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Posted to commits@mxnet.apache.org by te...@apache.org on 2017/12/31 22:59:41 UTC
[incubator-mxnet] branch style created (now dde81f9)
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at dde81f9 Lint enhancements to R demo scripts
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new dde81f9 Lint enhancements to R demo scripts
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[incubator-mxnet] 01/01: Lint enhancements to R demo scripts
Posted by te...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
terrytangyuan pushed a commit to branch style
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit dde81f90f29663b0facfbf2857f0ba55cc89bb3b
Author: terrytangyuan <te...@gmail.com>
AuthorDate: Sun Dec 31 17:59:20 2017 -0500
Lint enhancements to R demo scripts
---
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')
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