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TensorFlow学习笔记1.11:tf.reduce_sum

TensorFlow学习笔记1.11:tf.reduce_sum

作者: HBU_DAVID | 来源:发表于2018-04-14 14:30 被阅读510次

    http://devdocs.io/tensorflow~python/tf/reduce_sum

    tf.reduce_sum(
        input_tensor,
        axis=None,
        keepdims=None,
        name=None,
        reduction_indices=None,
        keep_dims=None
    )
    

    Computes the sum of elements across dimensions of a tensor. (deprecated arguments)

    计算一个张量的各个维度的元素之和。

    SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version.
    Instructions for updating: keep_dims is deprecated, use keepdims instead

    Reduces input_tensor along the dimensions given in axis.
    Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis.
    If keepdims is true, the reduced dimensions are retained with length 1.

    If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned.

    For example:

    x = tf.constant([[1, 1, 1], [1, 1, 1]])
    tf.reduce_sum(x)  # 6
    tf.reduce_sum(x, 0)  # [2, 2, 2]
    tf.reduce_sum(x, 1)  # [3, 3]
    tf.reduce_sum(x, 1, keepdims=True)  # [[3], [3]]
    tf.reduce_sum(x, [0, 1])  # 6
    

    tf.reduce_mean(
        input_tensor,
        axis=None,
        keepdims=None,
        name=None,
        reduction_indices=None,
        keep_dims=None
    )
    

    Computes the mean of elements across dimensions of a tensor. (deprecated arguments)

    计算一个张量的维数的平均值。

    SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead

    Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.

    If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned.

    For example:

    x = tf.constant([[1., 1.], [2., 2.]])
    tf.reduce_mean(x)  # 1.5
    tf.reduce_mean(x, 0)  # [1.5, 1.5]
    tf.reduce_mean(x, 1)  # [1.,  2.]
    

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