tf.keras.layers.experimental.preprocessing.RandomZoom
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Randomly zoom each image during training.
Inherits From: Layer
tf.keras.layers.experimental.preprocessing.RandomZoom(
height_factor, width_factor=None, fill_mode='reflect', interpolation='bilinear',
seed=None, name=None, **kwargs
)
Arguments |
|---|
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%. Defaults to None, i.e., zooming vertical and horizontal
directions by preserving the aspect ratio.
|
fill_mode
|
Points outside the boundaries of the input are filled according
to the given mode (one of {'constant', 'reflect', 'wrap'}).
- 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)
|
interpolation
|
Interpolation mode. Supported values: "nearest", "bilinear".
|
seed
|
Integer. Used to create a random seed.
|
name
|
A string, the name of the layer.
|
Example:
input_img = np.random.random((32, 224, 224, 3))
layer = tf.keras.layers.experimental.preprocessing.RandomZoom(.5, .2)
out_img = layer(input_img)
out_img.shape
TensorShape([32, 224, 224, 3])
4D tensor with shape:
(samples, height, width, channels), data_format='channels_last'.
Output shape:
4D tensor with shape:
(samples, height, width, channels), data_format='channels_last'.
Raise |
|---|
ValueError
|
if lower bound is not between [0, 1], or upper bound is
negative.
|
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.layers.experimental.preprocessing.RandomZoom\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py#L842-L986) |\n\nRandomly zoom each image during training.\n\nInherits From: [`Layer`](../../../../../tf/keras/layers/Layer)\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.layers.experimental.preprocessing.RandomZoom`](/api_docs/python/tf/keras/layers/experimental/preprocessing/RandomZoom)\n\n\u003cbr /\u003e\n\n tf.keras.layers.experimental.preprocessing.RandomZoom(\n height_factor, width_factor=None, fill_mode='reflect', interpolation='bilinear',\n seed=None, name=None, **kwargs\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\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%. Defaults to `None`, i.e., zooming vertical and horizontal directions by preserving the aspect ratio. |\n| `fill_mode` | Points outside the boundaries of the input are filled according to the given mode (one of `{'constant', 'reflect', 'wrap'}`). \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)` |\n| `interpolation` | Interpolation mode. Supported values: \"nearest\", \"bilinear\". |\n| `seed` | Integer. Used to create a random seed. |\n| `name` | A string, the name of the layer. |\n\n#### Example:\n\n input_img = np.random.random((32, 224, 224, 3))\n layer = tf.keras.layers.experimental.preprocessing.RandomZoom(.5, .2)\n out_img = layer(input_img)\n out_img.shape\n TensorShape([32, 224, 224, 3])\n\n#### Input shape:\n\n4D tensor with shape:\n`(samples, height, width, channels)`, data_format='channels_last'.\n\n#### Output shape:\n\n4D tensor with shape:\n`(samples, height, width, channels)`, data_format='channels_last'.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raise ----- ||\n|--------------|---------------------------------------------------------------------|\n| `ValueError` | if lower bound is not between \\[0, 1\\], or upper bound is negative. |\n\n\u003cbr /\u003e"]]
View source on GitHub