tf.keras.layers.Resizing

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,
    data_format=None,
    **kwargs
)

Used in the notebooks

Used in the tutorials

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]).

3D unbatched) or 4D (batched) tensor with shape

(..., height, width, channels), in "channels_last" format, or (..., channels, height, width), in "channels_first" format.

3D unbatched) or 4D (batched) tensor with shape

(..., target_height, target_width, channels), or (..., channels, target_height, target_width), in "channels_first" format.

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.

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

from_config

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

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

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)