Kaggleの「Dogs vs. Cats」をやってみる。
こちらを参考にさせて頂いた。
- train.zip（25000枚のjpeg画像）をダウンロードして解凍。
- 猫、犬の画像をそれぞれcatフォルダ、dogフォルダに分ける。
データの準備
library(EBImage)
x_train <- array(0, c(56, 56, 3, 25000))
path <- "D:/rworks/dogcat/train/cat/"
setwd(path)
files_cat <- list.files()
for (i in 1:12500) {
img <- readImage(files_cat[i])
resize <- resize(img, w = 56, h = 56)
x_train[,,,i] <- resize@.Data
}
path <- "D:/rworks/dogcat/train/dog/"
setwd(path)
files_dog <- list.files()
for (i in 1:12500) {
img <- readImage(files_dog[i])
resize <- resize(img, w = 56, h = 56)
x_train[,,, 12500 + i] <- resize@.Data
}
t_train <- c(rep(0, 12500), rep(1, 12500))
sampling <- sample(25000, 22500)
x_train_mini <- x_train[,,, sampling]
t_train_mini <- t_train[sampling]
x_test_mini <- x_train[,,, - sampling]
t_test_mini <- t_train[-sampling]
library(mxnet)
mydata <- mx.symbol.Variable("data")
conv1 <- mx.symbol.Convolution(
data = mydata,
kernel = c(3, 3),
num_filter = 32)
act1 <- mx.symbol.Activation(data = conv1, act_type = "relu")
pool1 <- mx.symbol.Pooling(
data = act1,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
conv2 <- mx.symbol.Convolution(
data = pool1,
kernel = c(3, 3),
num_filter = 64)
act2 <- mx.symbol.Activation(data = conv2, act_type = "relu")
pool2 <- mx.symbol.Pooling(
data = act2,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
fc1 <- mx.symbol.FullyConnected(data = pool2, num_hidden = 200)
drop1 <- mx.symbol.Dropout(data=fc1)
act3 <- mx.symbol.Activation(data = drop1, act_type = "relu")
fc2 <- mx.symbol.FullyConnected(data = act3, num_hidden = 2)
drop2 <- mx.symbol.Dropout(data = fc2)
softmax <- mx.symbol.SoftmaxOutput(data = drop2)
mx.set.seed(1)
model <- mx.model.FeedForward.create(
softmax, X = x_train_mini,
y = t_train_mini,
ctx = mx.cpu(),
num.round = 30,
array.batch.size = 100,
optimizer = "adam",
eval.metric = mx.metric.accuracy,
initializer = mx.init.Xavier(factor_type = "in", rnd_type = "gaussian", magnitude = 2),
eval.data = list(data = x_test_mini, label = t_test_mini),
array.layout = "auto")
結果
Start training with 1 devices
[1] Train-accuracy=0.557276785714286
[1] Validation-accuracy=0.6684
[2] Train-accuracy=0.627555555555556
[2] Validation-accuracy=0.6976
[3] Train-accuracy=0.663066666666667
[3] Validation-accuracy=0.7332
・・・
[28] Train-accuracy=0.815111111111111
[28] Validation-accuracy=0.7948
[29] Train-accuracy=0.820444444444445
[29] Validation-accuracy=0.7892
[30] Train-accuracy=0.817377777777778
[30] Validation-accuracy=0.796
テストデータのaccuracy：79.6%
> pred <- predict(model, x_train_mini)
> pred_train <- apply(pred, 2, which.max) - 1
> sum(pred_train == t_train_mini) / 22500
[1] 0.9488
訓練データのaccuracy：94.9%
訓練後のデータを保存する場合にはこちらを参照
mx.model.save.RData <- function(model, filename) {
if (!inherits(model, "MXFeedForwardModel")) stop("Not a MXNet model!")
model_rdata <- list()
model_rdata[['symbol_json']] <- model$symbol$as.json()
model_rdata[['arg.params']] <- lapply(model$arg.params, as.array)
model_rdata[['aux.params']] <- lapply(model$aux.params, as.array)
saveRDS(model_rdata, filename)
}
mx.model.load.RData <- function(filename) {
model_rdata <- readRDS(filename)
symbol <- mx.symbol.load.json(model_rdata$symbol_json)
arg.params <- lapply(model_rdata$arg.params, mx.nd.array)
aux.params <- lapply(model_rdata$aux.params, mx.nd.array)
model <- list(symbol = symbol, arg.params = arg.params, aux.params = aux.params)
return(structure(model, class = "MXFeedForwardModel"))
}
（2017年6月27日追記）こちらのほうがおすすめ。
フィルター数32の1番目の畳み込み層をフィルタ数16の二つの畳み込み層に分けてみる
library(mxnet)
mydata <- mx.symbol.Variable("data")
conv1 <- mx.symbol.Convolution(
data = mydata,
kernel = c(3, 3),
num_filter = 16)
conv1_5 <- mx.symbol.Convolution(
data = conv1,
kernel = c(3, 3),
num_filter = 16)
act1 <- mx.symbol.Activation(data = conv1_5, act_type = "relu")
pool1 <- mx.symbol.Pooling(
data = act1,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
conv2 <- mx.symbol.Convolution(
data = pool1,
kernel = c(3, 3),
num_filter = 64)
act2 <- mx.symbol.Activation(data = conv2, act_type = "relu")
pool2 <- mx.symbol.Pooling(
data = act2,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
fc1 <- mx.symbol.FullyConnected(data = pool2, num_hidden = 200)
drop1 <- mx.symbol.Dropout(data = fc1)
act3 <- mx.symbol.Activation(data = drop1, act_type = "relu")
fc2 <- mx.symbol.FullyConnected(data = act3, num_hidden = 2)
drop2 <- mx.symbol.Dropout(data = fc2)
softmax <- mx.symbol.SoftmaxOutput(data = drop2)
mx.set.seed(1)
model <- mx.model.FeedForward.create(
softmax, X = x_train_mini,
y = t_train_mini,
ctx = mx.cpu(),
num.round = 30,
array.batch.size = 100,
optimizer = "adam",
eval.metric = mx.metric.accuracy,
initializer = mx.init.Xavier(factor_type = "in", rnd_type = "gaussian", magnitude = 2),
eval.data = list(data = x_test_mini, label = t_test_mini),
array.layout = "auto")
結果
Start training with 1 devices
[1] Train-accuracy=0.585848214285714
[1] Validation-accuracy=0.7144
[2] Train-accuracy=0.644711111111111
[2] Validation-accuracy=0.7272
[3] Train-accuracy=0.675244444444445
[3] Validation-accuracy=0.7536
・・・
[28] Train-accuracy=0.823822222222223
[28] Validation-accuracy=0.79
[29] Train-accuracy=0.815111111111112
[29] Validation-accuracy=0.7984
[30] Train-accuracy=0.826044444444445
[30] Validation-accuracy=0.806
1番目と2番目の畳み込み層の間に活性化関数を入れてみる
library(mxnet)
mydata <- mx.symbol.Variable("data")
conv1 <- mx.symbol.Convolution(
data = mydata,
kernel = c(3, 3),
num_filter = 16)
act0 <- mx.symbol.Activation(data = conv1, act_type = "relu")
conv1_5 <- mx.symbol.Convolution(
data = act0,
kernel = c(3, 3),
num_filter = 16)
act1 <- mx.symbol.Activation(data = conv1_5, act_type = "relu")
pool1 <- mx.symbol.Pooling(
data = act1,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
conv2 <- mx.symbol.Convolution(
data = pool1,
kernel = c(3, 3),
num_filter = 64)
act2 <- mx.symbol.Activation(data = conv2, act_type = "relu")
pool2 <- mx.symbol.Pooling(
data = act2,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
fc1 <- mx.symbol.FullyConnected(data = pool2, num_hidden = 200)
drop1 <- mx.symbol.Dropout(data = fc1)
act3 <- mx.symbol.Activation(data = drop1, act_type = "relu")
fc2 <- mx.symbol.FullyConnected(data = act3, num_hidden = 2)
drop2 <- mx.symbol.Dropout(data = fc2)
softmax <- mx.symbol.SoftmaxOutput(data = drop2)
mx.set.seed(1)
model <- mx.model.FeedForward.create(
softmax, X = x_train_mini,
y = t_train_mini,
ctx = mx.cpu(),
num.round = 30,
array.batch.size = 100,
optimizer = "adam",
eval.metric = mx.metric.accuracy,
initializer = mx.init.Xavier(factor_type = "in", rnd_type = "gaussian", magnitude = 2),
eval.data = list(data = x_test_mini, label = t_test_mini),
array.layout = "auto")
結果
Start training with 1 devices
[1] Train-accuracy=0.558973214285714
[1] Validation-accuracy=0.6536
[2] Train-accuracy=0.642577777777778
[2] Validation-accuracy=0.7352
[3] Train-accuracy=0.681155555555555
[3] Validation-accuracy=0.7532
・・・
[28] Train-accuracy=0.824933333333334
[28] Validation-accuracy=0.8012
[29] Train-accuracy=0.820311111111112
[29] Validation-accuracy=0.8072
[30] Train-accuracy=0.827511111111112
[30] Validation-accuracy=0.798
2番目の畳み込み層をフィルタ数32、全結合層のニューロン数を300に増やしてみる
- そろそろCPUの限界を感じ始めた（num.round=20に）
- 時間の割には改善なし
library(mxnet)
mydata <- mx.symbol.Variable("data")
conv1 <- mx.symbol.Convolution(
data = mydata,
kernel = c(3, 3),
num_filter = 16)
conv1_5 <- mx.symbol.Convolution(
data = conv1,
kernel = c(3, 3),
num_filter = 32)
act1 <- mx.symbol.Activation(data = conv1_5, act_type = "relu")
pool1 <- mx.symbol.Pooling(
data = act1,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
conv2 <- mx.symbol.Convolution(
data = pool1,
kernel = c(3, 3),
num_filter = 64)
act2 <- mx.symbol.Activation(data = conv2, act_type = "relu")
pool2 <- mx.symbol.Pooling(
data = act2,
pool_type = "max",
kernel = c(2, 2),
stride = c(2, 2))
fc1 <- mx.symbol.FullyConnected(data = pool2, num_hidden = 300)
drop1 <- mx.symbol.Dropout(data = fc1)
act3 <- mx.symbol.Activation(data = drop1, act_type = "relu")
fc2 <- mx.symbol.FullyConnected(data = act3, num_hidden = 2)
drop2 <- mx.symbol.Dropout(data = fc2)
softmax <- mx.symbol.SoftmaxOutput(data = drop2)
mx.set.seed(1)
model <- mx.model.FeedForward.create(
softmax, X = x_train_mini,
y = t_train_mini,
ctx = mx.cpu(),
num.round = 20,
array.batch.size = 100,
optimizer = "adam",
eval.metric = mx.metric.accuracy,
initializer = mx.init.Xavier(factor_type = "in", rnd_type = "gaussian", magnitude = 2),
eval.data = list(data = x_test_mini, label = t_test_mini),
array.layout = "auto")
結果
Start training with 1 devices
[1] Train-accuracy=0.5896875
[1] Validation-accuracy=0.7084
[2] Train-accuracy=0.6484
[2] Validation-accuracy=0.7348
[3] Train-accuracy=0.692933333333333
[3] Validation-accuracy=0.74
・・・
[18] Train-accuracy=0.852711111111111
[18] Validation-accuracy=0.7916
[19] Train-accuracy=0.853066666666668
[19] Validation-accuracy=0.7856
[20] Train-accuracy=0.855155555555556
[20] Validation-accuracy=0.7948
訓練データ200～300枚程度を手作業で背景を取り除くようにトリミングしてみる
- あとで縮小しやすいように正方形でトリミング
- 結果はわずかに改善
- 訓練データの重要性を改めて認識した
Start training with 1 devices
[1] Train-accuracy=0.581026785714286
[1] Validation-accuracy=0.6712
[2] Train-accuracy=0.654933333333333
[2] Validation-accuracy=0.7428
[3] Train-accuracy=0.689022222222222
[3] Validation-accuracy=0.7696
・・・
[18] Train-accuracy=0.853777777777778
[18] Validation-accuracy=0.8084
[19] Train-accuracy=0.855688888888889
[19] Validation-accuracy=0.8132
[20] Train-accuracy=0.854355555555556
[20] Validation-accuracy=0.8172
