class Dump(SimpleExtension):
"""Dumps the state of the main loop.
Makes a `SAVED_TO` record in the log with the dumping destination
in the case of success and ``None`` in the case of failure.
Parameters
----------
state_path : str
The folder to dump the state to. Will be created it does not
exist.
Notes
-----
Requires the model to be a Brick or a list of Bricks.
"""
def __init__(self, state_path, **kwargs):
kwargs.setdefault("after_training", True)
super(Dump, self).__init__(**kwargs)
self.manager = MainLoopDumpManager(state_path)
def do(self, callback_name, *args, **kwargs):
try:
self.main_loop.log.current_row[SAVED_TO] = (self.manager.folder)
self.manager.dump(self.main_loop)
except Exception:
self.main_loop.log.current_row[SAVED_TO] = None
raise
class Dump(SimpleExtension):
"""Dumps the state of the main loop.
Makes a `SAVED_TO` record in the log with the dumping destination
in the case of success and ``None`` in the case of failure.
Parameters
----------
state_path : str
The folder to dump the state to. Will be created it does not
exist.
Notes
-----
Requires the model to be a Brick or a list of Bricks.
"""
def __init__(self, state_path, **kwargs):
kwargs.setdefault("after_training", True)
super(Dump, self).__init__(**kwargs)
self.manager = MainLoopDumpManager(state_path)
def do(self, callback_name, *args, **kwargs):
try:
self.main_loop.log.current_row[SAVED_TO] = (
self.manager.folder)
self.manager.dump(self.main_loop)
except Exception:
self.main_loop.log.current_row[SAVED_TO] = None
raise
def train_model(cost,
train_stream,
valid_stream,
valid_freq,
valid_rare,
load_location=None,
save_location=None):
cost.name = 'nll'
perplexity = 2**(cost / tensor.log(2))
perplexity.name = 'ppl'
# Define the model
model = Model(cost)
# Load the parameters from a dumped model
if load_location is not None:
logger.info('Loading parameters...')
model.set_param_values(load_parameter_values(load_location))
cg = ComputationGraph(cost)
algorithm = GradientDescent(cost=cost,
step_rule=Scale(learning_rate=0.01),
params=cg.parameters)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=[
DataStreamMonitoring([cost, perplexity],
valid_stream,
prefix='valid_all',
every_n_batches=5000),
# Overfitting of rare words occurs between 3000 and 4000 iterations
DataStreamMonitoring([cost, perplexity],
valid_rare,
prefix='valid_rare',
every_n_batches=500),
DataStreamMonitoring([cost, perplexity],
valid_freq,
prefix='valid_frequent',
every_n_batches=5000),
Printing(every_n_batches=500)
])
main_loop.run()
# Save the main loop
if save_location is not None:
logger.info('Saving the main loop...')
dump_manager = MainLoopDumpManager(save_location)
dump_manager.dump(main_loop)
logger.info('Saved')
def train_model(cost,
error_rate,
train_stream,
load_location=None,
save_location=None):
cost.name = "Cross_entropy"
error_rate.name = 'Error_rate'
# Define the model
model = Model(cost)
# Load the parameters from a dumped model
if load_location is not None:
logger.info('Loading parameters...')
model.set_param_values(load_parameter_values(load_location))
cg = ComputationGraph(cost)
step_rule = Momentum(learning_rate=0.1, momentum=0.9)
algorithm = GradientDescent(cost=cost,
step_rule=step_rule,
params=cg.parameters)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=[
# DataStreamMonitoring([cost], test_stream, prefix='test',
# after_epoch=False, every_n_epochs=10),
DataStreamMonitoring([cost],
train_stream,
prefix='train',
after_epoch=True),
Printing(after_epoch=True)
])
main_loop.run()
# Save the main loop
if save_location is not None:
logger.info('Saving the main loop...')
dump_manager = MainLoopDumpManager(save_location)
dump_manager.dump(main_loop)
logger.info('Saved')
def train_model(cost, train_stream, valid_stream, valid_freq, valid_rare,
load_location=None, save_location=None):
cost.name = 'nll'
perplexity = 2 ** (cost / tensor.log(2))
perplexity.name = 'ppl'
# Define the model
model = Model(cost)
# Load the parameters from a dumped model
if load_location is not None:
logger.info('Loading parameters...')
model.set_param_values(load_parameter_values(load_location))
cg = ComputationGraph(cost)
algorithm = GradientDescent(cost=cost, step_rule=Scale(learning_rate=0.01),
params=cg.parameters)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=[
DataStreamMonitoring([cost, perplexity], valid_stream,
prefix='valid_all', every_n_batches=5000),
# Overfitting of rare words occurs between 3000 and 4000 iterations
DataStreamMonitoring([cost, perplexity], valid_rare,
prefix='valid_rare', every_n_batches=500),
DataStreamMonitoring([cost, perplexity], valid_freq,
prefix='valid_frequent',
every_n_batches=5000),
Printing(every_n_batches=500)
]
)
main_loop.run()
# Save the main loop
if save_location is not None:
logger.info('Saving the main loop...')
dump_manager = MainLoopDumpManager(save_location)
dump_manager.dump(main_loop)
logger.info('Saved')
def train_model(cost, error_rate, train_stream,
load_location=None, save_location=None):
cost.name = "Cross_entropy"
error_rate.name = 'Error_rate'
# Define the model
model = Model(cost)
# Load the parameters from a dumped model
if load_location is not None:
logger.info('Loading parameters...')
model.set_param_values(load_parameter_values(load_location))
cg = ComputationGraph(cost)
step_rule = Momentum(learning_rate=0.1, momentum=0.9)
algorithm = GradientDescent(cost=cost, step_rule=step_rule,
params=cg.parameters)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=[
# DataStreamMonitoring([cost], test_stream, prefix='test',
# after_epoch=False, every_n_epochs=10),
DataStreamMonitoring([cost], train_stream, prefix='train',
after_epoch=True),
Printing(after_epoch=True)
]
)
main_loop.run()
# Save the main loop
if save_location is not None:
logger.info('Saving the main loop...')
dump_manager = MainLoopDumpManager(save_location)
dump_manager.dump(main_loop)
logger.info('Saved')