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Computes the variance of elements across dimensions of a tensor.
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See Migration guide for more details.
tf.math.reduce_variance( input_tensor, axis=None, keepdims=False, name=None )
input_tensor along the dimensions given in
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.
axis is None, all dimensions are reduced, and a tensor with a single element is returned.
x = tf.constant([[1., 2.], [3., 4.]]) tf.math.reduce_variance(x) <tf.Tensor: shape=(), dtype=float32, numpy=1.25> tf.math.reduce_variance(x, 0) <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], ...)> tf.math.reduce_variance(x, 1) <tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.25, 0.25], ...)>
| ||The tensor to reduce. Should have real or complex type.|
| || The dimensions to reduce. If |
| ||If true, retains reduced dimensions with length 1.|
| ||A name scope for the associated operations (optional).|
| The reduced tensor, of the same dtype as the input_tensor. Note, for |
Equivalent to np.var
np.var 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_variance has aggressive type inference from
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Code samples licensed under the Apache 2.0 License.