tf.keras.layers.RandomZoom
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A preprocessing layer which randomly zooms images during training.
Inherits From: Layer, Module
tf.keras.layers.RandomZoom(
height_factor,
width_factor=None,
fill_mode='reflect',
interpolation='bilinear',
seed=None,
fill_value=0.0,
**kwargs
)
This layer will randomly zoom in or out on each axis of an image
independently, filling empty space according to fill_mode.
Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and
of integer or floating point dtype.
By default, the layer will output floats.
For an overview and full list of preprocessing layers, see the preprocessing
guide.
Args |
|---|
height_factor
|
a float represented as fraction of value,
or a tuple of size 2 representing lower and upper bound
for zooming vertically. When represented as a single float,
this value is used for both the upper and
lower bound. A positive value means zooming out,
while a negative value
means zooming in. For instance, height_factor=(0.2, 0.3)
result in an output zoomed out by a random amount
in the range [+20%, +30%].
height_factor=(-0.3, -0.2) result in an output zoomed
in by a random amount in the range [+20%, +30%].
|
width_factor
|
a float represented as fraction of value,
or a tuple of size 2 representing lower and upper bound
for zooming horizontally. When
represented as a single float, this value is used
for both the upper and
lower bound. For instance, width_factor=(0.2, 0.3)
result in an output
zooming out between 20% to 30%.
width_factor=(-0.3, -0.2) result in an
output zooming in between 20% to 30%. None means
i.e., zooming vertical and horizontal directions
by preserving the aspect ratio. Defaults to None.
|
fill_mode
|
Points outside the boundaries of the input are
filled according to the given mode
(one of {"constant", "reflect", "wrap", "nearest"}).
- reflect:
(d c b a | a b c d | d c b a)
The input is extended by reflecting about
the edge of the last pixel.
- constant:
(k k k k | a b c d | k k k k)
The input is extended by filling all values beyond
the edge with the same constant value k = 0.
- wrap:
(a b c d | a b c d | a b c d) The input is extended by
wrapping around to the opposite edge.
- nearest:
(a a a a | a b c d | d d d d)
The input is extended by the nearest pixel.
|
interpolation
|
Interpolation mode. Supported values: "nearest",
"bilinear".
|
seed
|
Integer. Used to create a random seed.
|
fill_value
|
a float represents the value to be filled outside
the boundaries when fill_mode="constant".
|
Example:
input_img = np.random.random((32, 224, 224, 3))
layer = tf.keras.layers.RandomZoom(.5, .2)
out_img = layer(input_img)
out_img.shape
TensorShape([32, 224, 224, 3])
|
|---|
3D
|
unbatched) or 4D (batched) tensor with shape
(..., height, width, channels), in "channels_last" format.
|
Output shape |
|---|
3D
|
unbatched) or 4D (batched) tensor with shape
(..., height, width, channels), in "channels_last" format.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.keras.layers.RandomZoom\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/layers/preprocessing/image_preprocessing.py#L1019-L1209) |\n\nA preprocessing layer which randomly zooms images during training.\n\nInherits From: [`Layer`](../../../tf/keras/layers/Layer), [`Module`](../../../tf/Module)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.layers.experimental.preprocessing.RandomZoom`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomZoom)\n\n\u003cbr /\u003e\n\n tf.keras.layers.RandomZoom(\n height_factor,\n width_factor=None,\n fill_mode='reflect',\n interpolation='bilinear',\n seed=None,\n fill_value=0.0,\n **kwargs\n )\n\nThis layer will randomly zoom in or out on each axis of an image\nindependently, filling empty space according to `fill_mode`.\n\nInput pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and\nof integer or floating point dtype.\nBy default, the layer will output floats.\n\nFor an overview and full list of preprocessing layers, see the preprocessing\n[guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `height_factor` | a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming vertically. When represented as a single float, this value is used for both the upper and lower bound. A positive value means zooming out, while a negative value means zooming in. For instance, `height_factor=(0.2, 0.3)` result in an output zoomed out by a random amount in the range `[+20%, +30%]`. `height_factor=(-0.3, -0.2)` result in an output zoomed in by a random amount in the range `[+20%, +30%]`. |\n| `width_factor` | a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming horizontally. When represented as a single float, this value is used for both the upper and lower bound. For instance, `width_factor=(0.2, 0.3)` result in an output zooming out between 20% to 30%. `width_factor=(-0.3, -0.2)` result in an output zooming in between 20% to 30%. `None` means i.e., zooming vertical and horizontal directions by preserving the aspect ratio. Defaults to `None`. |\n| `fill_mode` | Points outside the boundaries of the input are filled according to the given mode (one of `{\"constant\", \"reflect\", \"wrap\", \"nearest\"}`). \u003cbr /\u003e - *reflect* : `(d c b a | a b c d | d c b a)` The input is extended by reflecting about the edge of the last pixel. - *constant* : `(k k k k | a b c d | k k k k)` The input is extended by filling all values beyond the edge with the same constant value k = 0. - *wrap* : `(a b c d | a b c d | a b c d)` The input is extended by wrapping around to the opposite edge. - *nearest* : `(a a a a | a b c d | d d d d)` The input is extended by the nearest pixel. |\n| `interpolation` | Interpolation mode. Supported values: `\"nearest\"`, `\"bilinear\"`. |\n| `seed` | Integer. Used to create a random seed. |\n| `fill_value` | a float represents the value to be filled outside the boundaries when `fill_mode=\"constant\"`. |\n\n#### Example:\n\n input_img = np.random.random((32, 224, 224, 3))\n layer = tf.keras.layers.RandomZoom(.5, .2)\n out_img = layer(input_img)\n out_img.shape\n TensorShape([32, 224, 224, 3])\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Input shape ----------- ||\n|------|----------------------------------------------------------------------------------------------------------------------|\n| `3D` | `unbatched) or 4D (batched) tensor with shape` \u003cbr /\u003e `(..., height, width, channels)`, in `\"channels_last\"` format. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Output shape ------------ ||\n|------|----------------------------------------------------------------------------------------------------------------------|\n| `3D` | `unbatched) or 4D (batched) tensor with shape` \u003cbr /\u003e `(..., height, width, channels)`, in `\"channels_last\"` format. |\n\n\u003cbr /\u003e"]]
View source on GitHub