tf.compat.v1.layers.Conv1D

1D convolution layer (e.g. temporal convolution).

Inherits From: Conv1D, Layer, Layer, Module

tf.compat.v1.layers.Conv1D(
    filters,
    kernel_size,
    strides=1,
    padding='valid',
    data_format='channels_last',
    dilation_rate=1,
    activation=None,
    use_bias=True,
    kernel_initializer=None,
    bias_initializer=tf.compat.v1.zeros_initializer(),
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    trainable=True,
    name=None,
    **kwargs
)

Migrate to TF2

This API is a legacy api that is only compatible with eager execution and tf.function if you combine it with tf.compat.v1.keras.utils.track_tf1_style_variables

Please refer to tf.layers model mapping section of the migration guide to learn how to use your TensorFlow v1 model in TF2 with Keras.

The corresponding TensorFlow v2 layer is tf.keras.layers.Conv1D.

Structural Mapping to Native TF2

None of the supported arguments have changed name.

Before:

 conv = tf.compat.v1.layers.Conv1D(filters=3, kernel_size=3)

After:

 conv = tf.keras.layers.Conv1D(filters=3, kernels_size=3)

Description

This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If use_bias is True (and a bias_initializer is provided), a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.

Args
`filters`	Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
`kernel_size`	An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
`strides`	An integer or tuple/list of a single integer, specifying the stride length 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`	An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1.
`activation`	Activation function. Set it to None to maintain a linear activation.
`use_bias`	Boolean, whether the layer uses a bias.
`kernel_initializer`	An initializer for the convolution kernel.
`bias_initializer`	An initializer for the bias vector. If None, the default initializer will be used.
`kernel_regularizer`	Optional regularizer for the convolution kernel.
`bias_regularizer`	Optional regularizer for the bias vector.
`activity_regularizer`	Optional regularizer function for the output.
`kernel_constraint`	Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement 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.
`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`).
`name`	A string, the name of the layer.

Attributes
`graph`
`scope_name`

Attributes

graph

scope_name

Methods

`convolution_op`

View source

convolution_op(
    inputs, kernel
)