Driver-Drowsiness-Detection-using-Deep-Learning/model_training.py at main · pydeveloperashish/Driver-Drowsiness-Detection-using-Deep-Learning · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
import os
import tensorflow as tf
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dropout,Input,Flatten,Dense,MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator  # Data Augumentation

BATCH_SIZE = 4
EPOCHS = 2

train_datagen = ImageDataGenerator(rescale = 1./255, rotation_range = 0.2,shear_range = 0.2,
    zoom_range = 0.2,width_shift_range = 0.2,
    height_shift_range = 0.2, validation_split = 0.2)

train_data= train_datagen.flow_from_directory(os.path.join('MRL Eye Data', 'Prepared_Data', 'train'),
                                target_size = (80,80), batch_size = BATCH_SIZE,
                                class_mode = 'categorical',subset='training' )

validation_data= train_datagen.flow_from_directory(os.path.join('MRL Eye Data', 'Prepared_Data', 'train'),
                                target_size = (80,80), batch_size = BATCH_SIZE,
                                class_mode = 'categorical', subset='validation')


test_datagen = ImageDataGenerator(rescale = 1./255)

test_data = test_datagen.flow_from_directory(os.path.join('MRL Eye Data', 'Prepared_Data', 'test'),
                                target_size=(80,80), batch_size = BATCH_SIZE, class_mode='categorical')


bmodel = InceptionV3(include_top = False, weights = 'imagenet',
                     input_tensor = Input(shape = (80,80,3)))
hmodel = bmodel.output
hmodel = Flatten()(hmodel)
hmodel = Dense(64, activation = 'relu')(hmodel)
hmodel = Dropout(0.5)(hmodel)
hmodel = Dense(2,activation = 'softmax')(hmodel)

model = Model(inputs = bmodel.input, outputs= hmodel)
for layer in bmodel.layers:
    layer.trainable = False


from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, ReduceLROnPlateau


checkpoint = ModelCheckpoint(os.path.join("models", "model.h5"),
                            monitor = 'val_loss', save_best_only = True, verbose = 3)

earlystop = EarlyStopping(monitor = 'val_loss', patience = 7,
                          verbose= 3, restore_best_weights = True)


learning_rate = ReduceLROnPlateau(monitor= 'val_loss', patience=3, verbose= 3, )

callbacks = [checkpoint, earlystop, learning_rate]


model.compile(optimizer = 'Adam',
              loss = 'categorical_crossentropy',
              metrics = ['accuracy'])


model.fit_generator(train_data,steps_per_epoch = train_data.samples// BATCH_SIZE,
                   validation_data = validation_data,
                   validation_steps = validation_data.samples// BATCH_SIZE,
                   callbacks = callbacks,
                    epochs = EPOCHS)


# Model Evaluation


acc_tr, loss_tr = model.evaluate_generator(train_data)
print(acc_tr)
print(loss_tr)


acc_vr, loss_vr = model.evaluate_generator(validation_data)
print(acc_vr)
print(loss_vr)


acc_test, loss_test = model.evaluate_generator(test_data)
print(acc_tr)
print(loss_tr)