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tf.rank

tf.rank

rank(
    input,
    name=None
)

Defined in tensorflow/python/ops/array_ops.py.

See the guide: Tensor Transformations > Shapes and Shaping

Returns the rank of a tensor.

This operation returns an integer representing the rank of input.

For example:

# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
# shape of tensor 't' is [2, 2, 3]
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

© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/rank