This is an automated email from the ASF dual-hosted git repository. terrytangyuan pushed a commit to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push: new 79cb705 Lint enhancements to R demo scripts (#9270) 79cb705 is described below commit 79cb705d3e7e7daa5315e57d437c24af0bb299b0 Author: Yuan (Terry) Tang <terrytangy...@users.noreply.github.com> AuthorDate: Mon Jan 1 20:55:16 2018 -0500 Lint enhancements to R demo scripts (#9270) --- R-package/demo/basic_bench.R | 19 +++++++------- R-package/demo/basic_executor.R | 25 +++++++++--------- R-package/demo/basic_kvstore.R | 12 +++------ R-package/demo/basic_model.R | 56 +++++++++++++++++++---------------------- R-package/demo/basic_ndarray.R | 29 +++++++++------------ R-package/demo/basic_random.R | 2 +- R-package/demo/basic_symbol.R | 14 +++++------ 7 files changed, 71 insertions(+), 86 deletions(-) diff --git a/R-package/demo/basic_bench.R b/R-package/demo/basic_bench.R index 4c4a5d5..121f07d 100644 --- a/R-package/demo/basic_bench.R +++ b/R-package/demo/basic_bench.R @@ -1,19 +1,18 @@ require(mxnet) require(methods) - -shape = c(1, 1) -lr = 0.01 -x = mx.nd.ones(shape) -y = mx.nd.zeros(shape) +shape <- c(1, 1) +lr <- 0.01 +x <- mx.nd.ones(shape) +y <- mx.nd.zeros(shape) print(x) -n = 1000 +n <- 1000 -tic = proc.time() -for (i in 1 : n) { - y = y + x *lr +tic <- proc.time() +for (i in 1:n) { + y <- y + x * lr } -toc = proc.time() - tic +toc <- proc.time() - tic as.array(y) print(toc) diff --git a/R-package/demo/basic_executor.R b/R-package/demo/basic_executor.R index fcb538c..17e8718 100644 --- a/R-package/demo/basic_executor.R +++ b/R-package/demo/basic_executor.R @@ -8,27 +8,26 @@ require(mxnet) # exec = mx.exec.set.arg.arrays(exec, some.array) # exec_old is moved, user get an error when use exec_old -A = mx.symbol.Variable('A') -B = mx.symbol.Variable('B') -C = A + B -a = mx.nd.zeros(c(2), mx.cpu()) -b = mx.nd.array(as.array(c(1, 2)), mx.cpu()) +A <- mx.symbol.Variable('A') +B <- mx.symbol.Variable('B') +C <- A + B +a <- mx.nd.zeros(c(2), mx.cpu()) +b <- mx.nd.array(as.array(c(1, 2)), mx.cpu()) -exec = mxnet:::mx.symbol.bind( - symbol=C, - ctx=mx.cpu(), - arg.arrays = list(A=a, B=b), +exec <- mxnet:::mx.symbol.bind( + symbol = C, + ctx = mx.cpu(), + arg.arrays = list(A = a, B = b), aux.arrays = list(), grad.reqs = list("null", "null")) # calculate outputs mx.exec.forward(exec) -out = as.array(exec$outputs[[1]]) +out <- as.array(exec$outputs[[1]]) print(out) -mx.exec.update.arg.arrays(exec, list(A=b, B=b)) +mx.exec.update.arg.arrays(exec, list(A = b, B = b)) mx.exec.forward(exec) -out = as.array(exec$outputs[[1]]) +out <- as.array(exec$outputs[[1]]) print(out) - diff --git a/R-package/demo/basic_kvstore.R b/R-package/demo/basic_kvstore.R index fd0695e..7e46851 100644 --- a/R-package/demo/basic_kvstore.R +++ b/R-package/demo/basic_kvstore.R @@ -1,18 +1,14 @@ require(mxnet) -kv = mx.kv.create() +kv <- mx.kv.create() -dlist = lapply(1:3, function(i) { - x = as.array(c(i, i+1)) +dlist <- lapply(1:3, function(i) { + x = as.array(c(i, i + 1)) mat = mx.nd.array(x, mx.cpu(i)) - list(x=mat) + list(x = mat) }) kv$init(c(0), dlist[[1]]) kv$push(c(0), dlist, 0) kv$pull(c(0), dlist, 0) print(as.array(dlist[[1]][[1]])) - - - - diff --git a/R-package/demo/basic_model.R b/R-package/demo/basic_model.R index 7e6dda5..022cb33 100644 --- a/R-package/demo/basic_model.R +++ b/R-package/demo/basic_model.R @@ -1,6 +1,6 @@ list.of.packages <- c("R.utils") -new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] -if(length(new.packages)) install.packages(new.packages, repos = "https://cloud.r-project.org/") +new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[, "Package"])] +if( length(new.packages)) install.packages(new.packages, repos = "https://cloud.r-project.org/") setwd(tempdir()) @@ -27,22 +27,22 @@ require(mxnet) # Network configuration batch.size <- 100 data <- mx.symbol.Variable("data") -fc1 <- mx.symbol.FullyConnected(data, name="fc1", num_hidden=128) -act1 <- mx.symbol.Activation(fc1, name="relu1", act_type="relu") +fc1 <- mx.symbol.FullyConnected(data, name = "fc1", num_hidden = 128) +act1 <- mx.symbol.Activation(fc1, name = "relu1", act_type = "relu") fc2 <- mx.symbol.FullyConnected(act1, name = "fc2", num_hidden = 64) -act2 <- mx.symbol.Activation(fc2, name="relu2", act_type="relu") -fc3 <- mx.symbol.FullyConnected(act2, name="fc3", num_hidden=10) +act2 <- mx.symbol.Activation(fc2, name = "relu2", act_type = "relu") +fc3 <- mx.symbol.FullyConnected(act2, name = "fc3", num_hidden = 10) softmax <- mx.symbol.Softmax(fc3, name = "sm") -dtrain = mx.io.MNISTIter( - image="train-images-idx3-ubyte", - label="train-labels-idx1-ubyte", - data.shape=c(784), - batch.size=batch.size, - shuffle=TRUE, - flat=TRUE, - silent=0, - seed=10) +dtrain <- mx.io.MNISTIter( + image = "train-images-idx3-ubyte", + label = "train-labels-idx1-ubyte", + data.shape = c(784), + batch.size = batch.size, + shuffle = TRUE, + flat = TRUE, + silent = 0, + seed = 10) dtest = mx.io.MNISTIter( image="t10k-images-idx3-ubyte", @@ -71,15 +71,15 @@ pred <- predict(model, dtest) label <- mx.io.extract(dtest, "label") dataX <- mx.io.extract(dtest, "data") # Predict with R's array -pred2 <- predict(model, X=dataX) +pred2 <- predict(model, X = dataX) accuracy <- function(label, pred) { ypred = max.col(t(as.array(pred))) return(sum((as.array(label) + 1) == ypred) / length(label)) } -print(paste0("Finish prediction... accuracy=", accuracy(label, pred))) -print(paste0("Finish prediction... accuracy2=", accuracy(label, pred2))) +print(paste0("Finish prediction... accuracy = ", accuracy(label, pred))) +print(paste0("Finish prediction... accuracy2 = ", accuracy(label, pred2))) @@ -87,28 +87,24 @@ print(paste0("Finish prediction... accuracy2=", accuracy(label, pred2))) model <- mx.model.load("chkpt", 1) #continue training with some new arguments -model <- mx.model.FeedForward.create(model$symbol, X=dtrain, eval.data=dtest, - ctx=devices, num.round=5, - learning.rate=0.1, momentum=0.9, - epoch.end.callback=mx.callback.save.checkpoint("reload_chkpt"), - batch.end.callback=mx.callback.log.train.metric(100), - arg.params=model$arg.params, aux.params=model$aux.params) +model <- mx.model.FeedForward.create(model$symbol, X = dtrain, eval.data = dtest, + ctx = devices, num.round = 5, + learning.rate = 0.1, momentum = 0.9, + epoch.end.callback = mx.callback.save.checkpoint("reload_chkpt"), + batch.end.callback = mx.callback.log.train.metric(100), + arg.params = model$arg.params, aux.params = model$aux.params) # do prediction pred <- predict(model, dtest) label <- mx.io.extract(dtest, "label") dataX <- mx.io.extract(dtest, "data") # Predict with R's array -pred2 <- predict(model, X=dataX) +pred2 <- predict(model, X = dataX) accuracy <- function(label, pred) { - ypred = max.col(t(as.array(pred))) + ypred <- max.col(t(as.array(pred))) return(sum((as.array(label) + 1) == ypred) / length(label)) } print(paste0("Finish prediction... accuracy=", accuracy(label, pred))) print(paste0("Finish prediction... accuracy2=", accuracy(label, pred2))) - - - - diff --git a/R-package/demo/basic_ndarray.R b/R-package/demo/basic_ndarray.R index c5ee752..17b3c34 100644 --- a/R-package/demo/basic_ndarray.R +++ b/R-package/demo/basic_ndarray.R @@ -1,26 +1,21 @@ require(mxnet) - -x = 1:3 -mat = mx.nd.array(x) - - -mat = mat + 1.0 -mat = mat + mat -mat = mat - 5 -mat = 10 / mat -mat = 7 * mat -mat = 1 - mat + (2 * mat)/(mat + 0.5) +x <- 1:3 +mat <- mx.nd.array(x) + +mat <- mat + 1.0 +mat <- mat + mat +mat <- mat - 5 +mat <- 10 / mat +mat <- 7 * mat +mat <- 1 - mat + (2 * mat) / (mat + 0.5) as.array(mat) -x = as.array(matrix(1:4, 2, 2)) +x <- as.array(matrix(1:4, 2, 2)) mx.ctx.default(mx.cpu(1)) print(mx.ctx.default()) print(is.mx.context(mx.cpu())) -mat = mx.nd.array(x) -mat = (mat * 3 + 5) / 10 +mat <- mx.nd.array(x) +mat <- (mat * 3 + 5) / 10 as.array(mat) - - - diff --git a/R-package/demo/basic_random.R b/R-package/demo/basic_random.R index 7046ab9..0caa683 100644 --- a/R-package/demo/basic_random.R +++ b/R-package/demo/basic_random.R @@ -5,6 +5,6 @@ mx.set.seed(10) print(mx.runif(c(2,2), -10, 10)) # Test initialization module for neural nets. -uinit = mx.init.uniform(0.1) +uinit <- mx.init.uniform(0.1) print(uinit("fc1_weight", c(2, 2), mx.cpu())) print(uinit("fc1_gamma", c(2, 2), mx.cpu())) diff --git a/R-package/demo/basic_symbol.R b/R-package/demo/basic_symbol.R index f4c1d0c..ec07a0d 100644 --- a/R-package/demo/basic_symbol.R +++ b/R-package/demo/basic_symbol.R @@ -1,13 +1,13 @@ require(mxnet) -data = mx.symbol.Variable('data') -net1 = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=10) -net1 = mx.symbol.FullyConnected(data=net1, name='fc2', num_hidden=100) +data <- mx.symbol.Variable('data') +net1 <- mx.symbol.FullyConnected(data = data, name = 'fc1', num_hidden = 10) +net1 <- mx.symbol.FullyConnected(data = net1, name = 'fc2', num_hidden = 100) all.equal(arguments(net1), c('data', 'fc1_weight', 'fc1_bias', 'fc2_weight', 'fc2_bias')) -net2 = mx.symbol.FullyConnected(name='fc3', num_hidden=10) -net2 = mx.symbol.Activation(data=net2, act_type='relu') -net2 = mx.symbol.FullyConnected(data=net2, name='fc4', num_hidden=20) +net2 <- mx.symbol.FullyConnected(name = 'fc3', num_hidden = 10) +net2 <- mx.symbol.Activation(data = net2, act_type = 'relu') +net2 <- mx.symbol.FullyConnected(data = net2, name = 'fc4', num_hidden = 20) -composed = mx.apply(net2, fc3_data=net1, name='composed') +composed <- mx.apply(net2, fc3_data = net1, name = 'composed') -- To stop receiving notification emails like this one, please contact ['"comm...@mxnet.apache.org" <comm...@mxnet.apache.org>'].