tf.keras.layers.RandomZoom  |  TensorFlow v2.16.1 (original) (raw)

tf.keras.layers.RandomZoom

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A preprocessing layer which randomly zooms images during training.

Inherits From: Layer, Operation

tf.keras.layers.RandomZoom(
    height_factor,
    width_factor=None,
    fill_mode='reflect',
    interpolation='bilinear',
    seed=None,
    fill_value=0.0,
    data_format=None,
    **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials
Working with preprocessing layers Image classification Retraining an Image Classifier

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.

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_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. Available methods are "constant","nearest", "wrap" and "reflect". Defaults to "constant". "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 specified byfill_value. "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. Note that when using torch backend, "reflect" is redirected to"mirror" (c d c b a b c d c b a b) because torch does not support "reflect". Note that torch backend does not support "wrap".
interpolation Interpolation mode. Supported values: "nearest","bilinear".
seed Integer. Used to create a random seed.
fill_value a float that represents the value to be filled outside the boundaries when fill_mode="constant".
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 theimage_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:

input_img = np.random.random((32, 224, 224, 3)) layer = keras.layers.RandomZoom(.5, .2) out_img = layer(input_img)

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

from_config

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@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
)