def loop_section():
for step in logger.loop(range(0, 10)):
with logger.section("Step"):
time.sleep(0.5)
with logger.section("Step2"):
time.sleep(0.1)
logger.write()
logger.new_line()
def loop(self):
# Loop through the monitored iterator
for epoch in logger.loop(range(0, self.__epochs)):
self._train()
self._test()
self.__log_model_params()
# Clear line and output to console
logger.write()
# Clear line and go to the next line;
# that is, we add a new line to the output
# at the end of each epoch
if (epoch + 1) % self.__log_new_line_interval == 0:
logger.new_line()
if self.__is_save_models:
logger.save_checkpoint()
def main():
args = parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
# Loading data
with logger.section("Loading data"):
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'./data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
# Model creation
with logger.section("Create model"):
model = Net().to(device)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum)
# Specify indicators
logger.add_indicator("train_loss", queue_limit=10, is_print=True)
logger.add_indicator("test_loss", is_histogram=False, is_print=True)
logger.add_indicator("accuracy", is_histogram=False, is_print=True)
for name, param in model.named_parameters():
if param.requires_grad:
logger.add_indicator(name, is_histogram=True, is_print=False)
logger.add_indicator(f"{name}_grad",
is_histogram=True,
is_print=False)
# Start the experiment
EXPERIMENT.start_train()
# Loop through the monitored iterator
for epoch in logger.loop(range(0, args.epochs)):
# Delayed keyboard interrupt handling to use
# keyboard interrupts to end the loop.
# This will capture interrupts and finish
# the loop at the end of processing the iteration;
# i.e. the loop won't stop in the middle of an epoch.
try:
with logger.delayed_keyboard_interrupt():
# Training and testing
train(args, model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
# Add histograms with model parameter values and gradients
for name, param in model.named_parameters():
if param.requires_grad:
logger.store(name, param.data.cpu().numpy())
logger.store(f"{name}_grad", param.grad.cpu().numpy())
# Clear line and output to console
logger.write()
# Output the progress summaries to `trial.yaml` and
# to the python file header
logger.save_progress()
# Clear line and go to the next line;
# that is, we add a new line to the output
# at the end of each epoch
logger.new_line()
# Handled delayed interrupt
except KeyboardInterrupt:
logger.finish_loop()
logger.new_line()
logger.log("\nKilling loop...")
break
is_histogram=False,
is_print=False,
is_pair=True)
# Create a TensorFlow session
with tf.Session() as session:
# Start the experiment from scratch, without loading from a
# saved checkpoint ('is_init=True')
# This will clear all the old checkpoints and summaries for this
# experiment.
# If you start with 'is_init=False',
# the experiment will load from the last saved checkpoint.
EXPERIMENT.start_train(session, is_init=True)
# This is the main training loop of this project.
for global_step in logger.loop(range(50)):
# You can set the global step explicitly with
# 'logger.set_global_step(global_step)'
# Handle Keyboard Interrupts
try:
with logger.delayed_keyboard_interrupt():
# A sample monitored section inside iterator
with logger.section("sample"):
time.sleep(0.5)
# A silent section is used only to organize code.
# It produces no output
with logger.section("logging", is_silent=True):
# Store values
logger.store(reward=global_step / 3.0, fps=12)
def main():
args = parse_args()
# Loading data
with logger.section("Load data"):
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
train_dataset = create_mnist_dataset(x_train, y_train, args.batch_size)
test_dataset = create_mnist_dataset(x_test, y_test, args.batch_size)
# Model creation
with logger.section("Create model"):
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
# Creation of the trainer
with logger.section("Create trainer"):
optimizer = tf.train.AdamOptimizer(learning_rate=args.lr)
train_iterator = train_dataset.make_initializable_iterator()
data, target = train_iterator.get_next()
train_loss = loss(model, data, target)
train_op = optimizer.minimize(train_loss)
test_iterator = test_dataset.make_initializable_iterator()
data, target = test_iterator.get_next()
test_loss = loss(model, data, target)
test_accuracy = accuracy(model, data, target)
logger.add_indicator("train_loss", queue_limit=10, is_print=True)
logger.add_indicator("test_loss", is_histogram=False, is_print=True)
logger.add_indicator("accuracy", is_histogram=False, is_print=True)
#
batches = len(x_train) // args.batch_size
with tf.Session() as session:
EXPERIMENT.start_train(session)
# Loop through the monitored iterator
for epoch in logger.loop(range(0, args.epochs)):
# Delayed keyboard interrupt handling to use
# keyboard interrupts to end the loop.
# This will capture interrupts and finish
# the loop at the end of processing the iteration;
# i.e. the loop won't stop in the middle of an epoch.
try:
with logger.delayed_keyboard_interrupt():
# Training and testing
session.run(train_iterator.initializer)
train(args, session, train_loss, train_op, batches, epoch)
session.run(test_iterator.initializer)
test(session, test_loss, test_accuracy,
len(x_test) // args.batch_size)
# Clear line and output to console
logger.write()
# Output the progress summaries to `trial.yaml` and
# to the python file header
logger.save_progress()
# Clear line and go to the next line;
# that is, we add a new line to the output
# at the end of each epoch
logger.new_line()
# Handled delayed interrupt
except KeyboardInterrupt:
logger.finish_loop()
logger.new_line()
logger.log("\nKilling loop...")
break
def loop_partial_section():
for step in logger.loop(range(0, 10)):
with logger.section("Step", is_partial=True):
time.sleep(0.5)
logger.progress((step % 5 + 1) / 5)
logger.write()