DK_CNN_net: Multi-classifiers: Feature-Mapping for Professional Careers
Written by DK
- rm(list=ls())
- getwd()
library(keras)
library(tensorflow)inpath = trainpath
dnall = list.files(inpath, full.names = FALSE)
# define the classification objects
clasobj_000 = "firefighter"
clasobj_001 = "doctor"
clasobj_002 = "engineer"
clasobj_003 = "farmer"
clasobj_004 = "chef"
# clasobj_005 = "mechanic"
# clasobj_006 = "waiter"
Img_000_dn = dnall[grep(clasobj_000, dnall)]
Img_001_dn = dnall[grep(clasobj_001, dnall)]
Img_002_dn = dnall[grep(clasobj_002, dnall)]
Img_003_dn = dnall[grep(clasobj_003, dnall)]
Img_004_dn = dnall[grep(clasobj_004, dnall)]
# Img_005_dn = dnall[grep(clasobj_005, dnall)]
# Img_006_dn = dnall[grep(clasobj_006, dnall)]Random Sampling: Train data
use_train_imag = 40
use_test_imag = 40
getsample_Img_000_dn = getRandomSample(Img_000_dn, use_train_imag, use_test_imag )
getsample_Img_001_dn = getRandomSample(Img_001_dn, use_train_imag, use_test_imag )
getsample_Img_002_dn = getRandomSample(Img_002_dn, use_train_imag, use_test_imag )
getsample_Img_003_dn = getRandomSample(Img_003_dn, use_train_imag, use_test_imag )
getsample_Img_004_dn = getRandomSample(Img_004_dn, use_train_imag, use_test_imag )
# getsample_Img_005_dn = getRandomSample(Img_005_dn, use_train_imag, use_test_imag )
# getsample_Img_006_dn = getRandomSample(Img_006_dn, use_train_imag, use_test_imag )mypic_Train_FHCD = c( Img_000_dn[sort(getsample_Img_000_dn[[1]])],
Img_001_dn[sort(getsample_Img_001_dn[[1]])],
Img_002_dn[sort(getsample_Img_002_dn[[1]])],
Img_003_dn[sort(getsample_Img_003_dn[[1]])],
Img_004_dn[sort(getsample_Img_004_dn[[1]])]
# Img_005_dn[sort(getsample_Img_005_dn[[1]])]
# Img_006_dn[sort(getsample_Img_006_dn[[1]])]
)Random Sampling: test data
mypic_Test_FHCD = c( Img_000_dn[sort(getsample_Img_000_dn[[2]])],
Img_001_dn[sort(getsample_Img_001_dn[[2]])],
Img_002_dn[sort(getsample_Img_002_dn[[2]])],
Img_003_dn[sort(getsample_Img_003_dn[[2]])],
Img_004_dn[sort(getsample_Img_004_dn[[2]])]
# Img_005_dn[sort(getsample_Img_005_dn[[2]])]
# Img_006_dn[sort(getsample_Img_006_dn[[2]])]
)+ image file : read, format to RGB, normalize and resize
+ 200 object Images for training
traincomb_FHCD = combine(trainImg_FHCD)
x = tile(traincomb_FHCD,20)
display(x, title ="Pictures")
+ 200 object Images for testing
testcomb_FHCD = combine(testImg_FHCD)
xy = tile(testcomb_FHCD,20)
display(xy, title ="Pictures")
+ Construct CNN Model
classifier_units = dim(trainLabels80)[2]
model = keras_model_sequential()
model %>%
layer_conv_2d(filters = 32,
kernel_size = c(3,3),
activation = "relu",
input_shape = c(100,100,3)) %>%
layer_conv_2d(filters = 32,
kernel_size = c(3,3),
activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
# layer_dropout(rate = 0.25) %>%
layer_conv_2d(filters = 64,
kernel_size = c(3,3),
activation = "relu") %>%
# layer_conv_2d(filters = 64,
# kernel_size = c(3,3),
# activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
# layer_dropout(rate = 0.25) %>%
layer_conv_2d(filters = 128,
kernel_size = c(3,3),
activation = "relu") %>%
layer_conv_2d(filters = 128,
kernel_size = c(3,3),
activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
# layer_dropout(rate = 0.25) %>%
# layer_conv_2d(filters = 256,
# kernel_size = c(3,3),
# activation = "relu") %>%
layer_conv_2d(filters = 256,
kernel_size = c(3,3),
activation = "relu") %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
# layer_dropout(rate = 0.25) %>%
layer_flatten() %>%
layer_dense(units = 128, activation = "relu")%>%
layer_dropout(rate=0.1)%>%
layer_dense(units = classifier_units, activation = "softmax") %>%
compile(loss = "categorical_crossentropy",
optimizer = optimizer_sgd(lr = 0.01,
decay =1e-6,
momentum = 0.9,
nesterov=T),
metrics = c("accuracy"))summary(model)## ___________________________________________________________________________
## Layer (type) Output Shape Param #
## ===========================================================================
## conv2d (Conv2D) (None, 98, 98, 32) 896
## ___________________________________________________________________________
## conv2d_1 (Conv2D) (None, 96, 96, 32) 9248
## ___________________________________________________________________________
## max_pooling2d (MaxPooling2D) (None, 48, 48, 32) 0
## ___________________________________________________________________________
## conv2d_2 (Conv2D) (None, 46, 46, 64) 18496
## ___________________________________________________________________________
## max_pooling2d_1 (MaxPooling2D) (None, 23, 23, 64) 0
## ___________________________________________________________________________
## conv2d_3 (Conv2D) (None, 21, 21, 128) 73856
## ___________________________________________________________________________
## conv2d_4 (Conv2D) (None, 19, 19, 128) 147584
## ___________________________________________________________________________
## max_pooling2d_2 (MaxPooling2D) (None, 9, 9, 128) 0
## ___________________________________________________________________________
## conv2d_5 (Conv2D) (None, 7, 7, 256) 295168
## ___________________________________________________________________________
## max_pooling2d_3 (MaxPooling2D) (None, 3, 3, 256) 0
## ___________________________________________________________________________
## flatten (Flatten) (None, 2304) 0
## ___________________________________________________________________________
## dense (Dense) (None, 128) 295040
## ___________________________________________________________________________
## dropout (Dropout) (None, 128) 0
## ___________________________________________________________________________
## dense_1 (Dense) (None, 5) 645
## ===========================================================================
## Total params: 840,933
## Trainable params: 840,933
## Non-trainable params: 0
## ___________________________________________________________________________+ fit model : first run: 500 epochs
Epochs_number_set = 500
validation_split_set = 0.1
Batch_size_set = tot_trainImg*(1-validation_split_set)
TrainResult80 = model %>%
fit(traincomb_FHCD_df ,
trainLabels80,
epochs = Epochs_number_set,
batch_size = Batch_size_set,
validation_split = validation_split_set)
+ evaluation and prediction for train data
model %>% evaluate(traincomb_FHCD_df, trainLabels80)## $loss
## [1] 1.242268
##
## $acc
## [1] 0.9pred = model %>% predict_classes(traincomb_FHCD_df)
table(PredictCls = pred, ActualCls = trainme80)## ActualCls
## PredictCls 0 1 2 3 4
## 0 40 0 0 0 9
## 1 0 40 0 0 5
## 2 0 0 40 0 3
## 3 0 0 0 40 3
## 4 0 0 0 0 20prob = model %>% predict_proba(traincomb_FHCD_df)
predtrain.df = data.frame(cbind(1:tot_trainImg, round(prob, 4)*100, PredCls = pred, ActuCls = trainme80))| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| 0 | 40 | 0 | 0 | 0 | 9 |
| 1 | 0 | 40 | 0 | 0 | 5 |
| 2 | 0 | 0 | 40 | 0 | 3 |
| 3 | 0 | 0 | 0 | 40 | 3 |
| 4 | 0 | 0 | 0 | 0 | 20 |
| Note: | |||||
| overall_accuracy = 180 units |
| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| 0 | 100 | 0 | 0 | 0 | 22.5 |
| 1 | 0 | 100 | 0 | 0 | 12.5 |
| 2 | 0 | 0 | 100 | 0 | 7.5 |
| 3 | 0 | 0 | 0 | 100 | 7.5 |
| 4 | 0 | 0 | 0 | 0 | 50.0 |
| Note: | |||||
| overall_accuracy = 90 % |
| Index | Prb_firefighter | Prb_doctor | Prb_engineer | Prb_farmer | Prb_chef | PredCls | ActuCls | ActuObjs |
|---|---|---|---|---|---|---|---|---|
| 1 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 2 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 3 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 4 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 5 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 6 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 7 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 8 | 99.91 | 0.00 | 0.08 | 0.01 | 0.00 | 0 | 0 | firefighter |
| 9 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 10 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 11 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 12 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 13 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 14 | 99.99 | 0.00 | 0.01 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 15 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 16 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 17 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 18 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 19 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 20 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 21 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 22 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 23 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 24 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 25 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 26 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 27 | 99.92 | 0.00 | 0.01 | 0.06 | 0.00 | 0 | 0 | firefighter |
| 28 | 99.99 | 0.00 | 0.00 | 0.01 | 0.00 | 0 | 0 | firefighter |
| 29 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 30 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 31 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 32 | 99.90 | 0.00 | 0.00 | 0.10 | 0.00 | 0 | 0 | firefighter |
| 33 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 34 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 35 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 36 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 37 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 38 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 39 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 40 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 41 | 0.00 | 99.99 | 0.00 | 0.00 | 0.01 | 1 | 1 | doctor |
| 42 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 43 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 44 | 0.00 | 99.99 | 0.00 | 0.00 | 0.01 | 1 | 1 | doctor |
| 45 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 46 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 47 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 48 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 49 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 50 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 51 | 0.00 | 99.99 | 0.00 | 0.00 | 0.01 | 1 | 1 | doctor |
| 52 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 53 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 54 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 55 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 56 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 57 | 0.00 | 99.99 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 58 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 59 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 60 | 0.00 | 99.99 | 0.01 | 0.00 | 0.00 | 1 | 1 | doctor |
| 61 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 62 | 0.00 | 99.95 | 0.05 | 0.00 | 0.00 | 1 | 1 | doctor |
| 63 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 64 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 65 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 66 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 67 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 68 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 69 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 70 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 71 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 72 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 73 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 74 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 75 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 76 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 77 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 78 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 79 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 80 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 81 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 82 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 83 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 84 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 85 | 0.00 | 0.01 | 99.99 | 0.01 | 0.00 | 2 | 2 | engineer |
| 86 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 87 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 88 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 89 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 90 | 0.01 | 0.00 | 99.99 | 0.00 | 0.00 | 2 | 2 | engineer |
| 91 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 92 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 93 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 94 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 95 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 96 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 97 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 98 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 99 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 100 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 101 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 102 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 103 | 0.02 | 0.00 | 99.98 | 0.00 | 0.00 | 2 | 2 | engineer |
| 104 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 105 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 106 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 107 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 108 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 109 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 110 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 111 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 112 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 113 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 114 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 115 | 0.00 | 0.00 | 99.98 | 0.01 | 0.00 | 2 | 2 | engineer |
| 116 | 0.01 | 0.00 | 99.99 | 0.00 | 0.00 | 2 | 2 | engineer |
| 117 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 118 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 119 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 120 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 121 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 122 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 123 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 124 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 125 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 126 | 0.01 | 0.00 | 0.01 | 99.98 | 0.00 | 3 | 3 | farmer |
| 127 | 0.00 | 0.00 | 0.01 | 99.99 | 0.00 | 3 | 3 | farmer |
| 128 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 129 | 0.01 | 0.00 | 0.02 | 99.97 | 0.00 | 3 | 3 | farmer |
| 130 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 131 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 132 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 133 | 0.00 | 0.00 | 0.00 | 99.99 | 0.01 | 3 | 3 | farmer |
| 134 | 0.06 | 0.00 | 0.01 | 99.92 | 0.01 | 3 | 3 | farmer |
| 135 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 136 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 137 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 138 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 139 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 140 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 141 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 142 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 143 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 144 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 145 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 146 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 147 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 148 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 149 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 150 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 151 | 0.02 | 0.00 | 0.04 | 99.90 | 0.05 | 3 | 3 | farmer |
| 152 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 153 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 154 | 0.31 | 0.00 | 0.02 | 99.64 | 0.02 | 3 | 3 | farmer |
| 155 | 0.00 | 0.00 | 0.01 | 99.99 | 0.00 | 3 | 3 | farmer |
| 156 | 0.00 | 0.00 | 0.01 | 99.98 | 0.01 | 3 | 3 | farmer |
| 157 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 158 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 159 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 160 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 161 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 162 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 163 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 164 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 165 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 166 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 167 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 168 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 169 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 170 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 171 | 0.00 | 0.02 | 0.00 | 0.00 | 99.98 | 4 | 4 | chef |
| 172 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 173 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 174 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 175 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 176 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 177 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 178 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 179 | 0.00 | 0.00 | 0.01 | 0.05 | 99.94 | 4 | 4 | chef |
| 180 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 181 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 182 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 4 | chef |
| 183 | 0.00 | 99.75 | 0.06 | 0.03 | 0.16 | 1 | 4 | chef |
| 184 | 0.00 | 99.61 | 0.00 | 0.00 | 0.39 | 1 | 4 | chef |
| 185 | 0.00 | 0.00 | 9.78 | 90.20 | 0.02 | 3 | 4 | chef |
| 186 | 99.97 | 0.00 | 0.00 | 0.03 | 0.00 | 0 | 4 | chef |
| 187 | 99.82 | 0.00 | 0.18 | 0.00 | 0.00 | 0 | 4 | chef |
| 188 | 0.00 | 13.66 | 84.04 | 2.29 | 0.00 | 2 | 4 | chef |
| 189 | 99.97 | 0.03 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 190 | 0.00 | 0.17 | 99.82 | 0.01 | 0.00 | 2 | 4 | chef |
| 191 | 99.58 | 0.00 | 0.00 | 0.41 | 0.00 | 0 | 4 | chef |
| 192 | 0.00 | 60.96 | 0.00 | 0.00 | 39.04 | 1 | 4 | chef |
| 193 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 194 | 36.48 | 0.00 | 44.45 | 19.07 | 0.00 | 2 | 4 | chef |
| 195 | 0.54 | 2.10 | 0.00 | 62.49 | 34.86 | 3 | 4 | chef |
| 196 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 4 | chef |
| 197 | 97.07 | 0.00 | 0.00 | 0.00 | 2.93 | 0 | 4 | chef |
| 198 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 4 | chef |
| 199 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 200 | 99.96 | 0.04 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
+ evaluation and prediction - test data
model %>% evaluate(testcomb_FHCD_df, testLabels60)## $loss
## [1] 5.877803
##
## $acc
## [1] 0.44pred = model %>% predict_classes(testcomb_FHCD_df)
table(PredictCls = pred, ActualCls = testme60)## ActualCls
## PredictCls 0 1 2 3 4
## 0 19 0 7 8 9
## 1 4 28 8 3 10
## 2 8 8 16 9 6
## 3 8 2 5 16 6
## 4 1 2 4 4 9prob = model %>% predict_proba(testcomb_FHCD_df)
predtrain.df = data.frame(cbind(1:tot_testImg, round(prob, 4)*100, PredCls = pred, ActuCls = testme60))| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| 0 | 19 | 0 | 7 | 8 | 9 |
| 1 | 4 | 28 | 8 | 3 | 10 |
| 2 | 8 | 8 | 16 | 9 | 6 |
| 3 | 8 | 2 | 5 | 16 | 6 |
| 4 | 1 | 2 | 4 | 4 | 9 |
| Note: | |||||
| overall_accuracy = 88 units |
| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| 0 | 47.5 | 0 | 17.5 | 20.0 | 22.5 |
| 1 | 10.0 | 70 | 20.0 | 7.5 | 25.0 |
| 2 | 20.0 | 20 | 40.0 | 22.5 | 15.0 |
| 3 | 20.0 | 5 | 12.5 | 40.0 | 15.0 |
| 4 | 2.5 | 5 | 10.0 | 10.0 | 22.5 |
| Note: | |||||
| overall_accuracy = 44 % |
| Index | Prb_firefighter | Prb_doctor | Prb_engineer | Prb_farmer | Prb_chef | PredCls | ActuCls | ActuObjs |
|---|---|---|---|---|---|---|---|---|
| 1 | 29.67 | 0.00 | 4.24 | 66.09 | 0.00 | 3 | 0 | firefighter |
| 2 | 0.00 | 0.00 | 48.35 | 51.65 | 0.00 | 3 | 0 | firefighter |
| 3 | 9.21 | 0.00 | 0.24 | 90.43 | 0.11 | 3 | 0 | firefighter |
| 4 | 0.64 | 0.00 | 20.15 | 79.21 | 0.00 | 3 | 0 | firefighter |
| 5 | 2.98 | 0.00 | 0.25 | 96.66 | 0.10 | 3 | 0 | firefighter |
| 6 | 65.66 | 0.00 | 1.17 | 33.18 | 0.00 | 0 | 0 | firefighter |
| 7 | 99.96 | 0.00 | 0.03 | 0.01 | 0.00 | 0 | 0 | firefighter |
| 8 | 0.47 | 0.00 | 0.06 | 99.45 | 0.03 | 3 | 0 | firefighter |
| 9 | 98.86 | 0.00 | 1.14 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 10 | 98.05 | 0.00 | 1.95 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 11 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 0 | firefighter |
| 12 | 14.11 | 0.01 | 2.25 | 81.74 | 1.88 | 3 | 0 | firefighter |
| 13 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 0 | firefighter |
| 14 | 90.61 | 0.00 | 9.39 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 15 | 0.08 | 99.92 | 0.00 | 0.00 | 0.00 | 1 | 0 | firefighter |
| 16 | 0.00 | 5.80 | 0.16 | 0.00 | 94.04 | 4 | 0 | firefighter |
| 17 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 18 | 8.47 | 0.00 | 91.53 | 0.00 | 0.00 | 2 | 0 | firefighter |
| 19 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 0 | firefighter |
| 20 | 98.70 | 0.00 | 1.30 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 21 | 0.46 | 0.00 | 99.54 | 0.00 | 0.00 | 2 | 0 | firefighter |
| 22 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 23 | 71.21 | 0.00 | 22.38 | 6.42 | 0.00 | 0 | 0 | firefighter |
| 24 | 1.77 | 0.00 | 98.17 | 0.06 | 0.00 | 2 | 0 | firefighter |
| 25 | 99.95 | 0.00 | 0.02 | 0.01 | 0.02 | 0 | 0 | firefighter |
| 26 | 0.00 | 0.00 | 71.63 | 28.37 | 0.00 | 2 | 0 | firefighter |
| 27 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 28 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 29 | 93.05 | 0.00 | 0.05 | 6.90 | 0.01 | 0 | 0 | firefighter |
| 30 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 31 | 99.88 | 0.00 | 0.12 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 32 | 0.16 | 95.27 | 0.08 | 0.01 | 4.47 | 1 | 0 | firefighter |
| 33 | 99.25 | 0.00 | 0.71 | 0.03 | 0.00 | 0 | 0 | firefighter |
| 34 | 33.09 | 0.00 | 63.68 | 3.23 | 0.00 | 2 | 0 | firefighter |
| 35 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 36 | 0.27 | 0.03 | 99.70 | 0.00 | 0.00 | 2 | 0 | firefighter |
| 37 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 0 | firefighter |
| 38 | 10.89 | 0.00 | 89.11 | 0.00 | 0.00 | 2 | 0 | firefighter |
| 39 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 40 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 0 | firefighter |
| 41 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 42 | 0.00 | 0.62 | 98.76 | 0.54 | 0.08 | 2 | 1 | doctor |
| 43 | 0.00 | 0.00 | 10.95 | 88.56 | 0.49 | 3 | 1 | doctor |
| 44 | 0.02 | 0.00 | 4.33 | 72.90 | 22.75 | 3 | 1 | doctor |
| 45 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 46 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 47 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 48 | 0.00 | 93.91 | 6.09 | 0.00 | 0.00 | 1 | 1 | doctor |
| 49 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 50 | 0.00 | 99.95 | 0.01 | 0.00 | 0.04 | 1 | 1 | doctor |
| 51 | 0.00 | 98.61 | 0.00 | 0.00 | 1.39 | 1 | 1 | doctor |
| 52 | 0.00 | 0.18 | 99.81 | 0.01 | 0.00 | 2 | 1 | doctor |
| 53 | 30.61 | 0.29 | 69.09 | 0.01 | 0.00 | 2 | 1 | doctor |
| 54 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 55 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 56 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 57 | 0.00 | 99.99 | 0.01 | 0.00 | 0.00 | 1 | 1 | doctor |
| 58 | 0.00 | 99.92 | 0.08 | 0.00 | 0.00 | 1 | 1 | doctor |
| 59 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 60 | 0.00 | 99.94 | 0.00 | 0.00 | 0.06 | 1 | 1 | doctor |
| 61 | 0.00 | 85.65 | 14.35 | 0.00 | 0.00 | 1 | 1 | doctor |
| 62 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 63 | 0.00 | 99.94 | 0.00 | 0.00 | 0.06 | 1 | 1 | doctor |
| 64 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 65 | 0.00 | 1.50 | 0.00 | 0.00 | 98.50 | 4 | 1 | doctor |
| 66 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 67 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 1 | doctor |
| 68 | 0.00 | 66.54 | 33.46 | 0.00 | 0.00 | 1 | 1 | doctor |
| 69 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 70 | 0.01 | 0.00 | 99.99 | 0.00 | 0.00 | 2 | 1 | doctor |
| 71 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 72 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 73 | 0.00 | 97.87 | 2.13 | 0.00 | 0.00 | 1 | 1 | doctor |
| 74 | 0.00 | 0.00 | 0.00 | 36.17 | 63.83 | 4 | 1 | doctor |
| 75 | 0.00 | 47.69 | 52.31 | 0.00 | 0.00 | 2 | 1 | doctor |
| 76 | 0.00 | 92.27 | 2.13 | 0.03 | 5.58 | 1 | 1 | doctor |
| 77 | 0.00 | 0.38 | 99.62 | 0.00 | 0.00 | 2 | 1 | doctor |
| 78 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 79 | 0.00 | 13.72 | 86.28 | 0.00 | 0.00 | 2 | 1 | doctor |
| 80 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 1 | doctor |
| 81 | 0.01 | 27.11 | 0.01 | 0.65 | 72.22 | 4 | 2 | engineer |
| 82 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 2 | engineer |
| 83 | 0.07 | 0.03 | 71.74 | 28.16 | 0.00 | 2 | 2 | engineer |
| 84 | 0.00 | 98.31 | 0.00 | 0.00 | 1.69 | 1 | 2 | engineer |
| 85 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 86 | 55.38 | 0.00 | 44.56 | 0.07 | 0.00 | 0 | 2 | engineer |
| 87 | 0.00 | 99.96 | 0.04 | 0.00 | 0.00 | 1 | 2 | engineer |
| 88 | 0.00 | 84.92 | 15.08 | 0.00 | 0.00 | 1 | 2 | engineer |
| 89 | 4.21 | 95.21 | 0.59 | 0.00 | 0.00 | 1 | 2 | engineer |
| 90 | 0.00 | 0.00 | 98.22 | 1.77 | 0.00 | 2 | 2 | engineer |
| 91 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 2 | engineer |
| 92 | 0.00 | 60.82 | 14.06 | 0.00 | 25.13 | 1 | 2 | engineer |
| 93 | 0.00 | 40.99 | 58.95 | 0.00 | 0.05 | 2 | 2 | engineer |
| 94 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 95 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 2 | engineer |
| 96 | 0.04 | 0.00 | 99.92 | 0.04 | 0.00 | 2 | 2 | engineer |
| 97 | 56.49 | 0.01 | 43.50 | 0.00 | 0.00 | 0 | 2 | engineer |
| 98 | 19.41 | 0.00 | 80.49 | 0.10 | 0.00 | 2 | 2 | engineer |
| 99 | 22.09 | 0.00 | 77.40 | 0.51 | 0.00 | 2 | 2 | engineer |
| 100 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 2 | engineer |
| 101 | 2.12 | 0.00 | 97.54 | 0.34 | 0.00 | 2 | 2 | engineer |
| 102 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 2 | engineer |
| 103 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 2 | engineer |
| 104 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 2 | engineer |
| 105 | 0.00 | 97.86 | 0.01 | 0.00 | 2.13 | 1 | 2 | engineer |
| 106 | 0.00 | 0.00 | 0.00 | 4.59 | 95.41 | 4 | 2 | engineer |
| 107 | 1.96 | 7.67 | 90.33 | 0.03 | 0.02 | 2 | 2 | engineer |
| 108 | 1.31 | 0.00 | 98.69 | 0.00 | 0.00 | 2 | 2 | engineer |
| 109 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 110 | 0.00 | 0.02 | 0.01 | 0.00 | 99.97 | 4 | 2 | engineer |
| 111 | 0.24 | 0.15 | 27.84 | 58.96 | 12.81 | 3 | 2 | engineer |
| 112 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 2 | engineer |
| 113 | 0.31 | 0.00 | 0.00 | 99.69 | 0.00 | 3 | 2 | engineer |
| 114 | 88.24 | 0.00 | 0.00 | 11.75 | 0.01 | 0 | 2 | engineer |
| 115 | 0.01 | 0.00 | 0.01 | 99.98 | 0.00 | 3 | 2 | engineer |
| 116 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 117 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 118 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 2 | engineer |
| 119 | 1.48 | 0.00 | 95.88 | 2.64 | 0.00 | 2 | 2 | engineer |
| 120 | 0.00 | 0.01 | 0.00 | 79.79 | 20.20 | 3 | 2 | engineer |
| 121 | 4.60 | 94.14 | 1.26 | 0.00 | 0.00 | 1 | 3 | farmer |
| 122 | 0.00 | 0.00 | 3.59 | 96.41 | 0.00 | 3 | 3 | farmer |
| 123 | 0.01 | 0.00 | 99.88 | 0.12 | 0.00 | 2 | 3 | farmer |
| 124 | 0.00 | 0.00 | 0.41 | 1.03 | 98.57 | 4 | 3 | farmer |
| 125 | 99.39 | 0.00 | 0.00 | 0.61 | 0.00 | 0 | 3 | farmer |
| 126 | 0.00 | 0.00 | 99.97 | 0.03 | 0.00 | 2 | 3 | farmer |
| 127 | 46.77 | 0.50 | 25.56 | 0.00 | 27.17 | 0 | 3 | farmer |
| 128 | 0.00 | 0.00 | 95.86 | 4.14 | 0.00 | 2 | 3 | farmer |
| 129 | 0.00 | 0.00 | 0.05 | 99.95 | 0.00 | 3 | 3 | farmer |
| 130 | 99.17 | 0.83 | 0.00 | 0.00 | 0.00 | 0 | 3 | farmer |
| 131 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 132 | 0.00 | 53.56 | 1.63 | 1.88 | 42.92 | 1 | 3 | farmer |
| 133 | 0.00 | 0.00 | 0.51 | 99.49 | 0.00 | 3 | 3 | farmer |
| 134 | 0.00 | 0.00 | 99.91 | 0.09 | 0.00 | 2 | 3 | farmer |
| 135 | 3.05 | 0.00 | 0.00 | 96.95 | 0.00 | 3 | 3 | farmer |
| 136 | 6.99 | 0.00 | 90.79 | 2.22 | 0.00 | 2 | 3 | farmer |
| 137 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 3 | farmer |
| 138 | 0.01 | 0.00 | 3.86 | 96.13 | 0.00 | 3 | 3 | farmer |
| 139 | 91.57 | 0.06 | 8.37 | 0.00 | 0.00 | 0 | 3 | farmer |
| 140 | 94.01 | 0.00 | 5.99 | 0.00 | 0.00 | 0 | 3 | farmer |
| 141 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 3 | farmer |
| 142 | 0.00 | 1.16 | 0.00 | 0.00 | 98.84 | 4 | 3 | farmer |
| 143 | 0.00 | 99.18 | 0.03 | 0.01 | 0.78 | 1 | 3 | farmer |
| 144 | 0.01 | 49.56 | 50.43 | 0.00 | 0.00 | 2 | 3 | farmer |
| 145 | 84.36 | 0.01 | 4.31 | 11.19 | 0.13 | 0 | 3 | farmer |
| 146 | 0.01 | 0.00 | 99.99 | 0.00 | 0.00 | 2 | 3 | farmer |
| 147 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 148 | 0.00 | 0.00 | 0.01 | 99.99 | 0.00 | 3 | 3 | farmer |
| 149 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 3 | farmer |
| 150 | 0.00 | 4.76 | 95.24 | 0.00 | 0.00 | 2 | 3 | farmer |
| 151 | 0.00 | 0.00 | 0.01 | 99.99 | 0.00 | 3 | 3 | farmer |
| 152 | 21.59 | 0.00 | 51.87 | 26.54 | 0.00 | 2 | 3 | farmer |
| 153 | 0.00 | 0.00 | 0.72 | 99.28 | 0.00 | 3 | 3 | farmer |
| 154 | 99.99 | 0.00 | 0.01 | 0.00 | 0.00 | 0 | 3 | farmer |
| 155 | 16.04 | 0.00 | 0.85 | 82.49 | 0.62 | 3 | 3 | farmer |
| 156 | 0.28 | 0.00 | 38.37 | 61.35 | 0.00 | 3 | 3 | farmer |
| 157 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 158 | 0.00 | 0.00 | 0.19 | 99.81 | 0.00 | 3 | 3 | farmer |
| 159 | 0.02 | 0.00 | 0.02 | 99.96 | 0.00 | 3 | 3 | farmer |
| 160 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 3 | farmer |
| 161 | 0.00 | 20.69 | 0.00 | 0.00 | 79.31 | 4 | 4 | chef |
| 162 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 2 | 4 | chef |
| 163 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 4 | chef |
| 164 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 4 | chef |
| 165 | 0.00 | 8.20 | 0.00 | 20.54 | 71.26 | 4 | 4 | chef |
| 166 | 0.06 | 95.42 | 0.00 | 0.07 | 4.45 | 1 | 4 | chef |
| 167 | 9.08 | 8.12 | 0.00 | 0.00 | 82.79 | 4 | 4 | chef |
| 168 | 16.88 | 0.00 | 2.54 | 80.57 | 0.00 | 3 | 4 | chef |
| 169 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 3 | 4 | chef |
| 170 | 0.10 | 3.02 | 0.00 | 0.00 | 96.87 | 4 | 4 | chef |
| 171 | 3.73 | 0.05 | 96.22 | 0.00 | 0.00 | 2 | 4 | chef |
| 172 | 96.26 | 3.74 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 173 | 0.00 | 0.51 | 0.00 | 0.00 | 99.48 | 4 | 4 | chef |
| 174 | 0.00 | 99.92 | 0.00 | 0.00 | 0.08 | 1 | 4 | chef |
| 175 | 30.51 | 69.42 | 0.06 | 0.00 | 0.00 | 1 | 4 | chef |
| 176 | 0.00 | 0.00 | 93.52 | 6.48 | 0.00 | 2 | 4 | chef |
| 177 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 178 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 179 | 0.00 | 99.92 | 0.00 | 0.00 | 0.08 | 1 | 4 | chef |
| 180 | 0.00 | 0.00 | 0.00 | 0.32 | 99.68 | 4 | 4 | chef |
| 181 | 0.00 | 0.00 | 99.19 | 0.81 | 0.00 | 2 | 4 | chef |
| 182 | 99.08 | 0.00 | 0.00 | 0.11 | 0.80 | 0 | 4 | chef |
| 183 | 3.47 | 0.00 | 0.00 | 96.52 | 0.01 | 3 | 4 | chef |
| 184 | 0.00 | 99.99 | 0.00 | 0.00 | 0.01 | 1 | 4 | chef |
| 185 | 0.00 | 98.50 | 1.50 | 0.00 | 0.00 | 1 | 4 | chef |
| 186 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 187 | 96.52 | 0.00 | 3.48 | 0.00 | 0.00 | 0 | 4 | chef |
| 188 | 93.09 | 0.01 | 6.64 | 0.20 | 0.06 | 0 | 4 | chef |
| 189 | 0.05 | 0.00 | 0.61 | 99.33 | 0.00 | 3 | 4 | chef |
| 190 | 98.86 | 0.00 | 1.14 | 0.00 | 0.00 | 0 | 4 | chef |
| 191 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 192 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 4 | 4 | chef |
| 193 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 194 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0 | 4 | chef |
| 195 | 0.00 | 0.10 | 99.90 | 0.00 | 0.00 | 2 | 4 | chef |
| 196 | 0.00 | 0.00 | 98.60 | 1.40 | 0.00 | 2 | 4 | chef |
| 197 | 0.00 | 99.98 | 0.00 | 0.00 | 0.02 | 1 | 4 | chef |
| 198 | 26.25 | 73.27 | 0.01 | 0.45 | 0.02 | 1 | 4 | chef |
| 199 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 1 | 4 | chef |
| 200 | 0.00 | 0.00 | 0.01 | 99.99 | 0.00 | 3 | 4 | chef |
Hardware configures:
- R version 3.5.2 Patched (2019-01-14 r75992)
- Platform: x86_64-w64-mingw32/x64 (64-bit)
- Processor Intel(R) Core(TM) i5-8250U CPU @ 1.60GHz, 1800 Mhz, 4 Core(s), 8 Logical Processor(s)
- Physical Memory : 4GB
- Graphic Display Card : GeFore MX150
total_run_time = round(end_time - start_time, 3)
total_run_time## Time difference of 25.071 mins