputs)
# Compute the loss, gradients and update network parameters
loss = loss_function(log_probs, batch_targets)
loss.backward()
#######################################################################
# Check for yourself: what happens here and why?
#######################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
#######################################################################
optimizer.step()
predictions = torch.argmax(log_probs, dim=1)
correct = (predictions == batch_targets).sum().item()
accuracy = correct / log_probs.size(0)
accuracy_list.append(accuracy)
loss_list.append(loss.item())
# print(predictions[0, ...], batch_targets[0, ...])
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % 60 == 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,
config.train_steps, config.batch_size,
examples_per_second, accuracy, loss))
# Check if training is finished
if step == config.train_steps or old_loss == loss.item():
# If you receive a PyTorch data-loader error, check this bug report
# https://github.com/pytorch/pytorch/pull/9655
break
else:
old_loss = loss.item()
print('Done training.')
###########################################################################
###########################################################################
print('Evaluating...')
acc = []
for i in range(3):
acc_sublist = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
model.eval()
batch_inputs = batch_inputs.to(
device) # [batch_size, seq_length,1]
batch_targets = batch_targets.to(device)
pred = model(batch_inputs)
predictions = torch.argmax(pred, dim=1)
correct = (predictions == batch_targets).sum().item()
accuracy = correct / pred.size(0)
acc_sublist.append(accuracy)
if step == 25:
break
acc.append(np.mean(acc_sublist))
print('Mean accuracy is {} and standard deviation is {}'.format(
np.mean(acc), np.std(acc)))
return accuracy_list, loss_list
def train(config, seed):
np.random.seed(seed)
torch.manual_seed(seed)
# Initialize the device which to run the model on
device = torch.device(config.device)
print(device)
# Load dataset
if config.dataset == 'randomcomb':
print('Load random combinations dataset ...')
# Initialize the dataset and data loader
config.num_classes = config.input_length
dataset = datasets.RandomCombinationsDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
elif config.dataset == 'bss':
print('Load bss dataset ...')
# Initialize the dataset and data loader
config.num_classes = 2
config.input_dim = 3
dataset = datasets.BaumSweetSequenceDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = 4 * config.input_length
elif config.dataset == 'bipalindrome':
print('Load binary palindrome dataset ...')
# Initialize the dataset and data loader
config.num_classes = 2
dataset = datasets.BinaryPalindromeDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = config.input_length * 4 + 2 - 1
# Setup the model that we are going to use
if config.model_type == 'LSTM':
print("Initializing LSTM model ...")
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'biLSTM':
print("Initializing bidirectional LSTM model...")
model = biLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
elif config.model_type == 'GRU':
print("Initializing GRU model ...")
model = GRU(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'peepLSTM':
print("Initializing peephole LSTM model ...")
model = peepLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
# Setup the loss and optimizer
loss_function = torch.nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=config.learning_rate)
losses = []
train_accuracies = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# Move to GPU
batch_inputs = batch_inputs.to(device) # [batch_size, seq_length,1]
batch_targets = batch_targets.to(device) # [batch_size]
# Reset for next iteration
model.zero_grad()
# Forward pass
log_probs = model(batch_inputs)
# Compute the loss, gradients and update network parameters
loss = loss_function(log_probs, batch_targets)
loss.backward()
losses.append(loss.item())
#######################################################################
# Check for yourself: what happens here and why?
#######################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
#######################################################################
optimizer.step()
predictions = torch.argmax(log_probs, dim=1)
correct = (predictions == batch_targets).sum().item()
accuracy = correct / log_probs.size(0)
train_accuracies.append(accuracy)
# print(predictions[0, ...], batch_targets[0, ...])
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % 60 == 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,
config.train_steps, config.batch_size,
examples_per_second, accuracy, loss))
# Check if training is finished
if step == config.train_steps:
# If you receive a PyTorch data-loader error, check this bug report
# https://github.com/pytorch/pytorch/pull/9655
break
# Stop early if the last 100 losses were all low enough
if all(x < 0.001 for x in losses[-100:]):
break
print('Done training.')
# evaluate the model on new random data
model.eval()
test_accuracies = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Move to GPU
batch_inputs = batch_inputs.to(device) # [batch_size, seq_length,1]
batch_targets = batch_targets.to(device) # [batch_size]
# Forward pass
with torch.no_grad():
log_probs = model(batch_inputs)
predictions = torch.argmax(log_probs, dim=1)
correct = (predictions == batch_targets).sum().item()
accuracy = correct / log_probs.size(0)
test_accuracies.append(accuracy)
if step >= 5000 / config.batch_size:
# If you receive a PyTorch data-loader error, check this bug report
# https://github.com/pytorch/pytorch/pull/9655
break
return losses, train_accuracies, torch.tensor(
test_accuracies).mean().item()
def train(config):
seed = config.seed
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
#torch.backends.cudnn.deterministic=True
#torch.backends.cudnn.benchmark=False
# Initialize the device which to run the model on
device = torch.device(config.device)
print(device)
# Load dataset
if config.dataset == 'randomcomb':
print('Load random combinations dataset ...')
# Initialize the dataset and data loader
config.num_classes = config.input_length
dataset = datasets.RandomCombinationsDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
elif config.dataset == 'bss':
print('Load bss dataset ...')
# Initialize the dataset and data loader
config.num_classes = 2
config.input_dim = 3
dataset = datasets.BaumSweetSequenceDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = 4 * config.input_length
elif config.dataset == 'bipalindrome':
print('Load binary palindrome dataset ...')
# Initialize the dataset and data loader
config.num_classes = 2 #config.input_length
dataset = datasets.BinaryPalindromeDataset(config.input_length)
data_loader = DataLoader(dataset,
config.batch_size,
num_workers=1,
drop_last=True)
config.input_length = config.input_length * 4 + 2 - 1
# Setup the model that we are going to use
if config.model_type == 'LSTM':
print("Initializing LSTM model ...")
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'biLSTM':
print("Initializing bidirectional LSTM model...")
model = biLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
elif config.model_type == 'GRU':
print("Initializing GRU model ...")
model = GRU(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device).to(device)
elif config.model_type == 'peepLSTM':
print("Initializing peephole LSTM model ...")
model = peepLSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device).to(device)
model.numTrainableParameters()
# Setup the loss and optimizer
loss_function = torch.nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=config.learning_rate)
# For plotting
acc_plt = []
loss_plt = []
convergenceCounter = 0 # to stop after consecutive accuracies of 1.0 on training
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# Move to GPU
batch_inputs = batch_inputs.to(device) # [batch_size, seq_length,1]
batch_targets = batch_targets.to(device) # [batch_size]
# Reset for next iteration
model.zero_grad()
# Forward pass
log_probs = model(batch_inputs)
# Compute the loss, gradients and update network parameters
loss = loss_function(log_probs, batch_targets)
loss.backward()
#######################################################################
# Check for yourself: what happens here and why?
#######################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
#######################################################################
optimizer.step()
predictions = torch.argmax(log_probs, dim=1)
correct = (predictions == batch_targets).sum().item()
accuracy = correct / log_probs.size(0)
# print(predictions[0, ...], batch_targets[0, ...])
acc_plt.append(accuracy)
loss_plt.append(loss)
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % 60 == 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,
config.train_steps, config.batch_size,
examples_per_second, accuracy, loss))
# Check if training is finished
if accuracy > 0.999:
convergenceCounter += 1
if step == config.train_steps or convergenceCounter > 100: # If you receive a PyTorch data-loader error, check this bug report
# https://github.com/pytorch/pytorch/pull/9655
break
# test on new data:
model.eval()
with torch.no_grad():
correct = 0
total = 0
test_loss = 0
numBatchesTestEval = 10
for step, (x, t) in enumerate(data_loader):
if device.type == 'cuda':
x = x.to(device)
t = t.to(device)
log_probs = model(x)
predictions = torch.argmax(log_probs, dim=1)
correct += (predictions == t).sum().item()
total += log_probs.size(0)
test_loss += loss_function(log_probs, t) / numBatchesTestEval
if step == numBatchesTestEval:
break
test_accuracy = correct / total
model.train()
print('Done training.')
print('Accuracy on testset of 5000 examples:', test_accuracy)
print('Avg. loss on testset:', test_loss)
pltLossAcc(loss_plt, acc_plt, config)