tf.reduce_mean与tf.reduce_min-CFANZ编程社区

tf.reduce_mean与tf.reduce_min

tf.reduce_mean

reduce_mean(
    input_tensor,
    axis=None,
    keep_dims=False,
    name=None,
    reduction_indices=None
)

Defined in tensorflow/python/ops/math_ops.py.

See the guide: Math > Reduction

Computes the mean of elements across dimensions of a tensor.

input_tensor along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dimsaxis

For example:

# 'x' is [[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.]

Args:

input_tensor

: The tensor to reduce. Should have numeric type.

axis

: The dimensions to reduce. If

Nonekeep_dims

: If true, retains reduced dimensions with length 1.

name

: A name for the operation (optional).

reduction_indices

: The old (deprecated) name for axis.

Returns:

The reduced tensor.

numpy compatibility

Equivalent to np.mean

不翻译了，这个就是计算向量均值的，加各种参数按各种方式计算均值，不懂再交流

tf.reduce_min

reduce_min(
    input_tensor,
    axis=None,
    keep_dims=False,
    name=None,
    reduction_indices=None
)

Defined in tensorflow/python/ops/math_ops.py.

See the guide: Math > Reduction

Computes the minimum of elements across dimensions of a tensor.

input_tensor along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dimsaxis