Creates a 3D bottleneck block.
tfm.vision.layers.BottleneckBlock3D(
filters,
temporal_kernel_size,
temporal_strides,
spatial_strides,
stochastic_depth_drop_rate=0.0,
se_ratio=None,
use_self_gating=False,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
activation='relu',
use_sync_bn=False,
norm_momentum=0.99,
norm_epsilon=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.
|
temporal_kernel_size
|
An int of kernel size for the temporal
convolutional layer.
|
temporal_strides
|
An int of ftemporal stride for the temporal
convolutional layer.
|
spatial_strides
|
An int of spatial stride for the spatial convolutional
layer.
|
stochastic_depth_drop_rate
|
A float or None. If not None, drop rate for
the stochastic depth layer.
|
se_ratio
|
A float or None. Ratio of the Squeeze-and-Excitation layer.
|
use_self_gating
|
A bool of whether to apply self-gating module or not.
|
kernel_initializer
|
A str of kernel_initializer for convolutional
layers.
|
kernel_regularizer
|
A tf.keras.regularizers.Regularizer object for
Conv2D. Default to None.
|
bias_regularizer
|
A tf.keras.regularizers.Regularizer object for Conv2d.
Default to None.
|
activation
|
A str name of the activation function.
|
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.
|