A preprocessing layer which normalizes continuous features.
layer_normalization(object, axis = -1L, mean = NULL, variance = NULL, ...)
What to call the new
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
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
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
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() will compute the mean and
variance of the data and store them as the layer's weights.
be called before
Other numerical features preprocessing layers:
Other preprocessing layers: