Depthwise Separable convolutions consists in performing just the first step
in a depthwise spatial convolution (which acts on each input channel
separately). The depth_multiplier
argument controls how many output
channels are generated per input channel in the depthwise step.
layer_depthwise_conv_2d( object, kernel_size, strides = c(1, 1), padding = "valid", depth_multiplier = 1, data_format = NULL, 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, input_shape = NULL, batch_input_shape = NULL, batch_size = NULL, dtype = NULL, name = NULL, trainable = NULL, weights = NULL )
object  Model or layer object 

kernel_size  An integer or list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. 
strides  An integer or list of 2 integers, specifying the strides of
the convolution along the width and height. 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 
activation  Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: 
use_bias  Boolean, whether the layer uses a bias vector. 
depthwise_initializer  Initializer for the depthwise kernel matrix. 
bias_initializer  Initializer for the bias vector. 
depthwise_regularizer  Regularizer function applied to the depthwise kernel matrix. 
bias_regularizer  Regularizer function applied to the bias vector. 
activity_regularizer  Regularizer function applied to the output of the layer (its "activation").. 
depthwise_constraint  Constraint function applied to the depthwise kernel matrix. 
bias_constraint  Constraint function applied to the bias vector. 
input_shape  Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model. 
batch_input_shape  Shapes, including the batch size. For instance,

batch_size  Fixed batch size for layer 
dtype  The data type expected by the input, as a string ( 
name  An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. 
trainable  Whether the layer weights will be updated during training. 
weights  Initial weights for layer. 
Other convolutional layers:
layer_conv_1d_transpose()
,
layer_conv_1d()
,
layer_conv_2d_transpose()
,
layer_conv_2d()
,
layer_conv_3d_transpose()
,
layer_conv_3d()
,
layer_conv_lstm_2d()
,
layer_cropping_1d()
,
layer_cropping_2d()
,
layer_cropping_3d()
,
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()