Resizing classkeras.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
)
A preprocessing layer which resizes images.
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.
Arguments
"bilinear", "nearest", "bicubic",
"lanczos3", "lanczos5". Defaults to "bilinear".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.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.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).pad_to_aspect_ratio=True."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".name and dtype.