tf.math.reduce_std

Computes the standard deviation of elements across dimensions of a tensor.

tf.math.reduce_std(
    input_tensor, axis=None, keepdims=False, name=None
)

Used in the notebooks

Used in the guideUsed in the tutorials

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each of 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., 2.], [3., 4.]])
tf.math.reduce_std(x)
<tf.Tensor: shape=(), dtype=float32, numpy=1.118034>
tf.math.reduce_std(x, 0)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>
tf.math.reduce_std(x, 1)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.5, 0.5], dtype=float32)>

input_tensorThe tensor to reduce. Should have real or complex type.
axisThe dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
keepdimsIf true, retains reduced dimensions with length 1.
nameA name scope for the associated operations (optional).

The reduced tensor, of the same dtype as the input_tensor. Note, for complex64 or complex128 input, the returned Tensor will be of type float32 or float64, respectively.

numpy compatibility

Equivalent to np.std

Please note np.std has a dtype parameter that could be used to specify the output type. By default this is dtype=float64. On the other hand, tf.math.reduce_std has aggressive type inference from input_tensor.