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|
Randomly vary the width of a batch of images during training.
Inherits From: Layer
tf.keras.layers.experimental.preprocessing.RandomWidth(
factor, interpolation='bilinear', seed=None, name=None, **kwargs
)
Adjusts the width of a batch of images by a random factor. The input should be a 4-D tensor in the "channels_last" image data format.
By default, this layer is inactive during inference.
Arguments | ||
|---|---|---|
factor
|
A positive float (fraction of original height), or a tuple of size 2
representing lower and upper bound for resizing vertically. When
represented as a single float, this value is used for both the upper and
lower bound. For instance, factor=(0.2, 0.3) results in an output with
width changed by a random amount in the range [20%, 30%].
factor=(-0.2, 0.3) results in an output with width changed by a random
amount in the range [-20%, +30%].factor=0.2results in an output with
width changed by a random amount in the range[-20%, +20%].
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<td>interpolation</td>
<td>
String, the interpolation method. Defaults tobilinear.
Supportsbilinear,nearest,bicubic,area,lanczos3,lanczos5,gaussian,mitchellcubic</td>
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<td>seed</td>
<td>
Integer. Used to create a random seed.
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<td>name`
|
A string, the name of the layer. |
Input shape:
4D tensor with shape:
(samples, height, width, channels) (data_format='channels_last').
Output shape:
4D tensor with shape:
(samples, height, random_width, channels).