|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/array_ops.py#L940-L992) | Extracts a slice from a tensor. #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.slice`](https://www.tensorflow.org/api_docs/python/tf/slice)
tf.slice( input_, begin, size, name=None ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Introduction to tensor slicing](https://www.tensorflow.org/guide/tensor_slicing) - [TFLite Authoring Tool](https://www.tensorflow.org/lite/guide/authoring) - [TensorFlow Lite Model Analyzer](https://www.tensorflow.org/lite/guide/model_analyzer) | - [Graph regularization for sentiment classification using synthesized graphs](https://www.tensorflow.org/neural_structured_learning/tutorials/graph_keras_lstm_imdb) | See also [`tf.strided_slice`](https://www.tensorflow.org/api_docs/python/tf/strided_slice). This operation extracts a slice of size `size` from a tensor `input_` starting at the location specified by `begin`. The slice `size` is represented as a tensor shape, where `size[i]` is the number of elements of the 'i'th dimension of `input_` that you want to slice. The starting location (`begin`) for the slice is represented as an offset in each dimension of `input_`. In other words, `begin[i]` is the offset into the i'th dimension of `input_` that you want to slice from. Note that [`tf.Tensor.getitem`](https://www.tensorflow.org/api_docs/python/tf/Tensor#__getitem__) is typically a more pythonic way to perform slices, as it allows you to write `foo[3:7, :-2]` instead of `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`. `begin` is zero-based; `size` is one-based. If `size[i]` is -1, all remaining elements in dimension i are included in the slice. In other words, this is equivalent to setting: `size[i] = input_.dim_size(i) - begin[i]` This operation requires that: `0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]` #### For example: t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]) tf.slice(t, [1, 0, 0], [1, 1, 3]) # [[[3, 3, 3]]] tf.slice(t, [1, 0, 0], [1, 2, 3]) # [[[3, 3, 3], # [4, 4, 4]]] tf.slice(t, [1, 0, 0], [2, 1, 3]) # [[[3, 3, 3]], # [[5, 5, 5]]]


| ## Args || |---|---| | `input_` | A `Tensor`. | | `begin` | An `int16`, `int32` or `int64` `Tensor`. | | `size` | An `int16`, `int32` or `int64` `Tensor`. | | `name` | A name for the operation (optional). |



| ## Returns || |---|---| | A `Tensor` the same type as `input_`. ||