# tf.compat.v1.layers.separable_conv1d

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/legacy_tf_layers/convolutional.py#L1183-L1347) |

Functional interface for the depthwise separable 1D convolution layer.

    tf.compat.v1.layers.separable_conv1d(
        inputs,
        filters,
        kernel_size,
        strides=1,
        padding='valid',
        data_format='channels_last',
        dilation_rate=1,
        depth_multiplier=1,
        activation=None,
        use_bias=True,
        depthwise_initializer=None,
        pointwise_initializer=None,
        bias_initializer=tf.compat.v1.zeros_initializer(),
        depthwise_regularizer=None,
        pointwise_regularizer=None,
        bias_regularizer=None,
        activity_regularizer=None,
        depthwise_constraint=None,
        pointwise_constraint=None,
        bias_constraint=None,
        trainable=True,
        name=None,
        reuse=None
    )

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## Migrate to TF2

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> [!CAUTION]
> **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.

This API is a legacy api that is only compatible with eager execution and
[`tf.function`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/function) if you combine it with
[`tf.compat.v1.keras.utils.track_tf1_style_variables`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/keras/utils/track_tf1_style_variables)

Please refer to [tf.layers model mapping section of the migration guide](https://www.tensorflow.org/guide/migrate/model_mapping)
to learn how to use your TensorFlow v1 model in TF2 with Keras.

The corresponding TensorFlow v2 layer is
[`tf.keras.layers.SeparableConv1D`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/SeparableConv1D).

#### Structural Mapping to Native TF2

None of the supported arguments have changed name.

Before:

     y = tf.compat.v1.layers.separable_conv1d(x, filters=3, kernel_size=3)

After:

To migrate code using TF1 functional layers use the [Keras Functional API](https://www.tensorflow.org/guide/keras/functional):

     x = tf.keras.Input((28, 28, 1))
     y = tf.keras.layers.SeparableConv1D(filters=3, kernels_size=3)(x)
     model = tf.keras.Model(x, y)

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## Description

This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If `use_bias` is True and a bias initializer is provided,
it adds a bias vector to the output. It then optionally applies an
activation function to produce the final output.

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| ## Args ||
|---|---|
| `inputs` | Input tensor. |
| `filters` | Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). |
| `kernel_size` | A single integer specifying the spatial dimensions of the filters. |
| `strides` | A single integer specifying the strides of the convolution. Specifying any `stride` value != 1 is incompatible with specifying any `dilation_rate` value != 1. |
| `padding` | One of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. |
| `data_format` | A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, length, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, length)`. |
| `dilation_rate` | A single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1. |
| `depth_multiplier` | The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. |
| `activation` | Activation function. Set it to None to maintain a linear activation. |
| `use_bias` | Boolean, whether the layer uses a bias. |
| `depthwise_initializer` | An initializer for the depthwise convolution kernel. |
| `pointwise_initializer` | An initializer for the pointwise convolution kernel. |
| `bias_initializer` | An initializer for the bias vector. If None, the default initializer will be used. |
| `depthwise_regularizer` | Optional regularizer for the depthwise convolution kernel. |
| `pointwise_regularizer` | Optional regularizer for the pointwise convolution kernel. |
| `bias_regularizer` | Optional regularizer for the bias vector. |
| `activity_regularizer` | Optional regularizer function for the output. |
| `depthwise_constraint` | Optional projection function to be applied to the depthwise kernel after being updated by an `Optimizer` (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. |
| `pointwise_constraint` | Optional projection function to be applied to the pointwise kernel after being updated by an `Optimizer`. |
| `bias_constraint` | Optional projection function to be applied to the bias after being updated by an `Optimizer`. |
| `trainable` | Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see [`tf.Variable`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Variable)). |
| `name` | A string, the name of the layer. |
| `reuse` | Boolean, whether to reuse the weights of a previous layer by the same name. |

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| ## Returns ||
|---|---|
| Output tensor. ||

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| ## Raises ||
|---|---|
| `ValueError` | if eager execution is enabled. |

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