tf.keras.layers.experimental.preprocessing.RandomWidth

Randomly vary the width of a batch of images during training.

Inherits From: Layer

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tf.compat.v1.keras.layers.experimental.preprocessing.RandomWidth

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%]. </td> </tr><tr> <td>interpolation</td> <td> String, the interpolation method. Defaults tobilinear. Supportsbilinear,nearest,bicubic,area,lanczos3,lanczos5,gaussian,mitchellcubic</td> </tr><tr> <td>seed</td> <td> Integer. Used to create a random seed. </td> </tr><tr> <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).