Randomly flip each image horizontally and vertically
layer_random_flip(object, mode = "horizontal_and_vertical", seed = NULL, ...)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
String indicating which flip mode to use. Can be "horizontal",
"vertical", or "horizontal_and_vertical". Defaults to
"horizontal_and_vertical". "horizontal" is a left-right flip and
"vertical" is a top-bottom flip.
Integer. Used to create a random seed.
standard layer arguments.
This layer will flip the images based on the mode attribute.
During inference time, the output will be identical to input. Call the layer
with training = TRUE to flip the input.
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.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomFlip
https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_flip
Other image augmentation layers:
layer_random_contrast(),
layer_random_crop(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
layer_random_width(),
layer_random_zoom()
Other preprocessing layers:
layer_category_encoding(),
layer_center_crop(),
layer_discretization(),
layer_hashing(),
layer_integer_lookup(),
layer_normalization(),
layer_random_contrast(),
layer_random_crop(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
layer_random_width(),
layer_random_zoom(),
layer_rescaling(),
layer_resizing(),
layer_string_lookup(),
layer_text_vectorization()