Functional interface for the depthwise separable 2D convolution layer. (deprecated)
tf.layers.separable_conv2d(
inputs, filters, kernel_size, strides=(1, 1), padding='valid',
data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1,
activation=None, use_bias=True, depthwise_initializer=None,
pointwise_initializer=None, bias_initializer=tf.zeros_initializer(),
depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None,
activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None,
bias_constraint=None, trainable=True, name=None, reuse=None
)
This layer performs a depthwise convolution that acts separately on
channels, followed by a pointwise convolution that mixes channels.
If use_bias
is True and a bias initializer is provided,
it adds a bias vector to the output.
It then optionally applies an activation function to produce the final output.
Arguments |
inputs
|
Input tensor.
|
filters
|
Integer, the dimensionality of the output space (i.e. the number
of filters in the convolution).
|
kernel_size
|
A tuple or list of 2 integers specifying the spatial
dimensions of the filters. Can be a single integer to specify the same
value for all spatial dimensions.
|
strides
|
A tuple or list of 2 positive integers specifying the strides
of the convolution. 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).
|
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, height, width, channels) while channels_first corresponds to
inputs with shape (batch, channels, height, width) .
|
dilation_rate
|
An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any dilation_rate value != 1 is
incompatible with specifying any stride value != 1.
|
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 num_filters_in * depth_multiplier .
|
activation
|
Activation function. Set it to None to maintain a
linear activation.
|
use_bias
|
Boolean, whether the layer uses a bias.
|
depthwise_initializer
|
An initializer for the depthwise convolution kernel.
|
pointwise_initializer
|
An initializer for the pointwise convolution kernel.
|
bias_initializer
|
An initializer for the bias vector. If None, the default
initializer will be used.
|
depthwise_regularizer
|
Optional regularizer for the depthwise
convolution kernel.
|
pointwise_regularizer
|
Optional regularizer for the pointwise
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
depthwise kernel after being updated by an Optimizer (e.g. used for
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.
|
pointwise_constraint
|
Optional projection function to be applied to the
pointwise kernel after being updated by an Optimizer .
|
bias_constraint
|
Optional projection function to be applied to the
bias after being updated by an Optimizer .
|
trainable
|
Boolean, if True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable ).
|
name
|
A string, the name of the layer.
|
reuse
|
Boolean, whether to reuse the weights of a previous layer
by the same name.
|
Raises |
ValueError
|
if eager execution is enabled.
|