tf.svd Tutorials and Examples for Beginners

tf.svd() can compute the singular value decompositions of one or more matrices.

svd(
    tensor,
    full_matrices=False,
    compute_uv=True,
    name=None
)

Here is example:

# a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
# v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)

In this page, we write some tutorials and examples on how to use tf.svd() function in AI applications, you can follow our tutorials and examples to learn it.

Solve tf.svd NaN bug with np.linalg.svd- TensorFlow Example

In this tutorial, we write an example to fix NaN bug when using tf.svd() function to compute singular value decomposition in tensorflow. We use np.linalg.svd() function to replace tensorflow original tf.svd() when computing. I tested this method in our train model. It works well.