MXNet | LeNet-5（卷积神经网络）用于手写字识别

>setwd("F:\\迅雷下载\\mnist")

>require(mxnet)
>library(readr)
>train <- read_csv('train.csv')
>test <- read_csv('test.csv')

>train <- data.matrix(train)
>test <- data.matrix(test)

>train.x <- train[,-1]
>train.y <- train[,1]

>train.x <- t(train.x/255)
>test <- t(test/255)

>table(train.y)

train.y
   0    1    2    3    4    5    6    7    8    9 
4132 4684 4177 4351 4072 3795 4137 4401 4063 4188 

# input
data <- mx.symbol.Variable('data')
# first conv
conv1 <- mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20)
tanh1 <- mx.symbol.Activation(data=conv1, act_type="tanh")
pool1 <- mx.symbol.Pooling(data=tanh1, pool_type="max",
                           kernel=c(2,2), stride=c(2,2))
# second conv
conv2 <- mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)
tanh2 <- mx.symbol.Activation(data=conv2, act_type="tanh")
pool2 <- mx.symbol.Pooling(data=tanh2, pool_type="max",
                           kernel=c(2,2), stride=c(2,2))
# first fullc
flatten <- mx.symbol.Flatten(data=pool2)
fc1 <- mx.symbol.FullyConnected(data=flatten, num_hidden=500)
tanh3 <- mx.symbol.Activation(data=fc1, act_type="tanh")
# second fullc
fc2 <- mx.symbol.FullyConnected(data=tanh3, num_hidden=10)

# loss，softmax
lenet <- mx.symbol.SoftmaxOutput(data=fc2)

##matrices into arrays
train.array <- train.x
dim(train.array) <- c(28, 28, 1, ncol(train.x))
test.array <- test
dim(test.array) <- c(28, 28, 1, ncol(test))

n.gpu <- 1
device.cpu <- mx.cpu()
device.gpu <- lapply(0:(n.gpu-1), function(i) {
  mx.gpu(i)
})

>#定义随机种子
>mx.set.seed(0)
>#当前的时间
>tic <- proc.time()
>model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
                                     ctx=device.cpu, num.round=1, array.batch.size=100,
                                     learning.rate=0.05, momentum=0.9, wd=0.00001,
                                     eval.metric=mx.metric.accuracy,
                                     epoch.end.callback=mx.callback.log.train.metric(100))

Start training with 1 devices
[1] Train-accuracy=0.557112171837709

>#基于cpu计算的时间
>print(proc.time() - tic)
用户    系统    流逝 
 502.69    2.66 3063.02 

> #随机种子
> mx.set.seed(0)
> #当前时间
> tic <- proc.time()
> #模型定义
> model <- mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
                                     ctx=device.gpu, num.round=5, array.batch.size=100,
                                     learning.rate=0.05, momentum=0.9, wd=0.00001,
                                     eval.metric=mx.metric.accuracy,
                                     epoch.end.callback=mx.callback.log.train.metric(100))


Start training with 1 devices
Error in mx.nd.internal.empty.array(shape, ctx) : 
  [17:08:47] src/storage/storage.cc:78: Compile with USE_CUDA=1 to enable GPU usage
[17:08:47] C:/Users/qkou/mxnet/dmlc-core/include/dmlc/logging.h:235: [17:08:47] src/storage/storage.cc:78: Compile with USE_CUDA=1 to enable GPU usage

> print(proc.time() - tic)

用户 系统 流逝 
0.26 0.33 4.68 

preds <- predict(model, test.array)
pred.label <- max.col(t(preds)) - 1

submission <- data.frame(ImageId=1:ncol(test), Label=pred.label)
write.csv(submission, file='submission.csv', row.names=FALSE, quote=FALSE)