Depthwise separable 2D convolution.

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)

Arguments

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 dilation_rate value != 1.
padding

one of "valid" or "same" (case-insensitive).
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 filters_in * depth_multiplier.
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, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
activation

Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
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_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size

Fixed batch size for layer
dtype

The data type expected by the input, as a string (float32, float64, int32...)
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.

See also

Other convolutional layers: 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