Separable convolutions consist in first performing a depthwise spatial
convolution (which acts on each input channel separately) followed by a
pointwise convolution which mixes together the resulting output channels. The
depth_multiplier
argument controls how many output channels are generated
per input channel in the depthwise step. Intuitively, separable convolutions
can be understood as a way to factorize a convolution kernel into two smaller
kernels, or as an extreme version of an Inception block.
layer_separable_conv_2d( object, filters, kernel_size, strides = c(1, 1), padding = "valid", data_format = NULL, dilation_rate = 1, depth_multiplier = 1, activation = NULL, use_bias = TRUE, depthwise_initializer = "glorot_uniform", pointwise_initializer = "glorot_uniform", bias_initializer = "zeros", depthwise_regularizer = NULL, pointwise_regularizer = NULL, bias_regularizer = NULL, activity_regularizer = NULL, depthwise_constraint = NULL, pointwise_constraint = NULL, bias_constraint = NULL, input_shape = NULL, batch_input_shape = NULL, batch_size = NULL, dtype = NULL, name = NULL, trainable = NULL, weights = NULL )
object  What to call the new 

filters  Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). 
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 
data_format  A string, one of 
dilation_rate  an integer or list of 2 integers, specifying the
dilation rate to use for dilated convolution. Can be a single integer to
specify the same value for all spatial dimensions. Currently, specifying
any 
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 
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. 
pointwise_initializer  Initializer for the pointwise kernel matrix. 
bias_initializer  Initializer for the bias vector. 
depthwise_regularizer  Regularizer function applied to the depthwise kernel matrix. 
pointwise_regularizer  Regularizer function applied to the pointwise 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. 
pointwise_constraint  Constraint function applied to the pointwise 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. 
4D tensor with shape: (batch, channels, rows, cols)
if data_format='channels_first' or 4D tensor with shape: (batch, rows, cols, channels)
if data_format='channels_last'.
4D tensor with shape: (batch, filters, new_rows, new_cols)
if data_format='channels_first' or 4D tensor with shape:
(batch, new_rows, new_cols, filters)
if data_format='channels_last'.
rows
and cols
values might have changed due to padding.
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_depthwise_conv_2d()
,
layer_separable_conv_1d()
,
layer_upsampling_1d()
,
layer_upsampling_2d()
,
layer_upsampling_3d()
,
layer_zero_padding_1d()
,
layer_zero_padding_2d()
,
layer_zero_padding_3d()