本文整理汇总了Python中tensorflow.python.ops.gen_nn_ops._softmax方法的典型用法代码示例。如果您正苦于以下问题：Python gen_nn_ops._softmax方法的具体用法？Python gen_nn_ops._softmax怎么用？Python gen_nn_ops._softmax使用的例子？那么恭喜您, 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.ops.gen_nn_ops的用法示例。
在下文中一共展示了gen_nn_ops._softmax方法的3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: softmax
# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _softmax [as 别名]
def softmax(logits, dim=-1, name=None):
"""Computes softmax activations.
For each batch `i` and class `j` we have
softmax = exp(logits) / reduce_sum(exp(logits), dim)
Args:
logits: A non-empty `Tensor`. Must be one of the following types: `half`,
`float32`, `float64`.
dim: The dimension softmax would be performed on. The default is -1 which
indicates the last dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
Raises:
InvalidArgumentError: if `logits` is empty or `dim` is beyond the last
dimension of `logits`.
"""
return _softmax(logits, gen_nn_ops._softmax, dim, name) 示例2: log_softmax
# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _softmax [as 别名]
def log_softmax(logits, dim=-1, name=None):
"""Computes log softmax activations.
For each batch `i` and class `j` we have
logsoftmax = logits - log(reduce_sum(exp(logits), dim))
Args:
logits: A non-empty `Tensor`. Must be one of the following types: `half`,
`float32`, `float64`.
dim: The dimension softmax would be performed on. The default is -1 which
indicates the last dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
Raises:
InvalidArgumentError: if `logits` is empty or `dim` is beyond the last
dimension of `logits`.
"""
return _softmax(logits, gen_nn_ops._log_softmax, dim, name) 示例3: softmax
# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _softmax [as 别名]
def softmax(logits, dim=-1, name=None):
"""Computes softmax activations.
This function performs the equivalent of
softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), dim)
Args:
logits: A non-empty `Tensor`. Must be one of the following types: `half`,
`float32`, `float64`.
dim: The dimension softmax would be performed on. The default is -1 which
indicates the last dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type and shape as `logits`.
Raises:
InvalidArgumentError: if `logits` is empty or `dim` is beyond the last
dimension of `logits`.
"""
return _softmax(logits, gen_nn_ops._softmax, dim, name) 开发者ID:PacktPublishing，项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代码行数:24，代码来源:nn_ops.py