def train(self, ):
print 'Starting training.'
print 'Initializing train dataset.'
self.batch_size = self.state.get('batch_size', 20)
train_set = Dataset([self.data['train']],
batch_size=self.batch_size,
targets=[self.targets['train']])
print 'Initializing valid dataset.'
valid_set = Dataset([self.data['valid']],
batch_size=self.batch_size,
targets=[self.targets['valid']])
self.optimizer = SGD_Optimizer(self.model.params,
[self.model.x, self.model.y],
[self.model.cost, self.model.acc],
momentum=self.state.get(
'momentum', False))
lr = self.state.get('learning_rate', 0.1)
num_epochs = self.state.get('num_epochs', 200)
save = self.state.get('save', False)
mom_rate = self.state.get('mom_rate', None)
self.optimizer.train(train_set,
valid_set,
learning_rate=lr,
num_epochs=num_epochs,
save=save,
mom_rate=mom_rate)
class trainer():
def __init__(self,state):
self.state = state
self.dataset_dir = self.state.get('dataset_dir','')
self.list_dir = os.path.join(self.dataset_dir,'lists')
self.lists = {}
self.lists['train'] = os.path.join(self.list_dir,'train_1_of_1.txt')
self.lists['valid'] = os.path.join(self.list_dir,'valid_1_of_1.txt')
self.lists['test'] = os.path.join(self.list_dir,'test_1_of_1.txt')
self.preprocessor = PreProcessor(self.dataset_dir)
print 'Preparing train/valid/test splits'
self.preprocessor.prepare_fold(self.lists['train'],self.lists['valid'],self.lists['test'])
self.data = self.preprocessor.data
self.targets = self.preprocessor.targets
print 'Building model.'
self.model = MLP(n_inputs=self.state.get('n_inputs',513),n_outputs=self.state.get('n_ouputs',10),
n_hidden=self.state.get('n_hidden',[50]),activation=self.state.get('activation','sigmoid'),
output_layer=self.state.get('sigmoid','sigmoid'),dropout_rates=self.state.get('dropout_rates',None))
def train(self,):
print 'Starting training.'
print 'Initializing train dataset.'
self.batch_size = self.state.get('batch_size',20)
train_set = Dataset([self.data['train']],batch_size=self.batch_size,targets=[self.targets['train']])
print 'Initializing valid dataset.'
valid_set = Dataset([self.data['valid']],batch_size=self.batch_size,targets=[self.targets['valid']])
self.optimizer = SGD_Optimizer(self.model.params,[self.model.x,self.model.y],[self.model.cost,self.model.acc],momentum=self.state.get('momentum',False))
lr = self.state.get('learning_rate',0.1)
num_epochs = self.state.get('num_epochs',200)
save = self.state.get('save',False)
mom_rate = self.state.get('mom_rate',None)
self.optimizer.train(train_set,valid_set,learning_rate=lr,num_epochs=num_epochs,save=save,mom_rate=mom_rate)
class trainer():
def __init__(self, state):
self.state = state
self.dataset_dir = self.state.get('dataset_dir', '')
self.list_dir = os.path.join(self.dataset_dir, 'lists')
self.lists = {}
self.lists['train'] = os.path.join(self.list_dir, 'train_1_of_1.txt')
self.lists['valid'] = os.path.join(self.list_dir, 'valid_1_of_1.txt')
self.lists['test'] = os.path.join(self.list_dir, 'test_1_of_1.txt')
self.preprocessor = PreProcessor(self.dataset_dir)
print 'Preparing train/valid/test splits'
self.preprocessor.prepare_fold(self.lists['train'],
self.lists['valid'], self.lists['test'])
self.data = self.preprocessor.data
self.targets = self.preprocessor.targets
print 'Building model.'
self.model = MLP(n_inputs=self.state.get('n_inputs', 513),
n_outputs=self.state.get('n_ouputs', 10),
n_hidden=self.state.get('n_hidden', [50]),
activation=self.state.get('activation', 'sigmoid'),
output_layer=self.state.get('sigmoid', 'sigmoid'),
dropout_rates=self.state.get('dropout_rates', None))
def train(self, ):
print 'Starting training.'
print 'Initializing train dataset.'
self.batch_size = self.state.get('batch_size', 20)
train_set = Dataset([self.data['train']],
batch_size=self.batch_size,
targets=[self.targets['train']])
print 'Initializing valid dataset.'
valid_set = Dataset([self.data['valid']],
batch_size=self.batch_size,
targets=[self.targets['valid']])
self.optimizer = SGD_Optimizer(self.model.params,
[self.model.x, self.model.y],
[self.model.cost, self.model.acc],
momentum=self.state.get(
'momentum', False))
lr = self.state.get('learning_rate', 0.1)
num_epochs = self.state.get('num_epochs', 200)
save = self.state.get('save', False)
mom_rate = self.state.get('mom_rate', None)
self.optimizer.train(train_set,
valid_set,
learning_rate=lr,
num_epochs=num_epochs,
save=save,
mom_rate=mom_rate)
def train(self,):
print 'Starting training.'
print 'Initializing train dataset.'
self.batch_size = self.state.get('batch_size', 20)
train_set = Dataset(
[self.data['train']],
batch_size=self.batch_size,
targets=[self.targets['train']])
print 'Initializing valid dataset.'
valid_set = Dataset(
[self.data['valid']],
batch_size=self.batch_size,
targets=[self.targets['valid']])
self.optimizer = SGD_Optimizer(
self.model.params,
[self.model.x, self.model.y],
[self.model.cost, self.model.acc],
momentum=self.state.get('momentum', False))
lr = self.state.get('learning_rate', 0.1)
num_epochs = self.state.get('num_epochs', 200)
save = self.state.get('save', False)
mom_rate = self.state.get('mom_rate', None)
plot = self.state.get('plot', False)
output_folder = self.state.get('output_folder', None)
self.optimizer.train(train_set,
valid_set,
self.state.get('update_lr_params'),
lr,
num_epochs,
save,
output_folder,
self.state.get('lr_update'),
mom_rate,
plot,
self.state.get('headless'))
def train(self,):
print 'Starting training.'
print 'Initializing train dataset.'
self.batch_size = self.state.get('batch_size',20)
train_set = Dataset([self.data['train']],batch_size=self.batch_size,targets=[self.targets['train']])
print 'Initializing valid dataset.'
valid_set = Dataset([self.data['valid']],batch_size=self.batch_size,targets=[self.targets['valid']])
self.optimizer = SGD_Optimizer(self.model.params,[self.model.x,self.model.y],[self.model.cost,self.model.acc],momentum=self.state.get('momentum',False))
lr = self.state.get('learning_rate',0.1)
num_epochs = self.state.get('num_epochs',200)
save = self.state.get('save',False)
mom_rate = self.state.get('mom_rate',None)
self.optimizer.train(train_set,valid_set,learning_rate=lr,num_epochs=num_epochs,save=save,mom_rate=mom_rate)