tf.keras.layers.Resizing

See Stable See Nightly

A preprocessing layer which resizes images.

Inherits From: Layer, Operation

tf.keras.layers.Resizing(
    height,
    width,
    interpolation='bilinear',
    crop_to_aspect_ratio=False,
    pad_to_aspect_ratio=False,
    fill_mode='constant',
    fill_value=0.0,
    antialias=False,
    data_format=None,
    **kwargs
)

Used in the notebooks

Used in the tutorials
Simple audio recognition: Recognizing keywords Data augmentation

This layer resizes an image input to a target height and width. The input should be a 4D (batched) or 3D (unbatched) tensor in "channels_last" format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]).

Note: This layer is safe to use inside a tf.data or grain pipeline (independently of which backend you're using).

Input shape
3D	unbatched) or 4D (batched) tensor with shape (..., height, width, channels), in "channels_last" format, or (..., channels, height, width), in "channels_first" format.

Output shape
3D	unbatched) or 4D (batched) tensor with shape (..., target_height, target_width, channels), or (..., channels, target_height, target_width), in "channels_first" format.

Args
height	Integer, the height of the output shape.
width	Integer, the width of the output shape.
interpolation	String, the interpolation method. Supports "bilinear", "nearest", "bicubic", "lanczos3", "lanczos5". Defaults to "bilinear".
crop_to_aspect_ratio	If True, resize the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be cropped so as to return the largest possible window in the image (of size (height, width)) that matches the target aspect ratio. By default (crop_to_aspect_ratio=False), aspect ratio may not be preserved.
pad_to_aspect_ratio	If True, pad the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be evenly padded on the short side.
fill_mode	When using pad_to_aspect_ratio=True, padded areas are filled according to the given mode. Only "constant" is supported at this time (fill with constant value, equal to fill_value).
fill_value	Float. Padding value to use when pad_to_aspect_ratio=True.
data_format	string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
**kwargs	Base layer keyword arguments, such as name and dtype.

Example:

(x_train, y_train), _ = keras.datasets.cifar10.load_data()
image = x_train[0]
resizer = keras.layers.Resizing(128, 128)
resized_image = resizer(image)
print("original:", image.shape, "resized:", resized_image.shape)

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

convert_weight

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convert_weight(
    weight
)

Convert the weight if it is from the a different backend.

from_config

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@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.

get_random_transformation

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get_random_transformation(
    data, training=True, seed=None
)

transform_bounding_boxes

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transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images

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transform_images(
    images, transformation=None, training=True
)

transform_labels

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transform_labels(
    labels, transformation=None, training=True
)

transform_segmentation_masks

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transform_segmentation_masks(
    segmentation_masks, transformation=None, training=True
)

transform_single_bounding_box

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transform_single_bounding_box(
    bounding_box, transformation, training=True
)

transform_single_image

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transform_single_image(
    image, transformation, training=True
)

transform_single_label

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transform_single_label(
    label, transformation, training=True
)

transform_single_segmentation_mask

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transform_single_segmentation_mask(
    segmentation_mask, transformation, training=True
)