tf.keras.layers.SpatialDropout2D
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Spatial 2D version of Dropout.
Inherits From: Dropout
, Layer
, Operation
tf.keras.layers.SpatialDropout2D(
rate, data_format=None, seed=None, name=None, dtype=None
)
This version performs the same function as Dropout, however, it drops
entire 2D feature maps instead of individual elements. If adjacent pixels
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, SpatialDropout2D
will help promote independence
between feature maps and should be used instead.
Args |
rate
|
Float between 0 and 1. Fraction of the input units to drop.
|
data_format
|
"channels_first" or "channels_last" .
In "channels_first" mode, the channels dimension (the depth)
is at index 1, in "channels_last" mode is it at index 3.
It defaults to the image_data_format value found in your
Keras config file at ~/.keras/keras.json .
If you never set it, then it will be "channels_last" .
|
Call arguments |
inputs
|
A 4D tensor.
|
training
|
Python boolean indicating whether the layer
should behave in training mode (applying dropout)
or in inference mode (pass-through).
|
|
4D tensor with shape: (samples, channels, rows, cols) if
data_format='channels_first'
or 4D tensor with shape: (samples, rows, cols, channels) if
data_format='channels_last'.
|
Output shape: Same as input.
Reference:
Attributes |
input
|
Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
output
|
Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
Methods
from_config
View source
@classmethod
from_config(
config
)
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args |
config
|
A Python dictionary, typically the
output of get_config.
|
Returns |
A layer instance.
|
symbolic_call
View source
symbolic_call(
*args, **kwargs
)
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.layers.SpatialDropout2D\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/layers/regularization/spatial_dropout.py#L71-L130) |\n\nSpatial 2D version of Dropout.\n\nInherits From: [`Dropout`](../../../tf/keras/layers/Dropout), [`Layer`](../../../tf/keras/Layer), [`Operation`](../../../tf/keras/Operation) \n\n tf.keras.layers.SpatialDropout2D(\n rate, data_format=None, seed=None, name=None, dtype=None\n )\n\nThis version performs the same function as Dropout, however, it drops\nentire 2D feature maps instead of individual elements. If adjacent pixels\nwithin feature maps are strongly correlated (as is normally the case in\nearly convolution layers) then regular dropout will not regularize the\nactivations and will otherwise just result in an effective learning rate\ndecrease. In this case, `SpatialDropout2D` will help promote independence\nbetween feature maps and should be used instead.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `rate` | Float between 0 and 1. Fraction of the input units to drop. |\n| `data_format` | `\"channels_first\"` or `\"channels_last\"`. In `\"channels_first\"` mode, the channels dimension (the depth) is at index 1, in `\"channels_last\"` mode is it at index 3. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `\"channels_last\"`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Call arguments -------------- ||\n|------------|------------------------------------------------------------------------------------------------------------------------------------|\n| `inputs` | A 4D tensor. |\n| `training` | Python boolean indicating whether the layer should behave in training mode (applying dropout) or in inference mode (pass-through). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Input shape ----------- ||\n|---|---|\n| 4D tensor with shape: `(samples, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, rows, cols, channels)` if data_format='channels_last'. ||\n\n\u003cbr /\u003e\n\nOutput shape: Same as input.\n\n#### Reference:\n\n- [Tompson et al., 2014](https://arxiv.org/abs/1411.4280)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|----------|------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | Retrieves the input tensor(s) of a symbolic operation. \u003cbr /\u003e Only returns the tensor(s) corresponding to the *first time* the operation was called. |\n| `output` | Retrieves the output tensor(s) of a layer. \u003cbr /\u003e Only returns the tensor(s) corresponding to the *first time* the operation was called. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `from_config`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/ops/operation.py#L191-L213) \n\n @classmethod\n from_config(\n config\n )\n\nCreates a layer from its config.\n\nThis method is the reverse of `get_config`,\ncapable of instantiating the same layer from the config\ndictionary. It does not handle layer connectivity\n(handled by Network), nor weights (handled by `set_weights`).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|----------|----------------------------------------------------------|\n| `config` | A Python dictionary, typically the output of get_config. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A layer instance. ||\n\n\u003cbr /\u003e\n\n### `symbolic_call`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/ops/operation.py#L58-L70) \n\n symbolic_call(\n *args, **kwargs\n )"]]