|View source on GitHub|
Computes the mean of elements across dimensions of a tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.reduce_mean( input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None )
Used in the notebooks
|Used in the tutorials|
input_tensor along the dimensions given in
axis by computing the
mean of elements across the dimensions in
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.
axis is None, all dimensions are reduced, and a tensor with a single
element is returned.
x = tf.constant([[1., 1.], [2., 2.]])
<tf.Tensor: shape=(), dtype=float32, numpy=1.5>
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.5, 1.5], dtype=float32)>
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>
||The tensor to reduce. Should have numeric type.|
The dimensions to reduce. If
||If true, retains r|