def train(ladder,
batch_size=100,
num_train_examples=60000,
num_epochs=150,
lrate_decay=0.67):
# Setting Logger
timestr = time.strftime("%Y_%m_%d_at_%H_%M")
save_path = 'results/mnist_100_standard_' + timestr
log_path = os.path.join(save_path, 'log.txt')
os.makedirs(save_path)
fh = logging.FileHandler(filename=log_path)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
# Training
model = Model(ladder.costs.total)
all_params = model.parameters
print len(all_params)
print all_params
training_algorithm = GradientDescent(
cost=ladder.costs.total,
parameters=all_params,
step_rule=Adam(learning_rate=ladder.lr))
# Fetch all batch normalization updates. They are in the clean path.
# In addition to actual training, also do BN variable approximations
bn_updates = ComputationGraph([ladder.costs.class_clean]).updates
training_algorithm.add_updates(bn_updates)
monitored_variables = [
ladder.costs.class_corr, ladder.costs.class_clean, ladder.error,
training_algorithm.total_gradient_norm, ladder.costs.total
] + ladder.costs.denois.values()
train_data_stream, test_data_stream = get_mixed_streams(batch_size)
train_monitoring = TrainingDataMonitoring(variables=monitored_variables,
prefix="train",
after_epoch=True)
valid_monitoring = DataStreamMonitoring(variables=monitored_variables,
data_stream=test_data_stream,
prefix="test",
after_epoch=True)
main_loop = MainLoop(algorithm=training_algorithm,
data_stream=train_data_stream,
model=model,
extensions=[
train_monitoring, valid_monitoring,
FinishAfter(after_n_epochs=num_epochs),
SaveParams('test_CE_corr', model, save_path),
SaveLog(save_path, after_epoch=True),
LRDecay(lr=ladder.lr,
decay_first=num_epochs * lrate_decay,
decay_last=num_epochs,
after_epoch=True),
Printing()
])
main_loop.run()
def train(cli_params):
cli_params['save_dir'] = prepare_dir(cli_params['save_to'])
logfile = os.path.join(cli_params['save_dir'], 'log.txt')
# Log also DEBUG to a file
fh = logging.FileHandler(filename=logfile)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
logger.info('Logging into %s' % logfile)
p, loaded = load_and_log_params(cli_params)
in_dim, data, whiten, cnorm = setup_data(p, test_set=False)
if not loaded:
# Set the zero layer to match input dimensions
p.encoder_layers = (in_dim,) + p.encoder_layers
ladder = setup_model(p)
# Training
all_params = ComputationGraph([ladder.costs.total]).parameters
logger.info('Found the following parameters: %s' % str(all_params))
# Fetch all batch normalization updates. They are in the clean path.
bn_updates = ComputationGraph([ladder.costs.class_clean]).updates
assert 'counter' in [u.name for u in bn_updates.keys()], \
'No batch norm params in graph - the graph has been cut?'
training_algorithm = GradientDescent(
cost=ladder.costs.total, params=all_params,
step_rule=Adam(learning_rate=ladder.lr))
# In addition to actual training, also do BN variable approximations
training_algorithm.add_updates(bn_updates)
short_prints = {
"train": {
'T_C_class': ladder.costs.class_corr,
'T_C_de': ladder.costs.denois.values(),
},
"valid_approx": OrderedDict([
('V_C_class', ladder.costs.class_clean),
('V_E', ladder.error.clean),
('V_C_de', ladder.costs.denois.values()),
]),
"valid_final": OrderedDict([
('VF_C_class', ladder.costs.class_clean),
('VF_E', ladder.error.clean),
('VF_C_de', ladder.costs.denois.values()),
]),
}
main_loop = MainLoop(
training_algorithm,
# Datastream used for training
make_datastream(data.train, data.train_ind,
p.batch_size,
n_labeled=p.labeled_samples,
n_unlabeled=p.unlabeled_samples,
whiten=whiten,
cnorm=cnorm),
model=Model(ladder.costs.total),
extensions=[
FinishAfter(after_n_epochs=p.num_epochs),
# This will estimate the validation error using
# running average estimates of the batch normalization
# parameters, mean and variance
ApproxTestMonitoring(
[ladder.costs.class_clean, ladder.error.clean]
+ ladder.costs.denois.values(),
make_datastream(data.valid, data.valid_ind,
p.valid_batch_size, whiten=whiten, cnorm=cnorm,
scheme=ShuffledScheme),
prefix="valid_approx"),
# This Monitor is slower, but more accurate since it will first
# estimate batch normalization parameters from training data and
# then do another pass to calculate the validation error.
FinalTestMonitoring(
[ladder.costs.class_clean, ladder.error.clean]
+ ladder.costs.denois.values(),
make_datastream(data.train, data.train_ind,
p.batch_size,
n_labeled=p.labeled_samples,
whiten=whiten, cnorm=cnorm,
scheme=ShuffledScheme),
make_datastream(data.valid, data.valid_ind,
p.valid_batch_size,
n_labeled=len(data.valid_ind),
whiten=whiten, cnorm=cnorm,
scheme=ShuffledScheme),
prefix="valid_final",
after_n_epochs=p.num_epochs),
TrainingDataMonitoring(
[ladder.costs.total, ladder.costs.class_corr,
training_algorithm.total_gradient_norm]
+ ladder.costs.denois.values(),
prefix="train", after_epoch=True),
SaveParams(None, all_params, p.save_dir, after_epoch=True),
SaveExpParams(p, p.save_dir, before_training=True),
SaveLog(p.save_dir, after_training=True),
ShortPrinting(short_prints),
LRDecay(ladder.lr, p.num_epochs * p.lrate_decay, p.num_epochs,
after_epoch=True),
])
main_loop.run()
# Get results
df = main_loop.log.to_dataframe()
col = 'valid_final_error_rate_clean'
logger.info('%s %g' % (col, df[col].iloc[-1]))
if main_loop.log.status['epoch_interrupt_received']:
return None
return df
def train(self):
""" Setup and train the model """
to_train = ComputationGraph([self.tagger.total_cost]).parameters
logger.info('Found the following parameters: %s' % str(to_train))
step_rule = Adam(learning_rate=self.p.lr)
training_algorithm = GradientDescent(
cost=self.tagger.total_cost, parameters=to_train, step_rule=step_rule,
on_unused_sources='warn',
theano_func_kwargs={'on_unused_input': 'warn'}
)
# TRACKED GRAPH NODES
train_params = {
'Train_Denoising_Cost': self.tagger.corr.denoising_cost,
}
if self.p.class_cost_x > 0:
train_params['Train_Classification_Cost'] = self.tagger.corr.class_cost
train_params['Train_Classification_Error'] = self.tagger.clean.class_error
valid_params = {
'Validation_Denoising_Cost': self.tagger.corr.denoising_cost,
}
if self.p.class_cost_x > 0:
valid_params['Validation_Classification_Cost'] = self.tagger.corr.class_cost
valid_params['Validation_Classification_Error'] = self.tagger.clean.class_error
test_params = {
'Test_AMI_Score': self.tagger.clean.ami_score,
'Test_Denoising_Cost': self.tagger.corr.denoising_cost,
}
if self.p.class_cost_x > 0:
test_params['Test_Classification_Cost'] = self.tagger.corr.class_cost
test_params['Test_Classification_Error'] = self.tagger.clean.class_error
short_prints = {
"train": train_params,
"valid": valid_params,
"test": test_params,
}
main_loop = MainLoop(
training_algorithm,
# Datastream used for training
self.streams['train'],
model=Model(self.tagger.total_cost),
extensions=[
FinishAfter(after_n_epochs=self.p.num_epochs),
SaveParams(self.p.get('save_freq', 0), self.tagger, self.save_dir, before_epoch=True),
DataStreamMonitoring(
valid_params.values(),
self.streams['valid'],
prefix="valid"
),
FinalTestMonitoring(
test_params.values(),
self.streams['train'],
{'valid': self.streams['valid'], 'test': self.streams['test']},
after_training=True
),
TrainingDataMonitoring(
train_params.values(),
prefix="train", after_epoch=True
),
SaveExpParams(self.p, self.save_dir, before_training=True),
Timing(after_epoch=True),
ShortPrinting(short_prints, after_epoch=True),
])
logger.info('Running the main loop')
main_loop.run()
def train(model, configs):
get_streams = configs['get_streams']
save_path = configs['save_path']
num_epochs = configs['num_epochs']
batch_size = configs['batch_size']
lrs = configs['lrs']
until_which_epoch = configs['until_which_epoch']
grad_clipping = configs['grad_clipping']
monitorings = model.monitorings
# Training
if configs['weight_noise'] > 0:
cg = ComputationGraph(model.cost)
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
cg = apply_noise(cg, weights, configs['weight_noise'])
model.cost = cg.outputs[0].copy(name='CE')
if configs['l2_reg'] > 0:
cg = ComputationGraph(model.cost)
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
new_cost = model.cost + configs['l2_reg'] * sum(
[(weight**2).sum() for weight in weights])
model.cost = new_cost.copy(name='CE')
blocks_model = Model(model.cost)
all_params = blocks_model.parameters
print "Number of found parameters:" + str(len(all_params))
print all_params
default_lr = np.float32(configs['lrs'][0])
lr_var = theano.shared(default_lr, name="learning_rate")
clipping = StepClipping(threshold=np.cast[floatX](grad_clipping))
# sgd_momentum = Momentum(
# learning_rate=0.0001,
# momentum=0.95)
# step_rule = CompositeRule([clipping, sgd_momentum])
adam = Adam(learning_rate=lr_var)
step_rule = CompositeRule([clipping, adam])
training_algorithm = GradientDescent(cost=model.cost,
parameters=all_params,
step_rule=step_rule,
on_unused_sources='warn')
monitored_variables = [
lr_var,
aggregation.mean(training_algorithm.total_gradient_norm)
] + monitorings
for param in all_params:
name = param.tag.annotations[0].name + "." + param.name
to_monitor = training_algorithm.gradients[param].norm(2)
to_monitor.name = name + "_grad_norm"
monitored_variables.append(to_monitor)
to_monitor = param.norm(2)
to_monitor.name = name + "_norm"
monitored_variables.append(to_monitor)
train_data_stream, valid_data_stream = get_streams(batch_size)
train_monitoring = TrainingDataMonitoring(variables=monitored_variables,
prefix="train",
after_epoch=True)
valid_monitoring = DataStreamMonitoring(variables=monitored_variables,
data_stream=valid_data_stream,
prefix="valid",
after_epoch=True)
main_loop = MainLoop(
algorithm=training_algorithm,
data_stream=train_data_stream,
model=blocks_model,
extensions=[
train_monitoring,
valid_monitoring,
FinishAfter(after_n_epochs=num_epochs),
SaveParams('valid_CE', blocks_model, save_path, after_epoch=True),
SaveLog(after_epoch=True),
ProgressBar(),
# ErrorPerVideo(model, after_epoch=True, on_interrupt=True),
LRDecay(lr_var, lrs, until_which_epoch, after_epoch=True),
Printing(after_epoch=True)
])
main_loop.run()
def train(model, batch_size=100, num_epochs=1000):
cost = model.cost
monitorings = model.monitorings
# Setting Loggesetr
timestr = time.strftime("%Y_%m_%d_at_%H_%M")
save_path = 'results/CMV_V2_' + timestr
log_path = os.path.join(save_path, 'log.txt')
os.makedirs(save_path)
fh = logging.FileHandler(filename=log_path)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
# Training
blocks_model = Model(cost)
all_params = blocks_model.parameters
print "Number of found parameters:" + str(len(all_params))
print all_params
clipping = StepClipping(threshold=np.cast[floatX](10))
adam = Adam(learning_rate=model.lr_var)
step_rule = CompositeRule([adam, clipping])
training_algorithm = GradientDescent(cost=cost,
parameters=all_params,
step_rule=step_rule)
monitored_variables = [
model.lr_var, cost,
aggregation.mean(training_algorithm.total_gradient_norm)
] + monitorings
blocks_model = Model(cost)
params_dicts = blocks_model.get_parameter_dict()
for name, param in params_dicts.iteritems():
to_monitor = training_algorithm.gradients[param].norm(2)
to_monitor.name = name + "_grad_norm"
monitored_variables.append(to_monitor)
to_monitor = param.norm(2)
to_monitor.name = name + "_norm"
monitored_variables.append(to_monitor)
train_data_stream, valid_data_stream = get_cmv_v2_streams(batch_size)
train_monitoring = TrainingDataMonitoring(variables=monitored_variables,
prefix="train",
after_epoch=True)
valid_monitoring = DataStreamMonitoring(variables=monitored_variables,
data_stream=valid_data_stream,
prefix="valid",
after_epoch=True)
main_loop = MainLoop(
algorithm=training_algorithm,
data_stream=train_data_stream,
model=blocks_model,
extensions=[
train_monitoring, valid_monitoring,
FinishAfter(after_n_epochs=num_epochs),
SaveParams('valid_misclassificationrate_apply_error_rate',
blocks_model, save_path),
SaveLog(save_path, after_epoch=True),
ProgressBar(),
LRDecay(model.lr_var, [0.001, 0.0001, 0.00001, 0.000001],
[8, 15, 30, 1000],
after_epoch=True),
Printing()
])
main_loop.run()
