本文整理汇总了Python中tensorflow.python.training.optimizer.Optimizer方法的典型用法代码示例。如果您正苦于以下问题:Python optimizer.Optimizer方法的具体用法?Python optimizer.Optimizer怎么用?Python optimizer.Optimizer使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.training.optimizer
的用法示例。
在下文中一共展示了optimizer.Optimizer方法的11个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def __init__(self,
optimizer1,
optimizer2,
switch,
use_locking=False,
name='Composite'):
"""Construct a new Composite optimizer.
Args:
optimizer1: A tf.python.training.optimizer.Optimizer object.
optimizer2: A tf.python.training.optimizer.Optimizer object.
switch: A tf.bool Tensor, selecting whether to use the first or the second
optimizer.
use_locking: Bool. If True apply use locks to prevent concurrent updates
to variables.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Composite".
"""
super(CompositeOptimizer, self).__init__(use_locking, name)
self._optimizer1 = optimizer1
self._optimizer2 = optimizer2
self._switch = switch
示例2: __init__
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def __init__(self,
opt,
staleness,
use_locking=False,
name="DropStaleGradient"):
"""Constructs a new DropStaleGradientOptimizer.
Args:
opt: The actual optimizer that will be used to compute and apply the
gradients. Must be one of the Optimizer classes.
staleness: The maximum staleness allowed for the optimizer.
use_locking: If `True` use locks for clip update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "DropStaleGradient".
"""
super(DropStaleGradientOptimizer, self).__init__(use_locking, name)
self._opt = opt
self._staleness = staleness
示例3: __init__
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def __init__(self, opt, average_decay=0.9999, num_updates=None,
sequential_update=True):
"""Construct a new MovingAverageOptimizer.
Args:
opt: A tf.Optimizer that will be used to compute and apply gradients.
average_decay: Float. Decay to use to maintain the moving averages
of trained variables.
See tf.train.ExponentialMovingAverage for details.
num_updates: Optional count of number of updates applied to variables.
See tf.train.ExponentialMovingAverage for details.
sequential_update: Bool. If False, will compute the moving average at the
same time as the model is updated, potentially doing
benign data races.
If True, will update the moving average after gradient
updates.
"""
self._optimizer = opt
self._ema = moving_averages.ExponentialMovingAverage(
average_decay, num_updates=num_updates)
self._variable_map = None
self._sequential_update = sequential_update
示例4: __init__
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def __init__(self,
optimizer1,
optimizer2,
switch,
use_locking=False,
name="Composite"):
"""Construct a new Composite optimizer.
Args:
optimizer1: A tf.python.training.optimizer.Optimizer object.
optimizer2: A tf.python.training.optimizer.Optimizer object.
switch: A tf.bool Tensor, selecting whether to use the first or the second
optimizer.
use_locking: Bool. If True apply use locks to prevent concurrent updates
to variables.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Composite".
"""
super(CompositeOptimizer, self).__init__(use_locking, name)
self._optimizer1 = optimizer1
self._optimizer2 = optimizer2
self._switch = switch
示例5: validate_trainop_names
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def validate_trainop_names(self):
""" Give names to all TrainOp, handle no names and duplicated names """
t_len = len(self.train_ops)
# Rename optimizers without name
for i in range(t_len):
if not self.train_ops[i].name:
self.train_ops[i].name = 'Optimizer'
self.train_ops[i].scope_name = 'Optimizer'
# Handle duplicate names
for i in range(t_len):
dupl = 0
for j in range(i+1, t_len):
if not self.train_ops[i].name:
break
if self.train_ops[i].name == self.train_ops[j].name:
if dupl == 0:
self.train_ops[i].name += '_' + str(dupl)
self.train_ops[i].scope_name = self.train_ops[i].name
dupl += 1
self.train_ops[j].name += '_' + str(dupl)
self.train_ops[j].scope_name = self.train_ops[j].name
示例6: get_slot
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def get_slot(self, *args, **kwargs):
"""Return a slot named "name" created for "var" by the Optimizer.
This simply wraps the get_slot() from the actual optimizer.
Args:
*args: Arguments for get_slot().
**kwargs: Keyword arguments for get_slot().
Returns:
The `Variable` for the slot if it was created, `None` otherwise.
"""
return self._opt.get_slot(*args, **kwargs)
示例7: get_slot_names
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def get_slot_names(self, *args, **kwargs):
"""Return a list of the names of slots created by the `Optimizer`.
This simply wraps the get_slot_names() from the actual optimizer.
Args:
*args: Arguments for get_slot().
**kwargs: Keyword arguments for get_slot().
Returns:
A list of strings.
"""
return self._opt.get_slot_names(*args, **kwargs)
示例8: set_weights
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def set_weights(self, weights):
"""Sets the weights of the optimizer, from Numpy arrays.
Should only be called after computing the gradients
(otherwise the optimizer has no weights).
Arguments:
weights: a list of Numpy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the optimizer (i.e. it should match the
output of `get_weights`).
Raises:
ValueError: in case of incompatible weight shapes.
"""
params = self.weights
weight_value_tuples = []
param_values = K.batch_get_value(params)
for pv, p, w in zip(param_values, params, weights):
if pv.shape != w.shape:
raise ValueError('Optimizer weight shape ' + str(pv.shape) +
' not compatible with '
'provided weight shape ' + str(w.shape))
weight_value_tuples.append((p, w))
K.batch_set_value(weight_value_tuples)
示例9: deserialize
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def deserialize(config, custom_objects=None):
"""Inverse of the `serialize` function.
Arguments:
config: Optimizer configuration dictionary.
custom_objects: Optional dictionary mapping
names (strings) to custom objects
(classes and functions)
to be considered during deserialization.
Returns:
A Keras Optimizer instance.
"""
all_classes = {
'sgd': SGD,
'rmsprop': RMSprop,
'adagrad': Adagrad,
'adadelta': Adadelta,
'adam': Adam,
'adamax': Adamax,
'nadam': Nadam,
'tfoptimizer': TFOptimizer,
}
# Make deserialization case-insensitive for built-in optimizers.
if config['class_name'].lower() in all_classes:
config['class_name'] = config['class_name'].lower()
return deserialize_keras_object(
config,
module_objects=all_classes,
custom_objects=custom_objects,
printable_module_name='optimizer')
示例10: __init__
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def __init__(self,
opt,
vars_to_clip_dims,
max_norm,
use_locking=False,
colocate_clip_ops_with_vars=False,
name="VariableClipping"):
"""Construct a new clip-norm optimizer.
Args:
opt: The actual optimizer that will be used to compute and apply the
gradients. Must be one of the Optimizer classes.
vars_to_clip_dims: A dict with keys as Variables and values as lists
of dimensions along which to compute the L2-norm. See
`tf.clip_by_norm` for more details.
max_norm: The L2-norm to clip to, for all variables specified.
use_locking: If `True` use locks for clip update operations.
colocate_clip_ops_with_vars: If `True`, try colocating the clip norm
ops with the corresponding variable.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "VariableClipping".
"""
super(VariableClippingOptimizer, self).__init__(use_locking, name)
self._opt = opt
# Defensive copy of input dict
self._vars_to_clip_dims = {
var: clip_dims[:] for var, clip_dims in vars_to_clip_dims.items()}
self._max_norm = max_norm
self._colocate_clip_ops_with_vars = colocate_clip_ops_with_vars
示例11: apply_gradients
# 需要导入模块: from tensorflow.python.training import optimizer [as 别名]
# 或者: from tensorflow.python.training.optimizer import Optimizer [as 别名]
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""Wraps the original apply_gradient of the optimizer.
Args:
grads_and_vars: List of (gradient, variable) pairs as returned by
`compute_gradients()`.
global_step: Optional `Variable` to increment by one after the
variables have been updated.
name: Optional name for the returned operation. Default to the
name passed to the `Optimizer` constructor.
Returns:
An `Operation` that applies the specified gradients. If `global_step`
was not None, that operation also increments `global_step`.
"""
pre_op = self._before_apply_gradients(grads_and_vars)
with ops.control_dependencies([pre_op]):
optimizer_update = self._optimizer.apply_gradients(
grads_and_vars, global_step=global_step, name=name)
# We get the default one after calling the super.apply_gradient(), since
# we want to preserve original behavior of the optimizer: don't increment
# anything if no global_step is passed. But we need the global step for
# the mask_update.
global_step = (global_step if global_step is not None
else training_util.get_or_create_global_step())
self._global_step = global_step
with ops.control_dependencies([optimizer_update]):
return self.cond_mask_update_op(global_step, control_flow_ops.no_op)