anirudhacharya commented on a change in pull request #14023: [R] Add NAG
optimizer
URL: https://github.com/apache/incubator-mxnet/pull/14023#discussion_r252488806
##########
File path: R-package/tests/testthat/test_optimizer.R
##########
@@ -164,22 +170,82 @@ test_that("adadelta", {
y <- mx.nd.array(c(5, 11, 16))
w1 <- mx.nd.array(array(c(1.1, 1.8), dim = c(2, 1)))
- exec <- mxnet:::mx.symbol.bind(symbol = loss, ctx = mx.cpu(), arg.arrays =
list(data = x,
+ exec <- mxnet:::mx.symbol.bind(symbol = loss, ctx = mx.ctx.default(),
arg.arrays = list(data = x,
fc1_weight = w1, label = y), aux.arrays = NULL, grad.reqs = c("null",
"write",
"null"))
optimizer <- mx.opt.create("adadelta", rho = 0.9, epsilon = 1e-05, wd = 0,
rescale.grad = 1,
clip_gradient = -1)
- updaters <- mx.opt.get.updater(optimizer, exec$ref.arg.arrays, ctx =
mx.cpu())
+ updaters <- mx.opt.get.updater(optimizer, exec$ref.arg.arrays, ctx =
mx.ctx.default())
mx.exec.forward(exec, is.train = T)
mx.exec.backward(exec)
arg.blocks <- updaters(exec$ref.arg.arrays, exec$ref.grad.arrays)
mx.exec.update.arg.arrays(exec, arg.blocks, skip.null = TRUE)
-
+
expect_equal(as.array(arg.blocks[[2]]), array(c(1.11, 1.81), dim = c(2, 1)),
tolerance = 0.1)
})
+
+
+test_that("nag_no_momentum", {
+ data <- mx.symbol.Variable("data")
+ label <- mx.symbol.Variable("label")
+ fc_weight <- mx.symbol.Variable("fc_weight")
+ fc <- mx.symbol.FullyConnected(data = data, weight = fc_weight, no.bias = T,
+ name = "fc1", num_hidden = 1)
+ loss <- mx.symbol.LinearRegressionOutput(data = fc, label = label, name =
"loss")
+
+ x <- mx.nd.array(array(1:6, dim = 2:3))
+ y <- mx.nd.array(c(5, 11, 16))
+ w1 <- mx.nd.array(array(c(1.1, 1.8), dim = c(2, 1)))
+
+ exec <- mxnet:::mx.symbol.bind(symbol = loss, ctx = mx.ctx.default(),
arg.arrays = list(data = x,
+ fc1_weight = w1, label = y), aux.arrays = NULL, grad.reqs = c("null",
"write", "null"))
+
+ optimizer <- mx.opt.create("nag", learning.rate = 1, momentum = 0, wd = 0,
rescale.grad = 1,
+ clip_gradient = -1)
+
+ updaters <- mx.opt.get.updater(optimizer, exec$ref.arg.arrays, ctx =
mx.ctx.default())
+
+ mx.exec.forward(exec, is.train = T)
+ mx.exec.backward(exec)
+
+ arg.blocks <- updaters(exec$ref.arg.arrays, exec$ref.grad.arrays)
+ mx.exec.update.arg.arrays(exec, arg.blocks, skip.null = TRUE)
+
+ expect_equal(as.array(arg.blocks[[2]]), array(c(1.4, 2.6), dim = c(2, 1)),
tolerance = 0.05)
+})
+
+
+test_that("nag_momentum", {
+ data <- mx.symbol.Variable("data")
+ label <- mx.symbol.Variable("label")
+ fc_weight <- mx.symbol.Variable("fc_weight")
+ fc <- mx.symbol.FullyConnected(data = data, weight = fc_weight, no.bias = T,
+ name = "fc1", num_hidden = 1)
+ loss <- mx.symbol.LinearRegressionOutput(data = fc, label = label, name =
"loss")
+
+ x <- mx.nd.array(array(1:6, dim = 2:3))
+ y <- mx.nd.array(c(5, 11, 16))
+ w1 <- mx.nd.array(array(c(1.1, 1.8), dim = c(2, 1)))
+
+ exec <- mxnet:::mx.symbol.bind(symbol = loss, ctx = mx.ctx.default(),
arg.arrays = list(data = x,
+
fc1_weight = w1, label = y), aux.arrays = NULL, grad.reqs =
c("null", "write", "null"))
+
+ optimizer <- mx.opt.create("nag", learning.rate = 1, momentum = 0.1, wd = 0,
rescale.grad = 1,
+ clip_gradient = 5)
+
+ updaters <- mx.opt.get.updater(optimizer, exec$ref.arg.arrays, ctx =
mx.ctx.default())
+
+ mx.exec.forward(exec, is.train = T)
+ mx.exec.backward(exec)
+
+ arg.blocks <- updaters(exec$ref.arg.arrays, exec$ref.grad.arrays)
+ mx.exec.update.arg.arrays(exec, arg.blocks, skip.null = TRUE)
+
+ expect_equal(as.array(arg.blocks[[2]]), array(c(1.45, 2.65), dim = c(2, 1)),
tolerance = 0.1)
Review comment:
by doing a similar operation in python api. and as the test deals with small
arrays, can also do this by hand
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