def loop():
logger.info(a=2, b=1)
logger.add_indicator(Queue("reward", 10, True))
for i in range(100):
logger.write(i, loss=100 / (i + 1), reward=math.pow(2, (i + 1)))
if (i + 1) % 2 == 0:
logger.write(valid=i**10)
logger.new_line()
time.sleep(0.3)
def loop():
logger.info(a=2, b=1)
logger.add_indicator('loss_ma', IndicatorType.queue,
IndicatorOptions(queue_size=10))
for i in range(10):
logger.add_global_step(1)
logger.store(loss=100 / (i + 1), loss_ma=100 / (i + 1))
logger.write()
if (i + 1) % 2 == 0:
logger.new_line()
time.sleep(2)
def __call__(self):
# Training and testing
logger_util.add_model_indicators(self.model)
logger.add_indicator(Queue("train_loss", 20, True))
logger.add_indicator(Histogram("test_loss", True))
logger.add_indicator(Histogram("accuracy", True))
logger.add_indicator(IndexedScalar('test_sample_loss'))
logger.add_indicator(IndexedScalar('test_sample_pred'))
test_data = np.array([d[0].numpy() for d in self.test_loader.dataset])
logger.save_numpy("test_data", test_data)
for _ in self.loop:
self._train()
self._test()
self.__log_model_params()
def startup(self):
logger_util.add_model_indicators(self.model)
logger.add_indicator("train_loss", IndicatorType.queue,
IndicatorOptions(queue_size=20, is_print=True))
logger.add_indicator("test_loss", IndicatorType.histogram,
IndicatorOptions(is_print=True))
logger.add_indicator("accuracy", IndicatorType.histogram,
IndicatorOptions(is_print=True))
def __call__(self):
logger_util.add_model_indicators(self.model)
logger.add_indicator(Queue("train_loss", 20, True))
logger.add_indicator(Histogram("test_loss", True))
logger.add_indicator(Histogram("accuracy", True))
for _ in self.loop:
self._train()
self._test()
self.__log_model_params()
def __call__(self):
# Training and testing
logger_util.add_model_indicators(self.model)
logger.add_indicator("train_loss", IndicatorType.queue,
IndicatorOptions(queue_size=20, is_print=True))
logger.add_indicator("test_loss", IndicatorType.histogram,
IndicatorOptions(is_print=True))
logger.add_indicator("accuracy", IndicatorType.histogram,
IndicatorOptions(is_print=True))
for _ in self.loop:
self._train()
self._test()
self.__log_model_params()
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
def add_model_indicators(model: torch.nn.Module, model_name: str = "model"):
for name, param in model.named_parameters():
if param.requires_grad:
logger.add_indicator(Histogram(f"{model_name}.{name}"))
logger.add_indicator(Histogram(f"{model_name}.{name}.grad"))
def startup(self):
logger_util.add_model_indicators(self.model)
logger.add_indicator(Queue("train_loss", 20, True))
logger.add_indicator(Histogram("test_loss", True))
logger.add_indicator(Histogram("accuracy", True))
with logger.section("Create model"):
# Indicate that this section failed. You don't have to set
# this if it is successful.
logger.set_successful(False)
# Sleep for a minute.
time.sleep(1)
# Print sample info
logger.info(one=1, two=2, string="string")
# ### Set logger indicators
# Reward is queued; this is useful when you want to track the moving
# average of something.
logger.add_indicator("reward", queue_limit=10)
# By default everything is a set of values and will create a TensorBoard histogram
# We specify that `fps` is a scalar.
# If you store multiple values for this it will output the mean.
logger.add_indicator("fps", is_histogram=False, is_print=False)
# This will produce a histogram
logger.add_indicator("loss", is_print=False)
# A heat map
logger.add_indicator("advantage_reward",
is_histogram=False,
is_print=False,
is_pair=True)
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
