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Python nn.sigmoid方法代码示例

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


在下文中一共展示了nn.sigmoid方法的9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: _kl_bernoulli_bernoulli

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def _kl_bernoulli_bernoulli(a, b, name=None):
  """Calculate the batched KL divergence KL(a || b) with a and b Bernoulli.

  Args:
    a: instance of a Bernoulli distribution object.
    b: instance of a Bernoulli distribution object.
    name: (optional) Name to use for created operations.
      default is "kl_bernoulli_bernoulli".

  Returns:
    Batchwise KL(a || b)
  """
  with ops.name_scope(name, "kl_bernoulli_bernoulli",
                      values=[a.logits, b.logits]):
    delta_probs0 = nn.softplus(-b.logits) - nn.softplus(-a.logits)
    delta_probs1 = nn.softplus(b.logits) - nn.softplus(a.logits)
    return (math_ops.sigmoid(a.logits) * delta_probs0
            + math_ops.sigmoid(-a.logits) * delta_probs1) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:20,代码来源:bernoulli.py

示例2: __init__

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def __init__(self,
               p=None,
               dtype=dtypes.int32,
               validate_args=False,
               allow_nan_stats=True,
               name="BernoulliWithSigmoidP"):
    parameters = locals()
    parameters.pop("self")
    with ops.name_scope(name) as ns:
      super(BernoulliWithSigmoidP, self).__init__(
          p=nn.sigmoid(p, name="sigmoid_p"),
          dtype=dtype,
          validate_args=validate_args,
          allow_nan_stats=allow_nan_stats,
          name=ns)
    self._parameters = parameters 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:18,代码来源:bernoulli.py

示例3: _kl_bernoulli_bernoulli

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def _kl_bernoulli_bernoulli(a, b, name=None):
  """Calculate the batched KL divergence KL(a || b) with a and b Bernoulli.

  Args:
    a: instance of a Bernoulli distribution object.
    b: instance of a Bernoulli distribution object.
    name: (optional) Name to use for created operations.
      default is "kl_bernoulli_bernoulli".

  Returns:
    Batchwise KL(a || b)
  """
  with ops.name_scope(name, "kl_bernoulli_bernoulli", [a.logits, b.logits]):
    return (math_ops.sigmoid(a.logits) * (-nn.softplus(-a.logits) +
                                          nn.softplus(-b.logits)) +
            math_ops.sigmoid(-a.logits) * (-nn.softplus(a.logits) +
                                           nn.softplus(b.logits))) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:19,代码来源:bernoulli.py

示例4: __init__

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def __init__(self,
               p=None,
               dtype=dtypes.int32,
               validate_args=False,
               allow_nan_stats=True,
               name="BernoulliWithSigmoidP"):
    parameters = locals()
    parameters.pop("self")
    with ops.name_scope(name) as ns:
      super(BernoulliWithSigmoidP, self).__init__(
          p=nn.sigmoid(p),
          dtype=dtype,
          validate_args=validate_args,
          allow_nan_stats=allow_nan_stats,
          name=ns)
    self._parameters = parameters 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:18,代码来源:bernoulli.py

示例5: hard_sigmoid

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def hard_sigmoid(x):
  """Segment-wise linear approximation of sigmoid.

  Faster than sigmoid.
  Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.
  In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.

  Arguments:
      x: A tensor or variable.

  Returns:
      A tensor.
  """
  x = (0.2 * x) + 0.5
  zero = _to_tensor(0., x.dtype.base_dtype)
  one = _to_tensor(1., x.dtype.base_dtype)
  x = clip_ops.clip_by_value(x, zero, one)
  return x 
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:20,代码来源:backend.py

示例6: focal_loss

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def focal_loss(labels, logits, gamma = 2.0, alpha = 0.75, normalize = True):
    labels = tf.where(labels > 0, tf.ones_like(labels), tf.zeros_like(labels))
    labels = tf.cast(labels, tf.float32)
    probs = tf.sigmoid(logits)
    CE = tf.nn.sigmoid_cross_entropy_with_logits(labels = labels, logits = logits)

    alpha_t = tf.ones_like(logits) * alpha
    alpha_t = tf.where(labels > 0, alpha_t, 1.0 - alpha_t)
    probs_t = tf.where(labels > 0, probs, 1.0 - probs)

    focal_matrix = alpha_t * tf.pow((1.0 - probs_t), gamma)
    fl = focal_matrix * CE

    fl = tf.reduce_sum(fl)
    if normalize:
        #n_pos = tf.reduce_sum(labels)
        #fl = fl / tf.cast(n_pos, tf.float32)
        total_weights = tf.stop_gradient(tf.reduce_sum(focal_matrix))
        fl = fl / total_weights
    return fl 
开发者ID:HLIG,项目名称:HUAWEIOCR-2019,代码行数:22,代码来源:tf.py

示例7: _entropy

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def _entropy(self):
    return (-self.logits * (math_ops.sigmoid(self.logits) - 1) +
            nn.softplus(-self.logits)) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:5,代码来源:bernoulli.py

示例8: __init__

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def __init__(self,
               logits=None,
               dtype=dtypes.int32,
               validate_args=False,
               allow_nan_stats=True,
               name="BernoulliWithSigmoidProbs"):
    parameters = locals()
    with ops.name_scope(name):
      super(BernoulliWithSigmoidProbs, self).__init__(
          probs=nn.sigmoid(logits, name="sigmoid_probs"),
          dtype=dtype,
          validate_args=validate_args,
          allow_nan_stats=allow_nan_stats,
          name=name)
    self._parameters = parameters 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:17,代码来源:bernoulli.py

示例9: sigmoid

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import sigmoid [as 别名]
def sigmoid(x):
  """Element-wise sigmoid.

  Arguments:
      x: A tensor or variable.

  Returns:
      A tensor.
  """
  return nn.sigmoid(x) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:12,代码来源:backend.py


注:本文中的tensorflow.python.ops.nn.sigmoid方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。