Crop the central portion of the images to target height and width
layer_center_crop(object, height, width, ...)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.
Integer, the height of the output shape.
Integer, the width of the output shape.
standard layer arguments.
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:
(..., target_height, target_width, channels).
If the input height/width is even and the target height/width is odd (or inversely), the input image is left-padded by 1 pixel.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/CenterCrop
https://keras.io/api/layers/preprocessing_layers/image_preprocessing/center_crop
Other image preprocessing layers:
layer_rescaling(),
layer_resizing()
Other preprocessing layers:
layer_category_encoding(),
layer_discretization(),
layer_hashing(),
layer_integer_lookup(),
layer_normalization(),
layer_random_brightness(),
layer_random_contrast(),
layer_random_crop(),
layer_random_flip(),
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()