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# tf.nn.top_k

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

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

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`.

© 2018 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/nn/top_k