Image resizing layer.
Inherits From: PreprocessingLayer
, Layer
, Module
tf.keras.layers.experimental.preprocessing.Resizing( height, width, interpolation='bilinear', name=None, **kwargs )
Resize the batched image input to target height and width. The input should be a 4-D tensor in the format of NHWC.
Arguments | |
---|---|
height | Integer, the height of the output shape. |
width | Integer, the width of the output shape. |
interpolation | String, the interpolation method. Defaults to bilinear . Supports bilinear , nearest , bicubic , area , lanczos3 , lanczos5 , gaussian , mitchellcubic |
name | A string, the name of the layer. |
adapt
adapt( data, reset_state=True )
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. |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/layers/experimental/preprocessing/Resizing