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Python layers.layer_norm方法代码示例

本文整理汇总了Python中tensorflow.contrib.layers.python.layers.layers.layer_norm方法的典型用法代码示例。如果您正苦于以下问题:Python layers.layer_norm方法的具体用法?Python layers.layer_norm怎么用?Python layers.layer_norm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tensorflow.contrib.layers.python.layers.layers的用法示例。


在下文中一共展示了layers.layer_norm方法的6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.contrib.layers.python.layers import layers [as 别名]
# 或者: from tensorflow.contrib.layers.python.layers.layers import layer_norm [as 别名]
def __init__(self, num_units, forget_bias=1.0,
               input_size=None, activation=math_ops.tanh,
               layer_norm=True, norm_gain=1.0, norm_shift=0.0,
               dropout_keep_prob=1.0, dropout_prob_seed=None,
               reuse=None):
    """Initializes the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      activation: Activation function of the inner states.
      layer_norm: If `True`, layer normalization will be applied.
      norm_gain: float, The layer normalization gain initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      norm_shift: float, The layer normalization shift initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
        recurrent dropout probability value. If float and 1.0, no dropout will
        be applied.
      dropout_prob_seed: (optional) integer, the randomness seed.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
    """
    super(LayerNormBasicLSTMCell, self).__init__(_reuse=reuse)

    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)

    self._num_units = num_units
    self._activation = activation
    self._forget_bias = forget_bias
    self._keep_prob = dropout_keep_prob
    self._seed = dropout_prob_seed
    self._layer_norm = layer_norm
    self._g = norm_gain
    self._b = norm_shift
    self._reuse = reuse 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:41,代码来源:rnn_cell.py

示例2: _norm

# 需要导入模块: from tensorflow.contrib.layers.python.layers import layers [as 别名]
# 或者: from tensorflow.contrib.layers.python.layers.layers import layer_norm [as 别名]
def _norm(self, inp, scope):
    shape = inp.get_shape()[-1:]
    gamma_init = init_ops.constant_initializer(self._g)
    beta_init = init_ops.constant_initializer(self._b)
    with vs.variable_scope(scope):
      # Initialize beta and gamma for use by layer_norm.
      vs.get_variable("gamma", shape=shape, initializer=gamma_init)
      vs.get_variable("beta", shape=shape, initializer=beta_init)
    normalized = layers.layer_norm(inp, reuse=True, scope=scope)
    return normalized 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:12,代码来源:rnn_cell.py

示例3: __init__

# 需要导入模块: from tensorflow.contrib.layers.python.layers import layers [as 别名]
# 或者: from tensorflow.contrib.layers.python.layers.layers import layer_norm [as 别名]
def __init__(self, num_units, forget_bias=1.0,
               input_size=None, activation=math_ops.tanh,
               layer_norm=True, norm_gain=1.0, norm_shift=0.0,
               dropout_keep_prob=1.0, dropout_prob_seed=None):
    """Initializes the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      activation: Activation function of the inner states.
      layer_norm: If `True`, layer normalization will be applied.
      norm_gain: float, The layer normalization gain initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      norm_shift: float, The layer normalization shift initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
        recurrent dropout probability value. If float and 1.0, no dropout will
        be applied.
      dropout_prob_seed: (optional) integer, the randomness seed.
    """

    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)

    self._num_units = num_units
    self._activation = activation
    self._forget_bias = forget_bias
    self._keep_prob = dropout_keep_prob
    self._seed = dropout_prob_seed
    self._layer_norm = layer_norm
    self._g = norm_gain
    self._b = norm_shift 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:35,代码来源:rnn_cell.py

示例4: _norm

# 需要导入模块: from tensorflow.contrib.layers.python.layers import layers [as 别名]
# 或者: from tensorflow.contrib.layers.python.layers.layers import layer_norm [as 别名]
def _norm(g, b, inp, scope):
  shape = inp.get_shape()[-1:]
  gamma_init = init_ops.constant_initializer(g)
  beta_init = init_ops.constant_initializer(b)
  with vs.variable_scope(scope):
    # Initialize beta and gamma for use by layer_norm.
    vs.get_variable("gamma", shape=shape, initializer=gamma_init)
    vs.get_variable("beta", shape=shape, initializer=beta_init)
  normalized = layers.layer_norm(inp, reuse=True, scope=scope)
  return normalized 
开发者ID:shaohua0116,项目名称:Multiview2Novelview,代码行数:12,代码来源:rnn_cell.py

示例5: __init__

# 需要导入模块: from tensorflow.contrib.layers.python.layers import layers [as 别名]
# 或者: from tensorflow.contrib.layers.python.layers.layers import layer_norm [as 别名]
def __init__(self, num_units, forget_bias=1.0,
               input_size=None, activation=math_ops.tanh,
               layer_norm=True, norm_gain=1.0, norm_shift=0.0,
               dropout_keep_prob=1.0, dropout_prob_seed=None,
               reuse=None):
    """Initializes the basic LSTM cell.
    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      activation: Activation function of the inner states.
      layer_norm: If `True`, layer normalization will be applied.
      norm_gain: float, The layer normalization gain initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      norm_shift: float, The layer normalization shift initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
        recurrent dropout probability value. If float and 1.0, no dropout will
        be applied.
      dropout_prob_seed: (optional) integer, the randomness seed.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
    """
    super(LayerNormBasicLSTMCell, self).__init__(_reuse=reuse)

    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)

    self._num_units = num_units
    self._activation = activation
    self._forget_bias = forget_bias
    self._keep_prob = dropout_keep_prob
    self._seed = dropout_prob_seed
    self._layer_norm = layer_norm
    self._norm_gain = norm_gain
    self._norm_shift = norm_shift
    self._reuse = reuse 
开发者ID:shaohua0116,项目名称:Multiview2Novelview,代码行数:40,代码来源:rnn_cell.py

示例6: _norm

# 需要导入模块: from tensorflow.contrib.layers.python.layers import layers [as 别名]
# 或者: from tensorflow.contrib.layers.python.layers.layers import layer_norm [as 别名]
def _norm(self, inp, scope):
    with vs.variable_scope(scope) as scope:
      shape = inp.get_shape()[-1:]
      gamma_init = init_ops.constant_initializer(self._g)
      beta_init = init_ops.constant_initializer(self._b)
      gamma = vs.get_variable("gamma", shape=shape, initializer=gamma_init)  # pylint: disable=unused-variable
      beta = vs.get_variable("beta", shape=shape, initializer=beta_init)  # pylint: disable=unused-variable
      normalized = layers.layer_norm(inp, reuse=True, scope=scope)
      return normalized 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:11,代码来源:rnn_cell.py


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