-
Notifications
You must be signed in to change notification settings - Fork 232
/
food.py
137 lines (116 loc) · 5.24 KB
/
food.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
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
from keras.utils.np_utils import to_categorical
from keras.applications.inception_v3 import InceptionV3
from keras.applications.inception_v3 import preprocess_input, decode_predictions
from keras.preprocessing import image
from keras.layers import Input
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, TensorBoard, CSVLogger
import keras.backend as K
from keras.optimizers import SGD, RMSprop, Adam
import numpy as np
from os import listdir
from os.path import isfile, join
import h5py
from sklearn.model_selection import train_test_split
print("Loading metadata...")
class_to_ix = {}
ix_to_class = {}
with open('food-101/meta/classes.txt', 'r') as txt:
classes = [l.strip() for l in txt.readlines()]
class_to_ix = dict(zip(classes, range(len(classes))))
ix_to_class = dict(zip(range(len(classes)), classes))
class_to_ix = {v: k for k, v in ix_to_class.items()}
####### Load concatenated data from disk
print("Loading X_all.hdf5")
h = h5py.File('X_all.hdf5', 'r')
X_all = np.array(h.get('data'))
y_all = np.array(h.get('classes'))
h.close()
####### Create train/val/test split
print("Creating train/val/test/split")
n_classes = len(np.unique(y_all))
X_train, X_val_test, y_train, y_val_test = train_test_split(X_all, y_all, test_size=.20, stratify=y_all)
X_val, X_test, y_val, y_test = train_test_split(X_val_test, y_val_test, test_size=.5, stratify=y_val_test)
y_train_cat = to_categorical(y_train, nb_classes=n_classes)
y_val_cat = to_categorical(y_val, nb_classes=n_classes)
y_test_cat = to_categorical(y_test, nb_classes=n_classes)
X_all = None
X_val_test = None
y_val_test = None
print("Writing X_test.hdf5")
h = h5py.File('X_test.hdf5', 'w')
h.create_dataset('data', data=X_test)
h.create_dataset('classes', data=y_test_cat)
h.close()
######## Set up Image Augmentation
print("Setting up ImageDataGenerator")
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=45, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.125, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.125, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False, # randomly flip images
rescale=1./255,
fill_mode='nearest')
datagen.fit(X_train)
generator = datagen.flow(X_train, y_train_cat, batch_size=32)
val_generator = datagen.flow(X_val, y_val_cat, batch_size=32)
## Fine tuning. 70% with image augmentation.
## 83% with pre processing (14 mins).
## 84.5% with rmsprop/img.aug/dropout
## 86.09% with batchnorm/dropout/img.aug/adam(10)/rmsprop(140)
## InceptionV3
K.clear_session()
base_model = InceptionV3(weights='imagenet', include_top=False, input_tensor=Input(shape=(299, 299, 3)))
x = base_model.output
x = GlobalAveragePooling2D()(x)
# # x = Flatten()(x)
x = Dense(4096)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(.5)(x)
predictions = Dense(n_classes, activation='softmax')(x)
# x = base_model.output
# x = AveragePooling2D((8, 8), strides=(8, 8), name='avg_pool')(x)
# x = Flatten(name='flatten')(x)
# predictions = Dense(101, activation='softmax', name='predictions')(x)
model = Model(input=base_model.input, output=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
metrics=['accuracy'])
print("First pass")
checkpointer = ModelCheckpoint(filepath='/home/stratospark/Code/AI/food101/first.3.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=True)
csv_logger = CSVLogger('first.3.log')
model.fit_generator(generator,
validation_data=val_generator,
nb_val_samples=10000,
samples_per_epoch=X_train.shape[0],
nb_epoch=10,
verbose=1,
callbacks=[csv_logger, checkpointer])
for layer in model.layers[:172]:
layer.trainable = False
for layer in model.layers[172:]:
layer.trainable = True
print("Second pass")
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='/home/stratospark/Code/AI/food101/second.3.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=True)
csv_logger = CSVLogger('second.3.log')
model.fit_generator(generator,
validation_data=val_generator,
nb_val_samples=10000,
samples_per_epoch=X_train.shape[0],
nb_epoch=100,
verbose=1,
callbacks=[csv_logger, checkpointer])