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