Depthwise 1D convolution.
Inherits From: Layer
, Module
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`tf.compat.v1.keras.layers.DepthwiseConv1D`
tf.keras.layers.DepthwiseConv1D(
kernel_size,
strides=1,
padding='valid',
depth_multiplier=1,
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs
)
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.
Args |
kernel_size
|
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.
|
strides
|
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.
|
padding
|
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.
|
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_size, height,
width, channels) while channels_first corresponds to inputs with
shape (batch_size, channels, height, width) . When unspecified, uses
image_data_format value found in your Keras config file at
~/.keras/keras.json (if exists) else 'channels_last'.
Defaults to 'channels_last'.
|
dilation_rate
|
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
|
Activation function to use. If you don't specify anything, no
activation is applied (see keras.activations ).
|
use_bias
|
Boolean, whether the layer uses a bias vector.
|
depthwise_initializer
|
Initializer for the depthwise kernel matrix (see
keras.initializers ). If None, the default initializer
('glorot_uniform') will be used.
|
bias_initializer
|
Initializer for the bias vector (see
keras.initializers ). If None, the default initializer ('zeros') will
be used.
|
depthwise_regularizer
|
Regularizer function applied to the depthwise
kernel matrix (see keras.regularizers ).
|
bias_regularizer
|
Regularizer function applied to the bias vector (see
keras.regularizers ).
|
activity_regularizer
|
Regularizer function applied to the output of the
layer (its 'activation') (see keras.regularizers ).
|
depthwise_constraint
|
Constraint function applied to the depthwise kernel
matrix (see keras.constraints ).
|
bias_constraint
|
Constraint function applied to the bias vector (see
keras.constraints ).
|
|
3D tensor with shape: [batch_size, channels, input_dim] if
data_format='channels_first'
or 3D tensor with shape: [batch_size, input_dim, channels] if
data_format='channels_last'.
|
Output shape |
3D tensor with shape:
[batch_size, channels * depth_multiplier, new_dims]
if data_format='channels_first'
or 3D tensor with shape: [batch_size,
new_dims, channels * depth_multiplier] if
data_format='channels_last' . new_dims values might have
changed due to padding.
|
Returns |
A tensor of rank 3 representing
activation(depthwiseconv1d(inputs, kernel) + bias) .
|
Raises |
ValueError
|
if padding is "causal".
|
ValueError
|
when both strides > 1 and dilation_rate > 1.
|
Methods
convolution_op
View source
convolution_op(
inputs, kernel
)