tf.compat.v1.layers.separable_conv1d

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.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
)

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.

Arguments
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).
name	A string, the name of the layer.
reuse	Boolean, whether to reuse the weights of a previous layer by the same name.

Returns
Output tensor.

Raises
ValueError	if eager execution is enabled.