View source: R/layersconvolutional.R
layer_depthwise_conv_1d  R Documentation 
Depthwise 1D convolution
layer_depthwise_conv_1d(
object,
kernel_size,
strides = 1L,
padding = "valid",
depth_multiplier = 1L,
data_format = NULL,
dilation_rate = 1L,
activation = NULL,
use_bias = TRUE,
depthwise_initializer = "glorot_uniform",
bias_initializer = "zeros",
depthwise_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
depthwise_constraint = NULL,
bias_constraint = NULL,
...
)
object 
What to compose the new

kernel_size 
An integer, specifying the height and width of the 1D convolution window. Can be a single integer to specify the same value for all spatial dimensions. 
strides 
An integer, specifying the strides of the convolution along the
height and width. Can be a single integer to specify the same value for
all spatial dimensions. Specifying any stride value != 1 is incompatible
with specifying any 
padding 
one of 
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 
data_format 
A string, one of 
dilation_rate 
A single integer, specifying the dilation rate to use for
dilated convolution. Currently, specifying any 
activation 
Activation function to use. If you don't specify anything, no
activation is applied (see 
use_bias 
Boolean, whether the layer uses a bias vector. 
depthwise_initializer 
Initializer for the depthwise kernel matrix (see

bias_initializer 
Initializer for the bias vector (see

depthwise_regularizer 
Regularizer function applied to the depthwise kernel
matrix (see 
bias_regularizer 
Regularizer function applied to the bias vector (see

activity_regularizer 
Regularizer function applied to the output of the
layer (its 'activation') (see 
depthwise_constraint 
Constraint function applied to the depthwise kernel
matrix (see 
bias_constraint 
Constraint function applied to the bias vector (see

... 
standard layer arguments. 
Depthwise convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution.
It is implemented via the following steps:
Split the input into individual channels.
Convolve each channel with an individual depthwise kernel with
depth_multiplier
output channels.
Concatenate the convolved outputs along the channels axis.
Unlike a regular 1D convolution, depthwise convolution does not mix information across different input channels.
The depth_multiplier
argument determines how many filter are applied to one
input channel. As such, it controls the amount of output channels that are
generated per input channel in the depthwise step.
Other convolutional layers:
layer_conv_1d()
,
layer_conv_1d_transpose()
,
layer_conv_2d()
,
layer_conv_2d_transpose()
,
layer_conv_3d()
,
layer_conv_3d_transpose()
,
layer_conv_lstm_2d()
,
layer_cropping_1d()
,
layer_cropping_2d()
,
layer_cropping_3d()
,
layer_depthwise_conv_2d()
,
layer_separable_conv_1d()
,
layer_separable_conv_2d()
,
layer_upsampling_1d()
,
layer_upsampling_2d()
,
layer_upsampling_3d()
,
layer_zero_padding_1d()
,
layer_zero_padding_2d()
,
layer_zero_padding_3d()
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