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tf.keras.layers.experimental.preprocessing.RandomWidth

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

Inherits From: PreprocessingLayer, Layer, Module

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.

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).

Methods

adapt

View source

Fits the state of the preprocessing layer to the data being passed.

Arguments
data The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.
reset_state Optional argument specifying whether to clear the state of the layer at the start of the call to adapt, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False.