随机缩放层

[source]

RandomZoom class

tf_keras.layers.RandomZoom(
    height_factor,
    width_factor=None,
    fill_mode="reflect",
    interpolation="bilinear",
    seed=None,
    fill_value=0.0,
    **kwargs
)

A preprocessing layer which randomly zooms images during training.

This layer will randomly zoom in or out on each axis of an image independently, filling empty space according to fill_mode.

Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of integer or floating point dtype. By default, the layer will output floats.

For an overview and full list of preprocessing layers, see the preprocessing guide.

Arguments

• height_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming vertically. When represented as a single float, this value is used for both the upper and lower bound. A positive value means zooming out, while a negative value means zooming in. For instance, height_factor=(0.2, 0.3) result in an output zoomed out by a random amount in the range [+20%, +30%]. height_factor=(-0.3, -0.2) result in an output zoomed in by a random amount in the range [+20%, +30%].
• width_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming horizontally. When represented as a single float, this value is used for both the upper and lower bound. For instance, width_factor=(0.2, 0.3) result in an output zooming out between 20% to 30%. width_factor=(-0.3, -0.2) result in an output zooming in between 20% to 30%. None means i.e., zooming vertical and horizontal directions by preserving the aspect ratio. Defaults to None.
• fill_mode: Points outside the boundaries of the input are filled according to the given mode (one of {"constant", "reflect", "wrap", "nearest"}).
  • reflect: (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel.
  • constant: (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k = 0.
  • wrap: (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge.
  • nearest: (a a a a | a b c d | d d d d) The input is extended by the nearest pixel.
• interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
• seed: Integer. Used to create a random seed.
• fill_value: a float represents the value to be filled outside the boundaries when fill_mode="constant".

Example

>>> input_img = np.random.random((32, 224, 224, 3))
>>> layer = tf.keras.layers.RandomZoom(.5, .2)
>>> out_img = layer(input_img)
>>> out_img.shape
TensorShape([32, 224, 224, 3])

Input shape

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

Output shape

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