Depthwise 1D convolution
layer_depthwise_conv_1d(
object,
kernel_size,
strides = 1L,
padding = "valid",
depth_multiplier = 1L,
data_format = NULL,
dilation_rate = 1L,
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,
...
)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
An integer, specifying the height and width of the 1D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
An integer, specifying the strides of the convolution along the
height and width. 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). "valid" means no
padding. "same" results in padding with zeros evenly to the left/right
or up/down of the input such that output has the same height/width
dimension as the input.
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_size, height, width, channels) while
channels_first corresponds to inputs with shape (batch_size, 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'.
A single integer, specifying the dilation rate to use for
dilated convolution. Currently, specifying any dilation_rate value != 1
is incompatible with specifying any stride value != 1.
Activation function to use. If you don't specify anything, no
activation is applied (see ?activation_relu).
Boolean, whether the layer uses a bias vector.
Initializer for the depthwise kernel matrix (see
initializer_glorot_uniform). If NULL, the default initializer
("glorot_uniform") will be used.
Initializer for the bias vector (see
keras.initializers). If NULL, the default initializer ('zeros') will be
used.
Regularizer function applied to the depthwise kernel
matrix (see regularizer_l1()).
Regularizer function applied to the bias vector (see
regularizer_l1()).
Regularizer function applied to the output of the
layer (its 'activation') (see regularizer_l1()).
Constraint function applied to the depthwise kernel
matrix (see constraint_maxnorm()).
Constraint function applied to the bias vector (see
constraint_maxnorm()).
standard layer arguments.
Depthwise convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution.
It is implemented via the following steps:
Split the input into individual channels.
Convolve each channel with an individual depthwise kernel with
depth_multiplier output channels.
Concatenate the convolved outputs along the channels axis.
Unlike a regular 1D convolution, depthwise convolution does not mix information across different input channels.
The depth_multiplier argument determines how many filter are applied to one
input channel. As such, it controls the amount of output channels that are
generated per input channel in the depthwise step.
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_separable_conv_2d(),
layer_upsampling_1d(),
layer_upsampling_2d(),
layer_upsampling_3d(),
layer_zero_padding_1d(),
layer_zero_padding_2d(),
layer_zero_padding_3d()