# tf.nn.top_k(input, k=1, sorted=True, name=None)

### tf.nn.top_k(input, k=1, sorted=True, name=None)

See the guide: Neural Network > Evaluation

Finds values and indices of the k largest entries for the last dimension.

If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].

For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus,

values.shape = indices.shape = input.shape[:-1] + [k]


If two elements are equal, the lower-index element appears first.

#### Args:

• input: 1-D or higher Tensor with last dimension at least k.
• k: 0-D int32 Tensor. Number of top elements to look for along the last dimension (along each row for matrices).
• sorted: If true the resulting k elements will be sorted by the values in descending order.
• name: Optional name for the operation.

#### Returns:

• values: The k largest elements along each last dimensional slice.
• indices: The indices of values within the last dimension of input.

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