Depthwise separable 2D convolution.
Inherits From: Layer
, Module
tf.keras.layers.SeparableConv2D(
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs
)
Separable convolutions consist of first performing
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes the resulting
output channels. The depth_multiplier
argument controls how many
output channels are generated per input channel in the depthwise step.
Intuitively, separable convolutions can be understood as
a way to factorize a convolution kernel into two smaller kernels,
or as an extreme version of an Inception block.
Args |
filters
|
Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
|
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. Current implementation only supports equal
length strides in the row and column 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.
|
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
|
An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
|
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 .
|
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
|
An initializer for the depthwise convolution kernel
(see keras.initializers ). If None, then the default initializer
('glorot_uniform') will be used.
|
pointwise_initializer
|
An initializer for the pointwise convolution kernel
(see keras.initializers ). If None, then the default initializer
('glorot_uniform') will be used.
|
bias_initializer
|
An initializer for the bias vector. If None, the default
initializer ('zeros') will be used (see keras.initializers ).
|
depthwise_regularizer
|
Regularizer function applied to
the depthwise kernel matrix (see keras.regularizers ).
|
pointwise_regularizer
|
Regularizer function applied to
the pointwise 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 ).
|
pointwise_constraint
|
Constraint function applied to
the pointwise 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, filters, new_rows, new_cols) if
data_format='channels_first'
or 4D tensor with shape:
(batch_size, new_rows, new_cols, filters) if
data_format='channels_last'. rows and cols values might have changed
due to padding.
|
Returns |
A tensor of rank 4 representing
activation(separableconv2d(inputs, kernel) + bias) .
|
Raises |
ValueError
|
if padding is "causal".
|
Methods
convolution_op
View source
convolution_op(
inputs, kernel
)