tf.compat.v1.reduce_mean

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)>

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.

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>