def __init__(self, model, **kwargs):
super(LanguageModelTrainerTf, self).__init__()
self.model = model
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
self.global_step, self.train_op = optimizer(self.loss, **kwargs)
self.nsteps = kwargs.get('nsteps', 500)
def __init__(self, model, **kwargs):
super(LanguageModelTrainerTf, self).__init__()
self.model = model
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
self.global_step, self.train_op = optimizer(self.loss, **kwargs)
self.nsteps = kwargs.get('nsteps', 500)
def __init__(self, model, **kwargs):
super(TaggerTrainerTf, self).__init__()
self.loss = model.create_loss()
self.model = model
span_type = kwargs.get('span_type', 'iob')
verbose = kwargs.get('verbose', False)
self.evaluator = TaggerEvaluatorTf(model, span_type, verbose)
self.global_step, self.train_op = optimizer(self.loss, **kwargs)
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)
def __init__(self, model, **kwargs):
super(TaggerTrainerTf, self).__init__()
self.loss = model.create_loss()
self.model = model
span_type = kwargs.get('span_type', 'iob')
verbose = kwargs.get('verbose', False)
self.evaluator = TaggerEvaluatorTf(model, span_type, verbose)
self.global_step, self.train_op = optimizer(self.loss, **kwargs)
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)
def __init__(self, model, **kwargs):
super(Seq2SeqTrainerTf, self).__init__()
self.sess = model.sess
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
self.model = model
self.tgt_rlut = kwargs['tgt_rlut']
self.base_dir = kwargs['basedir']
self.global_step, self.train_op = optimizer(self.loss, colocate_gradients_with_ops=True, **kwargs)
self.nsteps = kwargs.get('nsteps', 500)
self.beam = kwargs.get('beam', 10)
def __init__(self, model, **kwargs):
super(LanguageModelTrainerTf, self).__init__()
self.model = model
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
if kwargs.get('eval_mode', False):
# When reloaded a model creating the training op will break things.
self.train_op = tf.no_op()
else:
self.global_step, self.train_op = optimizer(self.loss, **kwargs)
self.nsteps = kwargs.get('nsteps', 500)
def __init__(self, model, **kwargs):
super(Seq2SeqTrainerTf, self).__init__()
self.sess = model.sess
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
self.model = model
self.tgt_rlut = kwargs['tgt_rlut']
self.base_dir = kwargs['basedir']
self.global_step, self.train_op = optimizer(
self.loss, colocate_gradients_with_ops=True, **kwargs)
self.nsteps = kwargs.get('nsteps', 500)
self.beam = kwargs.get('beam', 10)
def __init__(self, model, **kwargs):
super(ClassifyTrainerTf, self).__init__()
self.sess = model.sess
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
self.model = model
self.global_step, train_op = optimizer(self.loss, colocate_gradients_with_ops=True, **kwargs)
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)
decay = kwargs.get('ema_decay', None)
if decay is not None:
self.ema = True
ema_op, self.ema_load, self.ema_restore = _add_ema(model, float(decay))
with tf.control_dependencies([ema_op]):
self.train_op = tf.identity(train_op)
else:
self.ema = False
self.train_op = train_op
def __init__(self, model, **kwargs):
super(ClassifyTrainerTf, self).__init__()
self.sess = model.sess
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
self.model = model
self.global_step, train_op = optimizer(self.loss, colocate_gradients_with_ops=True, **kwargs)
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)
decay = kwargs.get('ema_decay', None)
if decay is not None:
self.ema = True
ema_op, self.ema_load, self.ema_restore = _add_ema(model, float(decay))
with tf.control_dependencies([ema_op]):
self.train_op = tf.identity(train_op)
else:
self.ema = False
self.train_op = train_op
def __init__(self, model, **kwargs):
super(Seq2SeqTrainerTf, self).__init__()
self.sess = model.sess
if kwargs.get('eval_mode', False):
# When reloading a model things like `decoder.preds` used in the loss are not present.
# Trying to create the loss would break things.
self.loss = tf.no_op()
self.test_loss = tf.no_op()
else:
self.loss = model.create_loss()
self.test_loss = model.create_test_loss()
self.model = model
self.tgt_rlut = kwargs['tgt_rlut']
self.base_dir = kwargs['basedir']
if kwargs.get('eval_mode', False):
# When reloaded a model creating the training op will break things.
self.train_op = tf.no_op()
else:
self.global_step, self.train_op = optimizer(
self.loss, colocate_gradients_with_ops=True, **kwargs)
self.nsteps = kwargs.get('nsteps', 500)
self.beam = kwargs.get('beam', 10)
