This function is maintained but deprecated. Please use new_layer_class() or
%py_class% to define custom layers.
Layer(
classname,
initialize,
build = NULL,
call = NULL,
compute_output_shape = NULL,
...,
inherit = keras::keras$layers$Layer
)the name of the custom Layer.
a function. This is where you define the arguments used to further
build your layer. For example, a dense layer would take the units argument.
You should always call super()$`__init__()` to initialize the base
inherited layer.
a function that takes input_shape as argument. This is where you will
define your weights. Note that if your layer doesn't define trainable weights then
you need not implement this method.
This is where the layer's logic lives. Unless you want your layer to
support masking, you only have to care about the first argument passed to call
(the input tensor).
a function that takes input_shape as an argument. In
case your layer modifies the shape of its input, you should specify here the
shape transformation logic. This allows Keras to do automatic shape inference.
If you don't modify the shape of the input then you need not implement this
method.
Any other methods and/or attributes can be specified using named arguments. They will be added to the layer class.
the Keras layer to inherit from.
A function that wraps create_layer, similar to keras::layer_dense.
if (FALSE) {
layer_dense2 <- Layer(
"Dense2",
initialize = function(units) {
super()$`__init__`()
self$units <- as.integer(units)
},
build = function(input_shape) {
print(class(input_shape))
self$kernel <- self$add_weight(
name = "kernel",
shape = list(input_shape[[2]], self$units),
initializer = "uniform",
trainable = TRUE
)
},
call = function(x) {
tensorflow::tf$matmul(x, self$kernel)
},
compute_output_shape = function(input_shape) {
list(input_shape[[1]], self$units)
}
)
l <- layer_dense2(units = 10)
l(matrix(runif(10), ncol = 1))
}