# tf.keras.layers.Cropping1D

<br />

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/layers/reshaping/cropping1d.py#L28-L97) |

Cropping layer for 1D input (e.g. temporal sequence).

Inherits From: [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.layers.Cropping1D\`

<br />

    tf.keras.layers.Cropping1D(
        cropping=(1, 1), **kwargs
    )

It crops along the time dimension (axis 1).

#### Examples:

    input_shape = (2, 3, 2)
    x = np.arange(np.prod(input_shape)).reshape(input_shape)
    print(x)
    [[[ 0  1]
      [ 2  3]
      [ 4  5]]
     [[ 6  7]
      [ 8  9]
      [10 11]]]
    y = tf.keras.layers.Cropping1D(cropping=1)(x)
    print(y)
    tf.Tensor(
      [[[2 3]]
       [[8 9]]], shape=(2, 1, 2), dtype=int64)

<br />

<br />

<br />

| ## Args ||
|---|---|
| `cropping` | Int or tuple of int (length 2) How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1). If a single int is provided, the same value will be used for both. |

<br />

<br />

<br />

<br />

| ## Input shape ||
|---|---|
| 3D tensor with shape `(batch_size, axis_to_crop, features)` ||

<br />

<br />

<br />

<br />

| ## Output shape ||
|---|---|
| 3D tensor with shape `(batch_size, cropped_axis, features)` ||

<br />