Depthwise 2D convolution.
Inherits From: Layer
, Module
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`tf.compat.v1.keras.layers.DepthwiseConv2D`
tf.keras.layers.DepthwiseConv2D(
kernel_size,
strides=(1, 1),
padding='valid',
depth_multiplier=1,
data_format=None,
dilation_rate=(1, 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 2D 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 or tuple/list of 2 integers, specifying the height
and width of the 2D convolution window. Can be a single integer to specify
the same value for all spatial dimensions.
|
strides
|
An integer or tuple/list of 2 integers, 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) . 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
|
An integer or tuple/list of 2 integers, specifying the
dilation rate to use for dilated convolution. Currently, specifying any
dilation_rate value != 1 is incompatible with specifying any strides
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 ).
|
|
4D tensor with shape: [batch_size, channels, rows, cols] if
data_format='channels_first'
or 4D tensor with shape: [batch_size, rows, cols, channels] if
data_format='channels_last'.
|
Output shape |
4D tensor with shape: [batch_size, channels * depth_multiplier, new_rows,
new_cols] if data_format='channels_first'
or 4D tensor with shape: [batch_size,
new_rows, new_cols, channels * depth_multiplier] if
data_format='channels_last' . rows and cols values might have changed
due to padding.
|
Returns |
A tensor of rank 4 representing
activation(depthwiseconv2d(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
)