layer_unit_normalization {keras}

Unit normalization layer

Description

Unit normalization layer

Usage

layer_unit_normalization(object, axis = -1L, ...)

Arguments

object

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.

axis

Integer or list. The axis or axes to normalize across. Typically this is the features axis or axes. The left-out axes are typically the batch axis or axes. Defaults to -1, the last dimension in the input.

...

standard layer arguments.

data <- as_tensor(1:6, shape = c(2, 3), dtype = "float32")
normalized_data <- data %>% layer_unit_normalization()
for(row in 1:2)
  normalized_data[row, ] %>%
  { sum(.^2) } %>%
  print()
# tf.Tensor(0.9999999, shape=(), dtype=float32)
# tf.Tensor(1.0, shape=(), dtype=float32)

Details

Normalize a batch of inputs so that each input in the batch has a L2 norm equal to 1 (across the axes specified in axis).

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/UnitNormalization