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.
input_tensor along the dimensions given in
keep_dims is true, the rank of the tensor is reduced by 1 for each
keep_dims is true, the reduced dimensions
are retained with length 1.
axis has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
# '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.]
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.
The reduced tensor.
Equivalent to np.mean