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 filters 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.
Args
kernel_size
int or tuple/list of 1 integer, specifying the size of the
depthwise convolution window.
strides
int or tuple/list of 1 integer, specifying the stride length
of the convolution. strides > 1 is incompatible with
dilation_rate > 1.
padding
string, either "valid" or "same" (case-insensitive).
"valid" means no padding. "same" results in padding evenly to
the left/right or up/down of the input. When padding="same" and
strides=1, the output has the same size as the input.
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 input_channel * depth_multiplier.
data_format
string, either "channels_last" or "channels_first".
The ordering of the dimensions in the inputs. "channels_last"
corresponds to inputs with shape (batch, steps, features)
while "channels_first" corresponds to inputs with shape
(batch, features, steps). 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".
dilation_rate
int or tuple/list of 1 integers, specifying the dilation
rate to use for dilated convolution.
activation
Activation function. If None, no activation is applied.
use_bias
bool, if True, bias will be added to the output.
depthwise_initializer
Initializer for the convolution kernel.
If None, the default initializer ("glorot_uniform")
will be used.
bias_initializer
Initializer for the bias vector. If None, the
default initializer ("zeros") will be used.
depthwise_regularizer
Optional regularizer for the convolution kernel.
bias_regularizer
Optional regularizer for the bias vector.
activity_regularizer
Optional regularizer function for the output.
depthwise_constraint
Optional projection function to be applied to the
kernel after being updated by an Optimizer (e.g. used to implement
norm constraints or value constraints for layer weights). The
function must take as input the unprojected variable and must return
the projected variable (which must have the same shape). Constraints
are not safe to use when doing asynchronous distributed training.
bias_constraint
Optional projection function to be applied to the
bias after being updated by an Optimizer.
Input shape:
If data_format="channels_last":
A 3D tensor with shape: (batch_shape, steps, channels)
If data_format="channels_first":
A 3D tensor with shape: (batch_shape, channels, steps)
Output shape:
If data_format="channels_last":
A 3D tensor with shape:
(batch_shape, new_steps, channels * depth_multiplier)
If data_format="channels_first":
A 3D tensor with shape:
(batch_shape, channels * depth_multiplier, new_steps)
Returns
A 3D tensor representing
activation(depthwise_conv1d(inputs, kernel) + bias).
View source on GitHub