Unit normalization layer
layer_unit_normalization(object, axis = -1L, ...)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 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).