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