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
)Model or layer object
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.
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.
one of "valid" or "same" (case-insensitive).
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.
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 function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: a(x) = x).
Boolean, whether the layer uses a bias vector.
Initializer for the depthwise kernel matrix.
Initializer for the bias vector.
Regularizer function applied to the depthwise kernel matrix.
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the depthwise kernel matrix.
Constraint function applied to the bias vector.
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.
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.
Fixed batch size for layer
The data type expected by the input, as a string (float32,
float64, int32...)
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
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()