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

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

See the guide: Math > Reduction

Computes the mean of elements across dimensions of a tensor.

Reduces 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_dims is true, the reduced dimensions are retained with length 1.

If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned.

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 None (the default), reduces all dimensions.
  • keep_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

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