def model():
tf.reset_default_graph()
m = Model()
with m.sess.graph.as_default():
ema_op, load, restore = _add_ema(m, 0.99)
with tf.control_dependencies([ema_op]):
train_op = m.opt.minimize(m.loss)
yield m, train_op, load, restore
m.sess.close()
def model():
tf.reset_default_graph()
m = Model()
with m.sess.graph.as_default():
ema_op, load, restore = _add_ema(m, 0.99)
with tf.control_dependencies([ema_op]):
train_op = m.opt.minimize(m.loss)
yield m, train_op, load, restore
m.sess.close()
def __init__(self, model_params, **kwargs):
"""Create a Trainer, and give it the parameters needed to instantiate the model
:param model_params: The model parameters
:param kwargs: See below
:Keyword Arguments:
* *nsteps* (`int`) -- If we should report every n-steps, this should be passed
* *ema_decay* (`float`) -- If we are doing an exponential moving average, what decay to us4e
* *clip* (`int`) -- If we are doing gradient clipping, what value to use
* *optim* (`str`) -- The name of the optimizer we are using
* *lr* (`float`) -- The learning rate we are using
* *mom* (`float`) -- If we are using SGD, what value to use for momentum
* *beta1* (`float`) -- Adam-specific hyper-param, defaults to `0.9`
* *beta2* (`float`) -- Adam-specific hyper-param, defaults to `0.999`
* *epsilon* (`float`) -- Adam-specific hyper-param, defaults to `1e-8
"""
super().__init__()
if type(model_params) is dict:
self.model = create_model_for('classify', **model_params)
else:
self.model = model_params
self.sess = self.model.sess
self.loss = self.model.create_loss()
self.test_loss = self.model.create_test_loss()
self.global_step, train_op = optimizer(
self.loss,
colocate_gradients_with_ops=True,
variables=self.model.trainable_variables,
**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(
self.model, float(decay))
with tf.compat.v1.control_dependencies([ema_op]):
self.train_op = tf.identity(train_op)
else:
self.ema = False
self.train_op = train_op
tables = tf.compat.v1.tables_initializer()
self.model.sess.run(tables)
self.model.sess.run(tf.compat.v1.global_variables_initializer())
self.model.set_saver(tf.compat.v1.train.Saver())
checkpoint = kwargs.get('checkpoint')
if checkpoint is not None:
skip_blocks = kwargs.get('blocks_to_skip', ['OptimizeLoss'])
reload_checkpoint(self.model.sess, checkpoint, skip_blocks)
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
