-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathFER2013_Model_Keras.py
97 lines (82 loc) · 3.29 KB
/
FER2013_Model_Keras.py
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
88
89
90
91
92
93
94
95
96
97
from __future__ import print_function
import keras
import sys
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from FER2013_Input_Keras import FER2013_Input_Keras
import csv
import numpy as np
import tensorflow as tf
from PIL import Image
from numpy import array
from scipy.misc import toimage
from resizeimage import resizeimage
batch_size = 128
num_classes = 7
epochs = int(sys.argv[1])
img_rows, img_cols = 42,42
fer = FER2013_Input_Keras('/home/alaa/Desktop/GP/')
Training_labels, Training_Images = fer.FER2013_Training_Set()
Testing_labels, Testing_Images = fer.FER2013_Testing_Set()
Validation_labels, Validation_Images = fer.FER2013_Validation_Set()
Training_Images = Training_Images.reshape(Training_Images.shape[0], img_rows, img_cols, 1)
Validation_Images = Validation_Images.reshape(Validation_Images.shape[0], img_rows, img_cols, 1)
Testing_Images = Testing_Images.reshape(Testing_Images.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
Training_Images = Training_Images.astype('float32')
Validation_Images = Validation_Images.astype('float32')
Testing_Images = Testing_Images.astype('float32')
Training_Images /= 255
Validation_Images/=255
Testing_Images /= 255
Training_labels = keras.utils.to_categorical(Training_labels, num_classes)
Validation_labels = keras.utils.to_categorical(Validation_labels, num_classes)
Testing_labels = keras.utils.to_categorical(Testing_labels, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5),
activation='relu',
input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (4, 4), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(3072, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1536, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(lr=float(sys.argv[2]), decay=0.0, momentum=0.0, nesterov=False),
metrics=['accuracy'])
model.fit(Training_Images, Training_labels,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(Validation_Images, Validation_labels))
score = model.evaluate(Testing_Images, Testing_labels, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model_json = model.to_json()
with open('model'+sys.argv[3]+'.json', "w") as json_file:
json_file.write(model_json)
model.save_weights('model'+sys.argv[3]+'_weights.h5')
print('Model Saved!')
'''
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# evaluate loaded model on test data
loaded_model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
score = loaded_model.evaluate(Testing_Images, Testing_labels, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
'''