Returns the rank of a tensor.

# Aliases:

• tf.compat.v1.rank
• tf.compat.v2.rank

 tf.rank(
    input,
    name=None
)

Returns a 0-D int32 Tensor representing the rank of input.

# For example:

 # shape of tensor 't' is [2, 2, 3]
t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
tf.rank(t)  # 3

Note: The rank of a tensor is not the same as the rank of a matrix. The rank of a tensor is the number of indices required to uniquely select each element of the tensor. Rank is also known as "order", "degree", or "ndims."

# Args:

• input: A Tensor or SparseTensor.
• name: A name for the operation (optional).

# Returns:

A Tensor of type int32.

# Numpy Compatibility

Equivalent to np.ndim