Creates a depthwise separable convolution block with batch normalization.
tfm.vision.layers.DepthwiseSeparableConvBlock(
filters: int,
kernel_size: int = 3,
strides: int = 1,
regularize_depthwise=False,
activation: Text = 'relu6',
kernel_initializer: Text = 'VarianceScaling',
kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
dilation_rate: int = 1,
use_sync_bn: bool = False,
norm_momentum: float = 0.99,
norm_epsilon: float = 0.001,
**kwargs
)
Args |
filters
|
An int number of filters for the first two convolutions. Note
that the third and final convolution will use 4 times as many filters.
|
kernel_size
|
An int that specifies the height and width of the 2D
convolution window.
|
strides
|
An int of block stride. If greater than 1, this block will
ultimately downsample the input.
|
regularize_depthwise
|
A bool . If Ture, apply regularization on
depthwise.
|
activation
|
A str name of the activation function.
|
kernel_initializer
|
A str of kernel_initializer for convolutional
layers.
|
kernel_regularizer
|
A tf.keras.regularizers.Regularizer object for
Conv2D. Default to None.
|
dilation_rate
|
An int or tuple/list of 2 int , specifying the dilation
rate to use for dilated convolution. Can be a single integer to specify
the same value for all spatial dimensions.
|
use_sync_bn
|
A bool . If True, use synchronized batch normalization.
|
norm_momentum
|
A float of normalization momentum for the moving average.
|
norm_epsilon
|
A float added to variance to avoid dividing by zero.
|
**kwargs
|
Additional keyword arguments to be passed.
|
Methods
call
View source
call(
inputs, training=None
)
This is where the layer's logic lives.
The call()
method may not create state (except in its first
invocation, wrapping the creation of variables or other resources in
tf.init_scope()
). It is recommended to create state, including
tf.Variable
instances and nested Layer
instances,
in __init__()
, or in the build()
method that is
called automatically before call()
executes for the first time.
Args |
inputs
|
Input tensor, or dict/list/tuple of input tensors.
The first positional inputs argument is subject to special rules:
inputs must be explicitly passed. A layer cannot have zero
arguments, and inputs cannot be provided via the default value
of a keyword argument.
- NumPy array or Python scalar values in
inputs get cast as
tensors.
- Keras mask metadata is only collected from
inputs .
- Layers are built (
build(input_shape) method)
using shape info from inputs only.
input_spec compatibility is only checked against inputs .
- Mixed precision input casting is only applied to
inputs .
If a layer has tensor arguments in *args or **kwargs , their
casting behavior in mixed precision should be handled manually.
- The SavedModel input specification is generated using
inputs
only.
- Integration with various ecosystem packages like TFMOT, TFLite,
TF.js, etc is only supported for
inputs and not for tensors in
positional and keyword arguments.
|
*args
|
Additional positional arguments. May contain tensors, although
this is not recommended, for the reasons above.
|
**kwargs
|
Additional keyword arguments. May contain tensors, although
this is not recommended, for the reasons above.
The following optional keyword arguments are reserved:
training : Boolean scalar tensor of Python boolean indicating
whether the call is meant for training or inference.
mask : Boolean input mask. If the layer's call() method takes a
mask argument, its default value will be set to the mask
generated for inputs by the previous layer (if input did come
from a layer that generated a corresponding mask, i.e. if it came
from a Keras layer with masking support).
|
Returns |
A tensor or list/tuple of tensors.
|