tfmri.math.normalize_no_nan

normalize_no_nan(tensor, ord='euclidean', axis=None, name=None)[source]

Normalizes tensor along dimension axis using specified norm.

Parameters

  • tensor – A Tensor of type float32, float64, complex64, complex128.

  • ord –

    Order of the norm. Supported values are 'fro', 'euclidean', 1, 2, np.inf and any positive real number yielding the corresponding p-norm. Default is 'euclidean' which is equivalent to Frobenius norm if tensor is a matrix and equivalent to 2-norm for vectors. Some restrictions apply: a) The Frobenius norm 'fro' is not defined for

    vectors, b) If axis is a 2-tuple (matrix norm), only 'euclidean', ‘fro', 1, 2, np.inf are supported. See the description of axis on how to compute norms for a batch of vectors or matrices stored in a tensor.

  • axis –

    If axis is None (the default), the input is considered a vector and a single vector norm is computed over the entire set of values in the tensor, i.e. norm(tensor, ord=ord) is equivalent to norm(reshape(tensor, [-1]), ord=ord). If axis is a Python integer, the input is considered a batch of vectors, and axis determines the axis in tensor over which to compute vector norms. If axis is a 2-tuple of Python integers it is considered a batch of matrices and axis determines the axes in tensor over which to compute a matrix norm. Negative indices are supported. Example: If you are passing a tensor that

    can be either a matrix or a batch of matrices at runtime, pass axis=[-2,-1] instead of axis=None to make sure that matrix norms are computed.

  • name – The name of the op.

Returns

A normalized Tensor with the same shape as tensor.