A preprocessing layer which normalizes continuous features.
layer_normalization(
object,
axis = -1L,
mean = NULL,
variance = NULL,
invert = FALSE,
...
)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, list of integers, or NULL. The axis or axes that should
have a separate mean and variance for each index in the shape. For
example, if shape is (NULL, 5) and axis=1, the layer will track 5
separate mean and variance values for the last axis. If axis is set to
NULL, the layer will normalize all elements in the input by a scalar
mean and variance. Defaults to -1, where the last axis of the input is
assumed to be a feature dimension and is normalized per index. Note that
in the specific case of batched scalar inputs where the only axis is the
batch axis, the default will normalize each index in the batch
separately. In this case, consider passing axis = NULL.
The mean value(s) to use during normalization. The passed value(s)
will be broadcast to the shape of the kept axes above; if the value(s)
cannot be broadcast, an error will be raised when this layer's build()
method is called.
The variance value(s) to use during normalization. The passed
value(s) will be broadcast to the shape of the kept axes above; if the
value(s) cannot be broadcast, an error will be raised when this layer's
build() method is called.
If TRUE, this layer will apply the inverse transformation
to its inputs: it would turn a normalized input back into its
original form.
standard layer arguments.
This layer will shift and scale inputs into a distribution centered around 0
with standard deviation 1. It accomplishes this by precomputing the mean and
variance of the data, and calling (input - mean) / sqrt(var) at runtime.
The mean and variance values for the layer must be either supplied on
construction or learned via adapt(). adapt() will compute the mean and
variance of the data and store them as the layer's weights. adapt() should
be called before fit(), evaluate(), or predict().
adapt()
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Normalization
https://keras.io/api/layers/preprocessing_layers/numerical/normalization
Other numerical features preprocessing layers:
layer_discretization()
Other preprocessing layers:
layer_category_encoding(),
layer_center_crop(),
layer_discretization(),
layer_hashing(),
layer_integer_lookup(),
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()