tf.reduce_mean

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

Defined in

tensorflow/python/ops/math_ops.py

.

See the guide:
Math > Reduction

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

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 eachentry in

axis

. If

keepdims

is true, the reduced dimensionsare retained with length 1.

If

axis

has no entries, all dimensions are reduced, and atensor 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.]

Args:

input_tensor

: The tensor to reduce. Should have numeric type.

axis

: The dimensions to reduce. If

None

(the default), reduces all dimensions. Must be in the range

[-rank(input_tensor), rank(input_tensor))

.

keepdims

: If true, retains reduced dimensions with length 1.

name

: A name for the operation (optional).

reduction_indices

: The old (deprecated) name for axis.

keep_dims

: Deprecated alias for

keepdims

.

Returns:

The reduced tensor.

Numpy Compatibility

Equivalent to np.mean

Please note that

np.mean

has a

dtype

parameter that could be used tospecify the output type. By default this is

dtype=float64

. On the otherhand,

tf.reduce_mean

has an aggressive type inference from

input_tensor

,for example:

x = tf.constant([1, 0, 1, 0])
tf.reduce_mean(x)  # 0
y = tf.constant([1., 0., 1., 0.])
tf.reduce_mean(y)  # 0.5

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上次更新日期：一月 27, 2018