Unit normalization layer

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

- 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)`

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`

).