def run(self):
dataset = self.build_dataset()
learning_rule = self.build_learning_rule()
learn_method = self.build_learning_method()
if self.state.num_layers == 1:
model = self.build_one_hid_model(dataset.feature_size())
elif self.state.num_layers == 2:
model = self.build_two_hid_model(dataset.feature_size())
elif self.state.num_layers == 3:
model = self.build_three_hid_model(dataset.feature_size())
else:
raise ValueError()
database = self.build_database(dataset, learning_rule, learn_method, model)
log = self.build_log(database)
dataset.log = log
train_obj = TrainObject(
log=log, dataset=dataset, learning_rule=learning_rule, learning_method=learn_method, model=model
)
train_obj.run()
log.info("Fine Tuning")
for layer in train_obj.model.layers:
layer.dropout_below = None
layer.noise = None
train_obj.setup()
train_obj.run()
def run(self):
dataset = self.build_dataset()
learning_rule = self.build_learning_rule()
learn_method = self.build_learning_method()
model = self.build_model()
if self.state.fine_tuning_only:
for layer in model.layers:
layer.dropout_below = None
layer.noise = None
print "Fine Tuning Only"
if self.state.log.save_to_database_name:
database = self.build_database(dataset, learning_rule, learn_method, model)
database['records']['model'] = self.state.hidden1.model
log = self.build_log(database)
train_obj = TrainObject(log = log,
dataset = dataset,
learning_rule = learning_rule,
learning_method = learn_method,
model = model)
train_obj.run()
if not self.state.fine_tuning_only:
log.info("..Fine Tuning after Noisy Training")
for layer in train_obj.model.layers:
layer.dropout_below = None
layer.noise = None
train_obj.setup()
train_obj.run()
def run(self):
dataset = self.build_dataset()
learning_rule = self.build_learning_rule()
learn_method = self.build_learning_method()
if self.state.num_layers == 1:
model = self.build_one_hid_model_no_transpose(dataset.feature_size())
else:
raise ValueError()
if self.state.log.save_to_database_name:
database = self.build_database(dataset, learning_rule, learn_method, model)
log = self.build_log(database)
train_obj = TrainObject(log = log,
dataset = dataset,
learning_rule = learning_rule,
learning_method = learn_method,
model = model)
train_obj.run()
# fine tuning
log.info("fine tuning")
train_obj.model.layers[0].dropout_below = None
train_obj.setup()
train_obj.run()
def run(self):
dataset = self.build_dataset()
learning_rule = self.build_learning_rule()
model = self.build_model(dataset)
learn_method = self.build_learning_method()
database = self.build_database(dataset, learning_rule, learn_method, model)
log = self.build_log(database)
train_obj = TrainObject(log = log,
dataset = dataset,
learning_rule = learning_rule,
learning_method = learn_method,
model = model)
train_obj.run()
log.info("fine tuning")
for layer in train_obj.model.layers:
layer.dropout_below = None
layer.noise = None
train_obj.setup()
train_obj.run()
def run(self):
dataset = self.build_dataset()
learning_rule = self.build_learning_rule()
model = self.build_model(dataset)
learn_method = self.build_learning_method()
database = self.build_database(dataset, learning_rule, learn_method,
model)
log = self.build_log(database)
train_obj = TrainObject(log=log,
dataset=dataset,
learning_rule=learning_rule,
learning_method=learn_method,
model=model)
train_obj.run()
log.info("fine tuning")
for layer in train_obj.model.layers:
layer.dropout_below = None
layer.noise = None
train_obj.setup()
train_obj.run()
def run(self):
dataset = self.build_dataset()
learning_rule = self.build_learning_rule()
learn_method = self.build_learning_method()
if self.state.num_layers == 1:
model = self.build_one_hid_model_no_transpose(
dataset.feature_size())
elif self.state.num_layers == 2:
model = self.build_two_hid_model_no_transpose(
dataset.feature_size())
elif self.state.num_layers == 3:
model = self.build_three_hid_model_no_transpose(
dataset.feature_size())
else:
raise ValueError()
database = self.build_database(dataset, learning_rule, learn_method,
model)
log = self.build_log(database)
dataset.log = log
train_obj = TrainObject(log=log,
dataset=dataset,
learning_rule=learning_rule,
learning_method=learn_method,
model=model)
train_obj.run()
log.info("Fine Tuning")
for layer in train_obj.model.layers:
layer.dropout_below = None
layer.noise = None
train_obj.setup()
train_obj.run()
