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Computes the mean of elements across dimensions of a tensor.
tf.compat.v1.reduce_mean(
input_tensor,
axis=None,
keepdims=None,
name=None,
reduction_indices=None,
keep_dims=None
)
Reduces input_tensor
along the dimensions given in axis
by computing the
mean of elements across the dimensions in axis
.
Unless keepdims
is true, the rank of the tensor is reduced by 1 for each
the entries in axis
, which must be unique. If keepdims
is true, the
reduced dimensions are retained with length 1.
If axis
is None, 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)
<tf.Tensor: shape=(), dtype=float32, numpy=1.5>
tf.reduce_mean(x, 0)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.5, 1.5], dtype=float32)>
tf.reduce_mean(x, 1)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>
Returns | |
---|---|
The reduced tensor. |
numpy compatibility
Equivalent to np.mean
Please note that np.mean
has a dtype
parameter that could be used to
specify the output type. By default this is dtype=float64
. On the other
hand, tf.reduce_mean
has an aggressive type inference from input_tensor
,
for example:
x = tf.constant([1, 0, 1, 0])
tf.reduce_mean(x)
<tf.Tensor: shape=(), dtype=int32, numpy=0>
y = tf.constant([1., 0., 1., 0.])
tf.reduce_mean(y)
<tf.Tensor: shape=(), dtype=float32, numpy=0.5>