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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 tutorials:
input_tensor along the dimensions given in
keepdims is true, the rank of the tensor is reduced by 1 for each
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., 2.], [3., 4.]]) tf.reduce_std(x) # 1.1180339887498949 tf.reduce_std(x, 0) # [1., 1.] tf.reduce_std(x, 1) # [0.5, 0.5]
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
keepdims: If true, retains reduced dimensions with length 1.
name: A name scope for the associated operations (optional).
The reduced tensor, of the same dtype as the input_tensor.
Equivalent to np.std
Please note that
np.std has a
dtype parameter that could be used to
specify the output type. By default this is
dtype=float64. On the other
tf.reduce_std has an aggressive type inference from