|View source on GitHub|
1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
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.
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_ratevalue != 1.
padding: One of
data_format: A string, one of
channels_first. The ordering of the dimensions in the inputs.
channels_lastcorresponds to inputs with shape
(batch, length, channels)while
channels_firstcorresponds 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_ratevalue != 1 is incompatible with specifying any
stridesvalue != 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
trainable: Boolean, if
Truealso add variables to the graph collection
name: A string, the name of the layer.
__init__( 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.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs )