tfm.vision.layers.SqueezeExcitation

Creates a squeeze and excitation layer.

tfm.vision.layers.SqueezeExcitation(
    in_filters,
    out_filters,
    se_ratio,
    divisible_by=1,
    use_3d_input=False,
    kernel_initializer='VarianceScaling',
    kernel_regularizer=None,
    bias_regularizer=None,
    activation='relu',
    gating_activation='sigmoid',
    round_down_protect=True,
    **kwargs
)

Args
`in_filters`	An `int` number of filters of the input tensor.
`out_filters`	An `int` number of filters of the output tensor.
`se_ratio`	A `float` or None. If not None, se ratio for the squeeze and excitation layer.
`divisible_by`	An `int` that ensures all inner dimensions are divisible by this number.
`use_3d_input`	A `bool` of whether input is 2D or 3D image.
`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.
`gating_activation`	A `str` name of the activation function for final gating function.
`round_down_protect`	A `bool` of whether round down more than 10% will be allowed.
`**kwargs`	Additional keyword arguments to be passed.

Methods

`call`

View source

call(
    inputs
)

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

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).

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.