def test(config, seq_size, n_examples):
# Initialize the dataset and data loader
dataset = PalindromeDataset(seq_size+1)
# dataset = PalindromeDataset(seq_size) ###################################
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
#Get one batch to test
(batch_inputs, batch_targets) = next(iter(data_loader))
#Convert inputs and labels into tensors
x = torch.tensor(batch_inputs, device=config.device)
# Load the trained model
model = torch.load('./Results/RNN/' + str(seq_size) + '_RNN_model', map_location=config.device)
# model = torch.load('./Results/RNN/Run 1/' + str(seq_size) + '_RNN_model_1', map_location=config.device) #############
model.to(config.device)
#get predictions for batch
with torch.no_grad():
pred = model.forward(x)
print('\n----------------------\nSequence length: ',str(seq_size+1),'\n----------------------') #####
for i in range(n_examples):
print('\nTesting on palindrome',str(i+1),':\n---------------\n\nInput:',str(batch_inputs[i].tolist()),'\nPredicted last digit:',str(pred[i,:].argmax().item()),'\n')
def train(config, input_length):
# Initialize the model that we are going to use
model = VanillaRNN(input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size) # fixme
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
# Initialize the dataset and data loader (leave the +1)
dataset = PalindromeDataset(input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss() # fixme
optimizer = torch.optim.Adam(model.parameters(),
lr=config.learning_rate) # fixme
losses = []
accuracies = []
loss = 0.0
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Add more code here ...
optimizer.zero_grad()
batch_inputs, batch_targets = batch_inputs.to(
device), batch_targets.to(device)
outputs = model(batch_inputs)
loss = criterion(outputs, batch_targets)
loss.backward()
optimizer.step()
# the following line is to deal with exploding gradients
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
# Add more code here ...
loss += loss.item() # fixme
accu = 0.0 # fixme
if step % 10 == 0:
# print acuracy/loss here
print('[step: %5d] loss: %.4f' % (step, loss / 10))
losses.append(loss / 10)
loss = 0.0
accu = accuracy(outputs, batch_targets)
accuracies.append(accu)
print('Accuracy on training dataset: %.3f %%' % (accu))
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
print('Done training.')
return model, losses, accuracies
def train(config):
print('Vanilla RNN is WORKING...')
model = VanillaRNN(config.input_length, config.input_dim, config.num_hidden, config.num_classes, config.batch_size)
dataset = PalindromeDataset(config.input_length+1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(), config.learning_rate, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)
#optimizer = torch.optim.SGD(model.parameters(), config.learning_rate)
# model.train()
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# step: epoch
# Add more code here ...
# the following line is to deal with exploding gradients
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config.max_norm)
optimizer.zero_grad()
batch_inputs = batch_inputs.unsqueeze(0)
output= model(batch_inputs)[0]
loss = criterion(output, batch_targets)
loss.backward()
optimizer.step()
_, pred = torch.max(output, 1)
all = len(pred)
correct = 0
for i in range(len(pred)):
if batch_targets[i] == pred[i]:
correct += 1
# Add more code here ...
accuracy = correct/all
if step % 25 == 0:
plot_step.append(step)
plot_loss.append(loss)
plot_accuracy.append(accuracy*100)
# print acuracy/loss here
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
fig1 = plt.subplot(2,1,1)
fig2 = plt.subplot(2,1,2)
fig1.plot(plot_step, plot_accuracy, c='red', label='accuracy')
fig1.legend()
fig2.plot(plot_step, plot_loss, c='green', label='loss')
fig2.legend()
plt.show()
print('Done training.')
def train(config):
# Initialize the model that we are going to use
model = VanillaRNN(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size) # fixme
# Initialize the dataset and data loader (leave the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss() # fixme
optimizer = torch.optim.RMSprop(model.parameters(),
lr=config.learning_rate) # fixme
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Add more code here ...
hit = 0
n, dim = batch_inputs.size()
batch_inputs_T = torch.transpose(batch_inputs, 0, 1)
# print(batch_inputs_T.size())
y_hat_oh = model.forward(batch_inputs_T)
for i in range(n):
y_pre, _ = max(enumerate(y_hat_oh[i]), key=itemgetter(1))
y = batch_targets[i].item()
# print(y_pre, y)
if y_pre == y:
hit += 1
# print("/////////")
# the following line is to deal with exploding gradients
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
# Add more code here ...
loss = criterion(y_hat_oh, batch_targets) # fixme
accuracy = hit / n * 100 # fixme
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 10 == 0:
print("loss: ", loss.item())
print("accuracy: ", accuracy)
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
print('Done training.')
def main_grads(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
for seq in range(config.input_length-1, config.input_length):
# Initialize the model that we are going to use
if config.model_type == "RNN":
model_def = VanillaRNN
else:
model_def = LSTM
model = model_def(seq, config.input_dim,
config.num_hidden, config.num_classes,
config.device).to(device) # fixme
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(seq + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
batch_inputs, batch_targets = next(iter(data_loader))
batch_inputs.requires_grad_(True)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss() # fixme
optimizer = optim.RMSprop(model.parameters(), config.learning_rate) # fixme
hidden_state, model_outputs = model.forward(batch_inputs)
# retain grad before doing actual backward pass
loss = criterion(model_outputs, batch_targets)
# model.zero_grad()
# optimizer.step()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config.max_norm)
grads_file = "results/{}_grad_hidden_state_seq.txt".format(config.model_type)
with open(grads_file, 'w+') as fd:
writer = csv.writer(fd)
for i, x in enumerate(model.hidden_states):
print(x.grad.abs().mean().item())
writer.writerow([i, x.grad.abs().mean().item()])
def test(model, config, input_length):
# Initialize the dataset and data loader (leave the +1)
dataset = PalindromeDataset(input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
accuracies = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
outputs = model(batch_inputs)
accu = 0.0
if step % 10 == 0:
accu = accuracy(outputs, batch_targets)
accuracies.append(accu)
if step == 2000:
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
break
print('Done testing.')
return accuracies
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == "RNN":
model_def = VanillaRNN
else:
model_def = LSTM
model = model_def(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.device).to(device) # fixme
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss() # fixme
optimizer = optim.RMSprop(model.parameters(),
config.learning_rate) # fixme
# init csv file
for d in ["results", "checkpoints", "assets"]:
if not os.path.exists(d):
os.mkdir(d)
cvs_file = 'results/w_grad_{}_inputlength_{}_hiddenunits_{}_lr_{}_batchsize_{}_{}.csv'.format(
config.model_type, config.input_length, config.num_hidden,
config.learning_rate, config.batch_size, int(time.time()))
cols_data = ['step', 'train_loss', 'train_accuracy', "avg_grad_w"]
with open(cvs_file, 'a') as fd:
writer = csv.writer(fd)
writer.writerow(cols_data)
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 ...
# voncert tensors to device for gpu training
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
_, model_outputs = model.forward(batch_inputs)
loss = criterion(model_outputs, batch_targets)
loss.backward()
############################################################################
# QUESTION: what happens here and why?
############################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
############################################################################
# Add more code here ...
optimizer.step()
loss = loss.item() # fixme
accuracy = calc_accuracy(model_outputs, batch_targets) # fixme
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % 10 == 0 and step > 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))
csv_data = [step, loss, accuracy]
with open(cvs_file, 'a') as fd:
writer = csv.writer(fd)
writer.writerow(csv_data)
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
print('Done training.')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == "RNN":
model = VanillaRNN(seq_length=config.input_length,
input_dim=config.input_dim,
num_hidden=config.num_hidden,
batch_size=config.batch_size,
num_classes=config.num_classes,
device=device)
elif config.model_type == "LSTM":
model = LSTM(seq_length=config.input_length,
input_dim=config.input_dim,
num_hidden=config.num_hidden,
num_classes=config.num_classes,
device=device,
batch_size=config.batch_size)
# send model to device
model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=config.learning_rate)
# track training statistics
train_accuracies = []
train_losses = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# batch inputs to device for cuda
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
# convert input batches to tensors on device
Ãnput_sequences = torch.tensor(batch_inputs,
dtype=torch.float,
device=device)
targets = torch.tensor(batch_targets, dtype=torch.long, device=device)
#print(Ãnput_sequences)
#print(targets)
# Backward pass
# reset gradients
optimizer.zero_grad()
# Forward pass
# Debugging
# predict classes for input batches
# a = Ãnput_sequences[:, 0].unsqueeze(1)
# print(Ãnput_sequences.size())
# print(a.size())
# break
# predict input sequences
predictions = model.forward(Ãnput_sequences)
# accuracy
accuracy = torch.div(
torch.sum(targets == predictions.argmax(dim=1)).to(torch.float),
config.batch_size)
# print(accuracy)
# backpropagate loss
# compute loss per batch
loss = criterion(predictions, targets)
loss.backward()
############################################################################
# QUESTION: what happens here and why?
# --> # ANSWER: Gradients are reinforced at each layer. Thus, very large gradients can appear. This leads to
# learning problems. Cutting the gradients to a limit overcomes that issue.
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
############################################################################
# update weights according to optimizer
optimizer.step()
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
# save stats for each step
train_accuracies.append(accuracy)
train_losses.append(loss)
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,
config.train_steps, config.batch_size, examples_per_second,
accuracy, loss))
# If the last 50 accuracies are already 1 (avg=1), stop the training, as convergence is reached and unnecessary
# computations dont have to be done
avg_accuracies = np.sum(train_accuracies[-50:]) / 50
print(avg_accuracies)
if avg_accuracies == 1:
print(
"\nTraining finished for length: {} after {} steps".format(
config.input_length, step))
print("Avg Accuracy : {:.3f}".format(avg_accuracies))
break
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
print('Done training.')
return max(train_accuracies), step
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length, config.input_dim, \
config.num_hidden, config.num_classes, \
config.batch_size, device=config.device)
elif config.model_type == 'LSTM':
model = LSTM(config.input_length, config.input_dim, \
config.num_hidden, config.num_classes, \
config.batch_size, device=config.device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.RMSprop(model.parameters(), lr=config.learning_rate)
results = 'palindrome length:' + str(config.input_length + 1) + '\n'
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 ...
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
optimizer.zero_grad()
nn_out = model(batch_inputs)
loss = criterion(nn_out, batch_targets)
loss.backward()
########################################################################
# QUESTION: what happens here and why?
########################################################################
nn.utils.clip_grad_norm_(model.parameters(), max_norm=config.max_norm)
########################################################################
optimizer.step()
accuracy = torch.sum(nn_out.argmax(dim=1) == batch_targets)\
.to(torch.float) / (config.batch_size)
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
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,
config.train_steps, config.batch_size,
examples_per_second, accuracy, loss))
results += str(accuracy.item()) + ", "
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
print('Done training.')
with open('results/' + str(config.model_type) \
+ str(config.input_length + 1) \
+ '.txt', 'w') as f:
f.write(results)
def train(net, input_length, print_log):
# Initialize the model that we are going to use
model = net(input_length, INPUT_DIM, NUM_HIDDEN, OUTPUT_DIM,
NUM_BATCH_SIZE).cuda()
# Initialize the dataset and data loader (leave the +1)
dataset = PalindromeDataset(input_length + 1)
data_loader = DataLoader(dataset, NUM_BATCH_SIZE, num_workers=1)
# Setup the loss and optimizer
cross_entropy = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=LEARNING_RATE)
# To avoid fluctuation
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=TRAIN_STEPS / 20,
gamma=0.96)
record_epochs, accs, losses = [], [], []
for step, train_data in enumerate(data_loader):
X, y = train_data
X, y = X.cuda().float(), y.cuda()
y_pred = model(X)
loss = cross_entropy(y_pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
# the following line is to deal with exploding gradients
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=MAX_NORM)
if step % 10 == 0:
correct = 0
total = 0
with torch.no_grad():
for i, test_data in enumerate(data_loader):
X, y = test_data
X, y = X.cuda().float(), y.cuda()
y_pred = model(X)
total += y.size(0)
_, predicted = torch.max(y_pred.data, 1)
correct += (predicted == y).sum().item()
if (i + 1) % TEST_SIZE == 0:
break
acc = round(correct / total, 4)
avg_loss = round(loss.item(), 6)
record_epochs.append(step)
accs.append(acc)
losses.append(avg_loss)
if print_log:
print('step: {}, loss: {}, test acc: {}'.format(
step, avg_loss, acc))
if step == TRAIN_STEPS:
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
break
correct = 0
total = 0
with torch.no_grad():
for i, test_data in enumerate(data_loader):
X, y = test_data
X, y = X.cuda().float(), y.cuda()
y_pred = model(X)
total += y.size(0)
_, predicted = torch.max(y_pred.data, 1)
correct += (predicted == y).sum().item()
if (i + 1) % TEST_SIZE == 0:
break
acc = round(correct / total, 4)
if print_log:
print('Done training.')
return record_epochs, accs, losses, acc
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
if config.device == 'best':
config.device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.embed_dim, config.num_hidden, \
config.num_classes, device)
else:
model = LSTM(config.embed_dim, config.num_hidden, \
config.num_classes, device)
# Initialize the dataset and data loader
dataset = PalindromeDataset(config.input_length)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = F.cross_entropy
optimizer = optim.RMSprop(model.parameters(), lr=config.learning_rate)
# Track metrics
losses = []
losses_last10 = []
accuracies = []
accuracies_last10 = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Transform input to RNN input format (sequence, batch, input)
# batch_inputs = batch_inputs.t().unsqueeze(2).to(device=device, dtype=torch.long)
batch_inputs = batch_inputs.t().to(device=device, dtype=torch.long)
batch_targets = batch_targets.to(device=device, dtype=torch.long)
# Only for time measurement of step through network
t1 = time.time()
# forward pass
logits = model.forward(batch_inputs)
# backprop
optimizer.zero_grad()
loss = criterion(logits, batch_targets)
loss.backward()
############################################################################
# QUESTION: what happens here and why?
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(), max_norm=config.max_norm)
############################################################################
optimizer.step()
# Compute metrics
accuracy = (logits.cpu().argmax(dim=1) == batch_targets.cpu()).numpy().mean()
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size/float(t2-t1)
# track metrics
accuracies_last10.append(accuracy.tolist())
losses_last10.append(loss.tolist())
if step % 10 == 0:
message = "[{}] 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)
print(message)
if config.log_path != "":
with open(config.log_path, "a") as f:
f.write(message + "\n")
accuracies.append(np.mean(accuracies_last10))
losses.append(np.mean(losses_last10))
accuracies_last10 = []
losses_last10 = []
# Early stopping criterion: average accuracy over last 1000 iters was lower than the 1000 before that
stopping_criterion = len(accuracies) > 200 and \
np.mean(accuracies[-100:]) <= np.mean(accuracies[-200:-100])
if step == config.train_steps or stopping_criterion:
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
print('Done training.')
return losses, accuracies
def train():
train_on_gpu = torch.cuda.is_available()
# Initialize the model that we are going to use
model = LSTM(config.input_length, config.input_dim, config.num_hidden, config.num_classes, config.batch_size)
if train_on_gpu:
model.cuda()
# Initialize the dataset and data loader (leave the +1)
dataset = PalindromeDataset(config.input_length+1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=config.learning_rate)
# Adjust learning rate
lrs = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.96)
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Add more code here ...
if train_on_gpu:
batch_inputs, batch_targets = batch_inputs.cuda().float(), batch_targets.cuda()
prediction = model(batch_inputs)
loss = criterion(prediction, batch_targets)
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
lrs.step()
# the following line is to deal with exploding gradients
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config.max_norm)
if step % 25 == 0:
# print acuracy/loss here
plot_epoch.append(step)
correct = 0
total = 0
with torch.no_grad():
for i, (test_input, test_target) in enumerate(data_loader):
if train_on_gpu:
test_input, test_target = test_input.cuda(), test_target.cuda()
eval_prediction = model(test_input)
num = len(test_target)
total += num
_, target = torch.max(eval_prediction.data, 1)
correct_batch = (target == test_target).sum().item()
correct += correct_batch
if i == len(test_target) - 1:
break;
accuracy = correct/total
test_loss = loss.item()
plot_test_accuracy.append(accuracy*100)
plot_test_loss.append(test_loss)
if step == config.train_steps:
break
fig1 = plt.subplot(2,1,1)
fig2 = plt.subplot(2,1,2)
fig1.plot(plot_epoch, plot_test_accuracy, c='red', label='accuracy')
fig1.legend()
fig2.plot(plot_epoch, plot_test_loss, c='green', label='loss')
fig2.legend()
plt.show()
print('Done training.')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device)
else:
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=config.learning_rate)
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
output = model.forward(batch_inputs)
loss = criterion(output, batch_targets)
optimizer.zero_grad()
loss.backward()
############################################################################
# QUESTION: It cuts off the gradient so we don't get exploding gradients
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
############################################################################
optimizer.step()
loss = loss.item()
accuracy = (torch.max(output, 1)[1] == batch_targets).float().mean()
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
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,
config.train_steps, config.batch_size, examples_per_second,
accuracy, loss))
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
print('Done training.')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == "RNN":
model = VanillaRNN(config.input_length, config.input_dim, config.num_hidden,\
config.num_classes, device=device)
elif config.model_type == "LSTM":
model = LSTM(config.input_length, config.input_dim, config.num_hidden,\
config.num_classes, device=device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
test_loader = iter(DataLoader(dataset, config.test_size, num_workers=1))
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=config.learning_rate)
results = open(config.out_file, "w+")
results.write(
"#model_type : {}\n#input_length : {}\n#input_dim : {}\n#num_classes : {}\n#num_hidden : {}\n#batch_size : {}\n#learn_rate : {}\n#train_steps : {}\n#max_norm : {}\n"
.format(config.model_type, config.input_length, config.input_dim,
config.num_classes, config.num_hidden, config.batch_size,
config.learning_rate, config.train_steps, config.max_norm))
results.write("#train_step accuracy loss\n")
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
batch_inputs = torch.nn.functional.one_hot(
batch_inputs.type(torch.LongTensor)).type(
torch.FloatTensor).to(device)
batch_targets = batch_targets.to(device)
# Only for time measurement of step through network
t1 = time.time()
optimizer.zero_grad()
# #for calculating gradients
# for timestep in range(config.input_length):
# model.zero_grad()
# batch_y, hGrad = model(batch_inputs, timestep) #without softmax
# #prevent gradients from exploding
# torch.nn.utils.clip_grad_norm(model.parameters(), max_norm=config.max_norm)
# loss = criterion(batch_y, batch_targets)
# loss.backward()
# results.write("{} {}\n".format(timestep,hGrad.grad.norm()))
# print("Done calculating gradients.")
# results.close()
# return
batch_y = model(batch_inputs) #without softmax
#prevent gradients from exploding
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
loss = criterion(batch_y, batch_targets)
loss.backward()
optimizer.step() if step > 0 else 0 #to be able to test initial model
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % config.eval_freq == 0:
# predictions = torch.argmax(torch.abs(batch_y),1) #training: smaller batch size than test
# accuracy = torch.sum(predictions == batch_targets).type(torch.FloatTensor)/config.batch_size
with torch.no_grad():
test_inputs, test_targets = next(test_loader)
test_inputs = torch.nn.functional.one_hot(
test_inputs.type(torch.LongTensor),
config.input_dim).type(torch.FloatTensor).to(device)
test_targets = test_targets.to(device)
test_y = model(test_inputs)
test_loss = criterion(test_y, test_targets)
test_predictions = torch.argmax(test_y, 1)
test_accuracy = torch.sum(
test_predictions == test_targets).type(
torch.FloatTensor) / config.test_size
# #uncomment for printing
# 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,
# test_accuracy, test_loss))
results.write("%d %.3f %.3f\n" %
(step, test_accuracy, test_loss))
optimizer.step() if step == 0 else 0
if np.round(test_accuracy, 2) == 1.00:
print("Achieved >99.95% accuracy.")
break
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
print('Done training.')
results.close()
def train(config):
np.random.seed(42)
torch.manual_seed(42)
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
print(device)
# Initialize the model that we are going to use
if config.model_type=="RNN":
print("Training VanillaRNN")
print()
model = VanillaRNN(config.input_length, config.input_dim,\
config.num_hidden, config.num_classes, config.batch_size, config.device) # fixme
else:
print("Training LSTM")
print()
model = LSTM(config.input_length, config.input_dim,\
config.num_hidden, config.num_classes, config.batch_size, config.device)
model = model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length+1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss() #fixme
if config.optimizer=="adam":
optimizer = optim.Adam(model.parameters(), lr = config.learning_rate) # fixme
else:
optimizer = optim.RMSprop(model.parameters(), lr = config.learning_rate)
pl_loss =[]
average_loss =[]
acc =[]
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
batch_targets = torch.LongTensor(batch_targets)
batch_inputs, batch_targets = batch_inputs.to(device), batch_targets.to(device)
# zero the parameter gradients
model.zero_grad()
# Add more code here ...
output = model(batch_inputs)
out_loss = criterion(output, batch_targets)
out_loss.backward()
############################################################################
# QUESTION: what happens here and why?
# ANSWER: helps prevent the exploding gradient problem in RNNs / LSTMs.
############################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config.max_norm)
############################################################################
optimizer.step()
# Add more code here ...
loss = out_loss.item() # fixme
# get argmax
softmax = torch.nn.Softmax(dim=1)
predictions = torch.argmax(softmax(output), dim=1)
predictions = config.batch_size-len(torch.nonzero(predictions - batch_targets))
accuracy = predictions/config.batch_size
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size/float(t2-t1)
pl_loss.append(loss)
average_loss.append(np.mean(pl_loss[:-100:-1]))
acc.append(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,
config.train_steps, config.batch_size, examples_per_second,
accuracy, loss
))
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
# if step%100==0:
# # save training loss
# plt.plot(pl_loss,'r-', label="Batch loss", alpha=0.5)
# plt.plot(average_loss,'g-', label="Average loss", alpha=0.5)
# plt.legend()
# plt.xlabel("Iterations")
# plt.ylabel("Loss")
# plt.title("Training Loss")
# plt.grid(True)
# # plt.show()
# plt.savefig(config.optimizer+"_loss_"+config.model_type+"_"+str(config.input_length)+".png")
# plt.close()
################################training##################################################
# plt.plot(acc,'g-', alpha=0.5)
# plt.xlabel("Iterations")
# plt.ylabel("Accuracy")
# plt.title("Train Accuracy")
# plt.grid(True)
# plt.savefig("accuracy_"+config.sampling+"_"+str(config.temp)+".png")
# plt.close()
# fl = config.optimizer+"_acc_"+config.model_type+"_"+str(config.input_length)
# np.savez(fl, acc=acc)
print('Done training.')
def train(config, pallindrome_length, m):
config.input_length = pallindrome_length
config.model_type = m
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Initialize the model that we are going to use
hyper_params = [
config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device
]
model = globals()['Vanilla' + config.model_type](
*hyper_params) if config.model_type == 'RNN' else globals()[
config.model_type](*hyper_params)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=config.learning_rate)
accuracies = []
losses = []
avg_loss = 0
########## One hot encoding buffer that you create out of the loop and just keep reusing
# if config.input_dim != 1:
# nb_digits = 10
# x_onehot = torch.FloatTensor(config.batch_size, config.input_length, nb_digits)
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
# Only for time measurement of step through network
t1 = time.time()
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
optimizer.zero_grad()
# Forward pass:
########## Convert input to one-hot:
# if config.input_dim != 1:
# batch_inputs = batch_inputs.type(torch.LongTensor).view(config.batch_size, config.input_length, 1)
# x_onehot.zero_()
# x_onehot.scatter_(2, batch_inputs, 1)
# y_pred = model.forward(x_onehot)
# else:
# y_pred = model.forward(batch_inputs)
y_pred = model.forward(batch_inputs)
loss = criterion.forward(y_pred, batch_targets)
#Backward pass
loss.backward(retain_graph=True)
optimizer.step()
accuracy = (y_pred.argmax(
dim=1) == batch_targets).float().mean().item()
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
# accuracies.append(accuracy)
losses.append(loss.item())
if step % 500 == 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))
accuracies.append(accuracy)
if loss < 0.01 or accuracy == 1:
break
else:
avg_loss = np.average(losses)
losses = []
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
print('Done training.')
return max(accuracies)
def train(config):
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length + 1, config.input_dim,
config.num_hidden, config.num_classes, device)
elif config.model_type == 'LSTM':
model = LSTM(config.input_length + 1, config.input_dim,
config.num_hidden, config.num_classes, device)
else:
print("Unknown model type, please use RNN or LSTM")
exit()
model.store_hidden = True
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.RMSprop(model.parameters(), lr=config.learning_rate)
accuracies = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
############################################################################
# QUESTION: what happens here and why?
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
############################################################################
optimizer.zero_grad()
outputs = model(batch_inputs)
loss = criterion(outputs, batch_targets)
loss.backward()
loss = loss.data.item()
optimizer.step()
outputs = outputs.cpu().detach().numpy()
acc = accuracy(outputs, batch_targets.cpu().detach().numpy())
accuracies.append(acc)
grads = [
torch.norm(t.grad).cpu().detach() for t in model.hiddenActivity
]
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
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,
config.train_steps, config.batch_size, examples_per_second,
acc, loss))
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
print('Done training.')
drawPlotMagn(
grads,
'./' + str(config.model_type) + '_len:' + str(config.input_length) +
'_lr:' + str(config.learning_rate) + '_grads_over_time.jpg',
"Gradients over time steps with " + str(config.model_type), 1)
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Print all configs to confirm parameter settings
print_flags()
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device)
else:
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device)
model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.RMSprop(model.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay,
momentum=config.momentum)
# Store some measures
best_acc = 0.
los = list()
iteration = list()
tmp_acc = list()
acc = list()
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
optimizer.zero_grad()
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
pred = model(batch_inputs)
accuracy = compute_accuracy(pred, batch_targets)
tmp_acc.append(accuracy)
loss = criterion(pred, batch_targets)
loss.backward()
############################################################################
# QUESTION: what happens here and why?
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
############################################################################
optimizer.step()
# Just for time measurement
t2 = time.time()
if not float(t2 - t1) == 0:
examples_per_second = config.batch_size / float(t2 - t1)
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,
config.train_steps, config.batch_size, examples_per_second,
accuracy, loss))
iteration.append(step)
acc.append(accuracy)
los.append(loss)
if accuracy > best_acc:
best_acc = accuracy
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
print('Done training.')
tmp_acc.sort(reverse=True)
avg_acc = sum(tmp_acc[:50]) / 50
print('Average of 50 best accuracies: {}'.format(avg_acc))
with open('result/{}_acc.txt'.format(config.model_type), 'a') as file:
file.write('{} {}\n'.format(config.input_length, avg_acc))
file.close()
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].plot(iteration, acc)
axs[0].set_xlabel('Iteration')
axs[0].set_ylabel('Accuracy')
axs[1].plot(iteration, los)
axs[1].set_xlabel('Iteration')
axs[1].set_ylabel('Loss')
fig.tight_layout()
plt.show()
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
print('Currently using: ', device)
# Initialize the model that we are going to use
input_length = config.input_length
input_dim = config.input_dim
num_classes = config.num_classes
num_hidden = config.num_hidden
batch_size = config.batch_size
learning_rate = config.learning_rate
if config.model_type == 'RNN':
model = VanillaRNN(input_length, input_dim, num_hidden, num_classes
, batch_size, device).double()
if config.model_type == 'LSTM':
model = LSTM(input_length, input_dim, num_hidden, num_classes, batch_size, device).double()
model = model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(inp_len+1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss() # fixme
optimizer = torch.optim.RMSprop(model.parameters(), lr = learning_rate) # fixme
accuracy_list = []
loss_list = []
## first 100 steps are to generate the test set
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
output = model.forward(batch_inputs.transpose(0,1).double())
optimizer.zero_grad()
output_indices = torch.argmax(output.transpose(0,1), dim=0)
loss_for_backward = criterion(output,batch_targets).to(device)
loss_for_backward.backward()
############################################################################
# QUESTION: what happens here and why?
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(), max_norm=config.max_norm)
############################################################################
#print(output.shape)
#print(batch_targets.shape)
optimizer.step()
#loss = criterion.forward(output, batch_targets)
correct_indices = output_indices == batch_targets
#if step == 4000:
# return correct_indices, output_indices, batch_targets, batch_inputs
accuracy = int(sum(correct_indices))/int(len(correct_indices))
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size/float(t2-t1)
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,
config.train_steps, config.batch_size, examples_per_second,
accuracy, loss_for_backward
))
accuracy_list.append(accuracy)
loss_list.append(loss_for_backward)
if step == config.train_steps or (len(accuracy_list) > 10 and (sum(accuracy_list[-3:])
/len(accuracy_list[-3:])) == 1.0):
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
break
print('Done training.')
line = ' '.join((str(config.model_type),'Palindrome length:',str(input_length),'Accuracy:',str(accuracy_list),'Loss', str(loss_list)))
with open('LSTMMMMM.txt', 'a') as file:
file.write(line + '\n')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
# if GPU was chosen, check if CUDA is available
if str(config.device) != "cpu":
if not torch.cuda.is_available():
print('\n* GPU was selected but CUDA is not available.\nTraining on CPU ...')
device = torch.device("cpu")
else:
print('\nCUDA is available! Training on GPU ...')
device = torch.device(config.device)
else:
print('\nTraining on GPU ...')
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length, config.input_dim,
config.num_hidden, config.num_classes, config.batch_size, device)
else:
model = LSTM(config.input_length, config.input_dim,
config.num_hidden, config.num_classes, config.batch_size, device)
# Print Configuration
print("Model Type: {!s:5} Input Length: {!s:5} Learning Rate: {}\n"
.format(config.model_type, config.input_length, config.learning_rate))
# Initialize model
model = torch.nn.DataParallel(model).to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length+1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=config.learning_rate)
train_loss, train_accuracy, train_steps = [], [], []
# Enable train mode
model.train()
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# move tensors to GPU, if enabled
batch_targets = batch_targets.long().to(device)
batch_inputs = batch_inputs.to(device)
# Forward pass
predictions = model(batch_inputs)
# Calculate loss
loss = criterion(predictions, batch_targets)
# Back-propagate
loss.backward()
############################################################################
# QUESTION: what happens here and why?
# ANSWER: `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
# ref: https://medium.com/usf-msds/deep-learning-best-practices-1-weight-initialization-14e5c0295b94
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(), max_norm=config.max_norm)
############################################################################
# Update weights
optimizer.step()
# Clear weights gradients
optimizer.zero_grad()
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size/float(t2-t1)
if step % 10 == 0:
# Store accuracy and loss
train_steps.append(step)
train_loss.append(loss.item())
train_accuracy.append(accuracy(predictions, batch_targets))
if step % 100 == 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,
train_accuracy[-1], train_loss[-1]))
if step == config.train_steps:
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
# Save Train and Test accuracies and losses
file_name = str(config.model_type) + '_' + str(config.input_length) + '.npz'
np.savez(file_name,
train_steps=train_steps,
train_accuracy=train_accuracy,
model_type=config.model_type,
input_length=config.input_length)
break
print('Done training.')
def train(config,n_run):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Train on T-1 first digits
config.input_length = config.input_length - 1
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length, config.input_dim, config.num_hidden, config.num_classes, config.batch_size, device=device)
elif config.model_type == 'LSTM':
model = LSTM(config.input_length, config.input_dim, config.num_hidden, config.num_classes, config.batch_size, device=device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length+1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=config.learning_rate)
model.to(device)
train_loss = []
train_acc = []
t_loss = []
t_acc = []
#Convergence condition
eps = 1e-6
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Clear stored gradient
model.zero_grad()
# Only for time measurement of step through network
t1 = time.time()
# Add more code here ...
#Convert inputs and labels into tensors
x = torch.tensor(batch_inputs, device=device)
y = torch.tensor(batch_targets,device=device)
#Forward pass
pred = model.forward(x)
loss = criterion(pred, y)
t_loss.append(loss.item())
optimizer.zero_grad()
#Backward pass
loss.backward()
############################################################################
# QUESTION: what happens here and why?
# ANSWER : the function torch.nn.utils.clip_grad_norm() is used to prevent
# exploding gradients by ‘clipping’ the norm of the gradients, to restrain
# the gradient values to a certain threshold. This essentially acts as a
# limit to the size of the updates of the parameters of every layer, ensuring
# that the parameter values don't change too much from their previous values.
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(), max_norm=config.max_norm)
############################################################################
# Add more code here ...
optimizer.step()
accuracy = get_accuracy(pred,y, config.batch_size)
t_acc.append(accuracy.item())
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size/float(t2-t1)
if step % 1000 == 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
))
if step % 100 == 0:
#Get loss and accuracy averages over 100 steps
train_loss.append(np.mean(t_loss))
train_acc.append(np.mean(t_acc))
t_loss = []
t_acc = []
if step > 0 and abs(train_loss[-1] - train_loss[-2]) < eps:
break
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
print('\nDone training.\n')
#
#Save trained model and results
if config.model_type == 'RNN':
#save model
torch.save(model, "./Results/RNN/" + str(config.input_length) + "_RNN_model")
#save train accuracy and loss
np.save("./Results/RNN/" + str(config.input_length) + "_RNN_accuracy", train_acc)
np.save("./Results/RNN/" + str(config.input_length) + "_RNN_loss", train_loss)
# #save model ####################################################################### For SURFsara
# torch.save(model, str(config.input_length+1) + "_RNN_model_" + str(n_run))
# #save train accuracy and loss
# np.save(str(config.input_length+1) + "_RNN_accuracy_" + str(n_run), train_acc)
# np.save(str(config.input_length+1) + "_RNN_loss_" + str(n_run), train_loss)
elif config.model_type == 'LSTM':
#save model
torch.save(model, "./Results/LSTM/" + str(config.input_length) + "_LSTM_model")
#save train accuracy and loss
np.save("./Results/LSTM/" + str(config.input_length) + "_LSTM_accuracy", train_acc)
np.save("./Results/LSTM/" + str(config.input_length) + "_LSTM_loss", train_loss)
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, device) # fixme
else:
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, device)
print(model)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss() # fixme
optimizer = torch.optim.RMSprop(model.parameters(),
config.learning_rate) # fixme
optimizer.zero_grad()
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 ...
model_outputs = model.forward(batch_inputs)
############################################################################
# QUESTION: what happens here and why?
# This function clips the norm of the gradient to an acceptable level.
# It accually puts a limit of the update parameters.
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
############################################################################
# Add more code here ...
loss = criterion(torch.t(model_outputs), batch_targets) # fixme
accuracy = accuracy_(model_outputs, batch_targets) # fixme
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
# writer.add_scalar('accuracy',accuracy,step)
# writer.add_scalar('loss',loss,step)
# if loss < 0.001:
# writer.add_scalar('loss',loss,10000)
# writer.add_scalar('accuracy',accuracy,10000)
# break
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,
config.train_steps, config.batch_size, examples_per_second,
accuracy, loss))
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
print('Done training.')
def train(config):
# Initialize the device which to run the model on
device = torch.device(config.device)
# Setup the model that we are going to use
print("Initializing Vanilla RNN model...")
model = VanillaRNN(
seq_length=config.input_length,
input_dim=config.input_dim,
num_hidden=config.num_hidden,
num_classes=config.num_classes,
batch_size=config.batch_size,
device=device
)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length+1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
loss_function = torch.nn.NLLLoss()
optimizer = optim.RMSprop(model.parameters(), lr=config.learning_rate)
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.unsqueeze(-1) # add input dimensionality
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()
############################################################################
# QUESTION: 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)
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size/float(t2-t1)
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,
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
print('Done training.')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
if config.device == 'cuda':
if torch.cuda.is_available():
device = torch.device(config.device)
else:
device = torch.device('cpu')
else:
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(seq_length=config.input_length,
input_dim=config.input_dim,
num_hidden=config.num_hidden,
num_classes=config.num_classes,
batch_size=config.batch_size,
device=device)
elif config.model_type == 'LSTM':
model = LSTM(seq_length=config.input_length,
input_dim=config.input_dim,
num_hidden=config.num_hidden,
num_classes=config.num_classes,
batch_size=config.batch_size,
device=device)
# make the results directory (if it doesn't exist)
RESULTS_DIR = Path.cwd() / 'results'
RESULTS_DIR.mkdir(parents=True, exist_ok=True)
results_filepath = RESULTS_DIR / (model.__class__.__name__ + '.csv')
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=config.learning_rate)
results = {
'T': [],
'step': [],
'accuracy': [],
'loss': [],
}
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 ...
# send the data to device
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
# (re)set the optimizer gradient to 0
optimizer.zero_grad()
# forward pass the mini-batch
pred_targets = model.forward(batch_inputs)
loss = criterion.forward(pred_targets, batch_targets)
# backwards propogate the loss
loss.backward()
############################################################################
# QUESTION: what happens here and why?
# clip_grad_norm is deprecated, use clip_grad_norm_ instead
############################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
############################################################################
# Add more code here ...
optimizer.step()
accuracy = (pred_targets.argmax(dim=1) == batch_targets).float().mean()
# append the results
results['T'].append(config.input_length)
results['step'].append(step)
results['accuracy'].append(accuracy.item())
results['loss'].append(loss.item())
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % 1000 == 0:
print(
f'[{datetime.now().strftime("%Y-%m-%d %H:%M")}] Train Step {step:04d}/{config.train_steps:04d}, Batch Size = {config.batch_size}, Examples/Sec = {examples_per_second:.2f}, Accuracy = {accuracy:.2f}, Loss = {loss:.3f}'
)
if step == config.train_steps:
results_df = df.from_dict(results)
if not results_filepath.exists():
results_df.to_csv(results_filepath,
sep=';',
mode='w',
encoding='utf-8',
index=False)
else:
results_df.to_csv(results_filepath,
sep=';',
mode='a',
header=False,
encoding='utf-8',
index=False)
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
break
print('Done training.')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
# print(torch.nn.init.constant_(torch.empty(5), 0))
# print(asdasda)
# print(torch.nn.Parameter(torch.nn.init.normal_((torch.empty(5, 5)))))
if (config.model_type == 'RNN'):
model = VanillaRNN(config.input_length,
config.input_dim,
config.num_hidden,
config.num_classes,
config.batch_size,
device=device)
# model = model.to(device)
else:
model = LSTM(config.input_length,
config.input_dim,
config.num_hidden,
config.num_classes,
config.batch_size,
device=device)
# model = model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = nn.CrossEntropyLoss()
# import pdb
# pdb.set_trace()
optimizer = optim.RMSprop(
model.parameters(),
lr=config.learning_rate) #, weight_decay=1/(200*9))
# optimizer = optim.Adam(model.parameters(), lr=config.learning_rate) #, weight_decay=1/(200*9))
# optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate)
accuracies = []
losses = []
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 ...
# print(batch_inputs.shape[1])
# print(sadasd)
############################################################################
# QUESTION: what happens here and why?
# Clipping gradients helps prevent exploding gradients (hence clipping)
# However it does nothing against vanishing gradients for RNN's
# For vanishing gradients LSTMs are useful
############################################################################
############################################################################
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
out = model.forward(batch_inputs)
# Add more code here ...
# print(out.argmax(dim=1).shape, batch_targets.shape)
loss = criterion(out, batch_targets)
optimizer.zero_grad()
loss.backward()
# if (config.model_type == 'RNN'):
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
optimizer.step()
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % 100 == 0:
compare = (out.argmax(dim=1) == batch_targets)
summed = compare.sum().item()
accuracy = summed / compare.size()[0]
accuracies.append(accuracy)
losses.append(loss)
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))
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
plt.plot(accuracies, label='accuracies')
plt.plot(losses, label='losses')
plt.tight_layout()
plt.legend()
plt.show()
print('Done training.')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length, config.input_dim,
config.num_hidden, config.num_classes,
config.batch_size, config.device)
elif config.model_type == 'LSTM':
model = LSTM(config.input_length, config.input_dim, config.num_hidden,
config.num_classes, config.batch_size, config.device)
else:
AssertionError('Models available: RNN, LSTM')
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), config.learning_rate)
# keep track of variables
accuracy_list = []
loss_list = []
# loop through data (get batches)
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# get model predictions (USE THE PLAIN INTEGERS FROM THE PALINDROME TO INSERT INTO THE MODEL AS INPUT)
predictions = model(batch_inputs)
############################################################################
# QUESTION: what happens here and why?
#
# it clips the gradient to a border value, to prevent exploding or vanishing
# gradients.
#
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
############################################################################
# calculate loss
loss = criterion(predictions, batch_targets)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
targets = batch_targets.data.numpy()
predictions_np = predictions.data.numpy()
# get amount of correct predictions
correct = 0
for i in range(len(targets)):
if targets[i] == np.argmax(predictions_np[i]):
correct += 1
# get accuracy
accuracy = correct / len(targets)
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
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,
config.train_steps, config.batch_size, examples_per_second,
accuracy, loss))
# keep track of losses
loss_list.append(loss.data.numpy())
# keep track of accuracies
accuracy_list.append(accuracy)
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
# save variables
data = [loss_list, accuracy_list]
filename = 'Result_' + config.model_type + '_inputlen_' + str(
config.input_length) + '.p'
pickle.dump(data, open(filename, 'wb'))
print('Done training.')
def train(config, device="cpu"):
assert config.model_type in ('RNN', 'LSTM')
# Tensorboard summary writer
run_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S_" +
config.model_type.lower() + '_' +
str(config.input_length))
log_dir = 'tensorboard/' + config.model_type.lower() + '/' + run_id
writer = SummaryWriter(log_dir=log_dir)
# Torch settings
if device == 'cpu':
torch.set_default_tensor_type(torch.FloatTensor)
elif device == 'cuda:0':
torch.set_default_tensor_type(torch.cuda.FloatTensor)
dtype = torch.float
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(config.input_length,
config.input_dim,
config.num_hidden,
config.num_classes,
config.batch_size,
device=device).to(device)
elif config.model_type == 'LSTM':
model = LSTM(config.input_length,
config.input_dim,
config.num_hidden,
config.num_classes,
config.batch_size,
device=device).to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(),
lr=config.learning_rate)
# Accuracy and loss to be saved
accuracies = []
losses = []
# Useful for convergence check
avg_range = 200
last_accuracy = 0
convergence_threshold = 1e-4
model.train()
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
# Load batches in the GPU
batch_inputs = batch_inputs.to(device=device)
batch_targets = batch_targets.to(device=device)
# Forward pass
predictions = model.forward(batch_inputs)
# Compute loss
loss = criterion(predictions, batch_targets)
# Reset gradients before backwards pass
optimizer.zero_grad()
# Backward pass
loss.backward()
# Clipping gradients to avoid exploding gradient problem
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
# Update weights
optimizer.step()
# Compute accuracy
accuracy = get_accuracy(predictions, batch_targets)
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
# Add accuracy and loss to the writer
writer.add_scalars('accuracy_and_loss', {
'acc': accuracy,
'loss': loss
}, step)
# Store accuracy and loss
accuracies.append(accuracy)
losses.append(loss)
# Print information
if step % 100 == 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 for convergence
if step % avg_range == 0 and step != 0:
avg_accuracy = np.mean(accuracies[-avg_range:])
if np.abs(avg_accuracy - last_accuracy) < convergence_threshold:
print(
"The model has converged with accuracy", avg_accuracy,
"(" + ("+" if avg_accuracy > last_accuracy else "-") +
str(np.abs(avg_accuracy - last_accuracy)) + ")")
break
last_accuracy = avg_accuracy
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
save_results(accuracies, losses, run_id, config.model_type,
config.input_length, last_accuracy)
writer.close()
print('Done training. Accuracy:', avg_accuracy)
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device(config.device)
final_accuracy = []
seq_list = []
for i in range(30):
input_length = config.input_length + i
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(input_length,
config.input_dim,
config.num_hidden,
config.num_classes,
device=device)
elif config.model_type == 'LSTM':
model = LSTM(input_length,
config.input_dim,
config.num_hidden,
config.num_classes,
device=device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
optimizer = optim.RMSprop(model.parameters(), config.learning_rate)
criterion = nn.CrossEntropyLoss()
accuracies = []
losses = []
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
optimizer.zero_grad()
prediction = model(batch_inputs.to(device))
loss = criterion(prediction, batch_targets.to(device))
loss.backward()
############################################################################
# QUESTION: what happens here and why?
# this function causes the gradient not to explode
############################################################################
torch.nn.utils.clip_grad_norm(model.parameters(),
max_norm=config.max_norm)
############################################################################
optimizer.step()
_, predicted = torch.max(prediction, 1)
accuracy = (predicted == batch_targets.to(device)
).sum().item() / len(predicted)
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
accuracies.append(accuracy * 100)
losses.append(loss)
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,
config.train_steps, config.batch_size,
examples_per_second, accuracy, loss))
if step % 100 == 0:
# go on with training if the model predicted with 100% accuracy for the last 100 steps
accuracy_average = sum(accuracies[-100:]) / 100
if accuracy_average == 100:
break
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
final_accuracy.append(accuracy_average)
seq_list.append(input_length)
print('Done training.')
def train(config):
assert config.model_type in ('RNN', 'LSTM')
# Initialize the device which to run the model on
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Initialize the model that we are going to use
if config.model_type == 'RNN':
model = VanillaRNN(seq_length=config.input_length,
input_dim=config.input_dim,
num_hidden=config.num_hidden,
num_classes=config.num_classes,
batch_size=config.batch_size,
device=device)
elif config.model_type == 'LSTM':
model = LSTM(seq_length=config.input_length,
input_dim=config.input_dim,
num_hidden=config.num_hidden,
num_classes=config.num_classes,
batch_size=config.batch_size,
device=device)
model.to(device)
# Initialize the dataset and data loader (note the +1)
dataset = PalindromeDataset(config.input_length + 1)
data_loader = DataLoader(dataset, config.batch_size, num_workers=1)
# Setup the loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(params=model.parameters(),
lr=config.learning_rate)
# evaluation metrics
results = []
print_setting(config)
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# Only for time measurement of step through network
t1 = time.time()
batch_inputs = batch_inputs.to(device)
batch_targets = batch_targets.to(device)
s_inputs = batch_inputs.shape
s_targets = batch_targets.shape
#forward pass
predictions = model.forward(batch_inputs)
#compute loss
loss = criterion(predictions, batch_targets)
#backward pass & updates
# set gradients to zero
optimizer.zero_grad()
loss.backward()
############################################################################
# QUESTION: what happens here and why?
# Prevents exploding gradients by rescaling to a limit specified by config.max_norm
# Forcing gradients to be within a certain norm to ensure reasonable updates
############################################################################
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=config.max_norm)
############################################################################
optimizer.step()
accuracy = (predictions.argmax(dim=1)
== batch_targets).sum().float() / (config.batch_size)
# Just for time measurement
t2 = time.time()
examples_per_second = config.batch_size / float(t2 - t1)
if step % config.eval_freq == 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))
#l = loss.float().item()
results.append([step, accuracy.item(), loss.float().item()])
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
print('Done training. \n')
return results
X[:, t, :].view(1, -1, 1), X[:, t, :].view(1, -1, 1),
X[:, t, :].view(1, -1, 1), X[:, t, :].view(1, -1, 1)
],
dim=0)
# term1 = torch.einsum('kij,bij->kji', X_in, self.W_x) # kij,kij->kij , kij,kjl->kil
tmm1 = torch.einsum('kij,kjl->kil', X_t, self.W_x)
tmm2 = torch.einsum('ij,kjl->kil', h_t, self.W_h)
gates_t = self.activation_gates(tmm1 + tmm2 + self.bias_gates)
c_t = gates_t[0, :, :] * gates_t[1, :, :] + c_t * gates_t[2, :, :]
h_t = self.activation_hidden(c_t) * gates_t[3, :, :]
return torch.matmul(h_t, self.W_p) + self.bias_p
if __name__ == '__main__':
palindrom_generator = PalindromeDataset(3)
pali = palindrom_generator.generate_palindrome()
print(pali)
############################### Comment out Before Submission #######################
# defaults
config = {
'model_type': 'LSTM',
'seq_length': 5,
'input_length': 10,
'input_dim': 1,
'num_classes': 10,
'num_hidden': 100,
'batch_size': 128,
'learning_rate': 0.001,
'train_steps': 10000,