View source on GitHub
|
Randomly vary the height of a batch of images during training.
Inherits From: Layer
tf.keras.layers.experimental.preprocessing.RandomHeight(
factor, interpolation='bilinear', seed=None, name=None, **kwargs
)
Adjusts the height 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 height
varying in the range [original + 20%, original + 30%]. factor=(-0.2,
0.3) results in an output height varying in the range [original - 20%,
original + 30%]. factor=0.2 results in an output height varying in the
range [original - 20%, original + 20%].
|
interpolation
|
String, the interpolation method. Defaults to bilinear.
Supports bilinear, nearest, bicubic, area, lanczos3, lanczos5,
gaussian, mitchellcubic
|
seed
|
Integer. Used to create a random seed. |
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, random_height, width, channels).