def get(self, name=None):
params = {} if self.params is None else self.params
if self.opt_name == "Adam":
if name is None:
return AdamOptimizer(**params)
else:
return AdamOptimizer(name=name, **params)
elif self.opt_name == "Adadelta":
if name is None:
return AdadeltaOptimizer(**params)
else:
return AdadeltaOptimizer(name=name, **params)
else:
raise NotImplemented()
def get(self, name=None, lr_decay=None, global_step=None):
params = {} if self.params is None else self.params.copy()
with tf.variable_scope('opt'):
lr_tensor = tf.get_variable('lr',
dtype=tf.float32,
initializer=tf.constant(
params['learning_rate']),
trainable=False)
if lr_decay is not None:
params['learning_rate'] = lr_decay(
learning_rate=params['learning_rate'],
global_step=global_step,
name='lr_decay')
self.lr_op = lr_tensor if lr_decay is None else lr_tensor.assign(
params['learning_rate'])
params['learning_rate'] = self.lr_op
if self.opt_name == "Adam":
if name is None:
return AdamOptimizer(**params)
else:
return AdamOptimizer(name=name, **params)
elif self.opt_name == "Adadelta":
if name is None:
return AdadeltaOptimizer(**params)
else:
return AdadeltaOptimizer(name=name, **params)
elif self.opt_name == "RMSprop":
if name is None:
return RMSPropOptimizer(**params)
else:
return RMSPropOptimizer(name=name, **params)
elif self.opt_name == "Momentum":
if name is None:
return MomentumOptimizer(**params)
else:
return MomentumOptimizer(name=name, **params)
else:
raise NotImplemented()