Musicon/test.py at main · kunald08/Musicon · 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
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator

train_dir = 'data/train'
val_dir = 'data/test'
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(48, 48),
    batch_size=64,
    color_mode="grayscale",
    class_mode='categorical'
)

val_generator = val_datagen.flow_from_directory(
    val_dir,
    target_size=(48, 48),
    batch_size=64,
    color_mode="grayscale",
    class_mode='categorical'
)

emotion_model = Sequential()

emotion_model.add(Conv2D(32, kernel_size=(
    3, 3), activation='relu', input_shape=(48, 48, 1)))
emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Dropout(0.25))

emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Dropout(0.25))

emotion_model.add(Flatten())
emotion_model.add(Dense(1024, activation='relu'))
emotion_model.add(Dropout(0.5))
emotion_model.add(Dense(7, activation='softmax'))

emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(
    lr=0.0001, decay=1e-6), metrics=['accuracy'])

emotion_model_info = emotion_model.fit_generator(
    train_generator,
    steps_per_epoch=28709 // 64,
    epochs=75,
    validation_data=val_generator,
    validation_steps=7178 // 64
)

emotion_model.save_weights('data.h5')