layer_random_rotation: A preprocessing layer which randomly rotates images during...
In rstudio/keras: R Interface to 'Keras'

layer_random_rotation

R Documentation

A preprocessing layer which randomly rotates images during training.

Description

This layer will apply random rotations to each image, filling empty space according to fill_mode.

By default, random rotations are only applied during training. At inference time, the layer does nothing. If you need to apply random rotations at inference time, pass training = TRUE when calling the layer.

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.

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

Usage

layer_random_rotation(
  object,
  factor,
  fill_mode = "reflect",
  interpolation = "bilinear",
  seed = NULL,
  fill_value = 0,
  data_format = NULL,
  ...
)

Arguments

`object`	Object to compose the layer with. A tensor, array, or sequential model.
`factor`	a float represented as fraction of 2 Pi, or a tuple of size 2 representing lower and upper bound for rotating clockwise and counter-clockwise. A positive values means rotating counter clock-wise, while a negative value means clock-wise. When represented as a single float, this value is used for both the upper and lower bound. For instance, `factor=c(-0.2, 0.3)` results in an output rotation by a random amount in the range `⁠[-20% * 360, 30% * 360]⁠`. `factor=0.2` results in an output rotating by a random amount in the range `⁠[-20% * 360, 20% * 360]⁠`.
`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"`.
`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"`.
`...`	For forward/backward compatability.

Value

The return value depends on the value provided for the first argument. If object is:

a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.
a keras_input(), then the output tensor from calling layer(input) is returned.
NULL or missing, then a Layer instance is returned.

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

Other preprocessing layers:
layer_auto_contrast()
layer_category_encoding()
layer_center_crop()
layer_discretization()
layer_equalization()
layer_feature_space()
layer_hashed_crossing()
layer_hashing()
layer_integer_lookup()
layer_max_num_bounding_boxes()
layer_mel_spectrogram()
layer_mix_up()
layer_normalization()
layer_rand_augment()
layer_random_brightness()
layer_random_color_degeneration()
layer_random_color_jitter()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_grayscale()
layer_random_hue()
layer_random_posterization()
layer_random_saturation()
layer_random_sharpness()
layer_random_shear()
layer_random_translation()
layer_random_zoom()
layer_rescaling()
layer_resizing()
layer_solarization()
layer_stft_spectrogram()
layer_string_lookup()
layer_text_vectorization()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_auto_contrast()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_equalization()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_num_bounding_boxes()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_mix_up()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_rand_augment()
layer_random_brightness()
layer_random_color_degeneration()
layer_random_color_jitter()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_grayscale()
layer_random_hue()
layer_random_posterization()
layer_random_saturation()
layer_random_sharpness()
layer_random_shear()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_solarization()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_stft_spectrogram()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()