def evaluate(ladder, load_path):
with open(load_path + '/trained_params_best.npz') as f:
loaded = np.load(f)
model = Model(ladder.costs.total)
params_dicts = model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
# '/f_6_.W' --> 'f_6_.W'
slash_index = param_name.find('/')
param_name = param_name[slash_index + 1:]
assert param.get_value().shape == loaded[param_name].shape
param.set_value(loaded[param_name])
test_data_stream, test_data_stream = get_mixed_streams(10000)
test_data = test_data_stream.get_epoch_iterator().next()
test_data_input = test_data[10]
test_data_target = test_data[0]
print 'Compiling ...'
cg = ComputationGraph([ladder.costs.total])
eval_ = theano.function(cg.inputs, ladder.error)
print 'Test_set_Error: ' + str(eval_(test_data_input, test_data_target))
import ipdb
ipdb.set_trace()
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(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()
