tft.scale_to_z_score

Returns a standardized column with mean 0 and variance 1.

tft.scale_to_z_score(
    x: common_types.ConsistentTensorType,
    elementwise: bool = False,
    name: Optional[str] = None,
    output_dtype: Optional[tf.DType] = None
) -> common_types.ConsistentTensorType

Used in the notebooks

Used in the tutorials

Scaling to z-score subtracts out the mean and divides by standard deviation. Note that the standard deviation computed here is based on the biased variance (0 delta degrees of freedom), as computed by analyzers.var.

x A numeric Tensor, SparseTensor, or RaggedTensor.
elementwise If true, scales each element of the tensor independently; otherwise uses the mean and variance of the whole tensor.
name (Optional) A name for this operation.
output_dtype (Optional) If not None, casts the output tensor to this type.

A Tensor, SparseTensor, or RaggedTensor containing the input column scaled to mean 0 and variance 1 (standard deviation 1), given by: (x - mean(x)) / std_dev(x). If x is floating point, the mean will have the same type as x. If x is integral, the output is cast to tf.float32. If the analysis dataset is empty or contains a single distinct value, then the input is returned without scaling.

Note that TFLearn generally permits only tf.int64 and tf.float32, so casting this scaler's output may be necessary.