/
train.py
119 lines (91 loc) · 3.56 KB
/
train.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
from keras.applications.mobilenetv2 import MobileNetV2#, VGG16
from keras.optimizers import SGD
from keras.models import Model
from keras.layers import GlobalAveragePooling2D, Dense
from keras import backend as K
from keras.engine.network import Network
input_shape = (96, 96, 3)
classes = 10
batchsize = 128
#feature_out = 512 #secondary network out for VGG16
feature_out = 1280 #secondary network out for MobileNet
alpha = 0.5 #for MobileNetV2
lambda_ = 0.1 #for compact loss
#Loss function
def original_loss(y_true, y_pred):
lc = 1/(classes*batchsize) * batchsize**2 * K.sum((y_pred -K.mean(y_pred,axis=0))**2,axis=[1]) / ((batchsize-1)**2)
return lc
#Learning
def train(x_target, x_ref, y_ref, epoch_num):
#Read VGG16, S network
print("Model build...")
#mobile = VGG16(include_top=False, input_shape=input_shape, weights='imagenet')
#Read mobile net, S network
mobile = MobileNetV2(include_top=False, input_shape=input_shape, alpha=alpha, depth_multiplier=1, weights='imagenet')
#Delete last layer
mobile.layers.pop()
#Fixed weight
for layer in mobile.layers:
if layer.name == "block_13_expand": # "block5_conv1": for VGG16
break
else:
layer.trainable = False
model_t = Model(inputs=mobile.input,outputs=mobile.layers[-1].output)
#R network S and Weight sharing
model_r = Network(inputs=model_t.input,
outputs=model_t.output,
name="shared_layer")
#Apply a Fully Connected Layer to R
prediction = Dense(classes, activation='softmax')(model_t.output)
model_r = Model(inputs=model_r.input,outputs=prediction)
#Compile
optimizer = SGD(lr=5e-5, decay=0.00005)
model_r.compile(optimizer=optimizer, loss="categorical_crossentropy")
model_t.compile(optimizer=optimizer, loss=original_loss)
model_t.summary()
model_r.summary()
print("x_target is",x_target.shape[0],'samples')
print("x_ref is",x_ref.shape[0],'samples')
ref_samples = np.arange(x_ref.shape[0])
loss, loss_c = [], []
print("training...")
#Learning
for epochnumber in range(epoch_num):
x_r, y_r, lc, ld = [], [], [], []
#Shuffle target data
np.random.shuffle(x_target)
#Shuffle reference data
np.random.shuffle(ref_samples)
for i in range(len(x_target)):
x_r.append(x_ref[ref_samples[i]])
y_r.append(y_ref[ref_samples[i]])
x_r = np.array(x_r)
y_r = np.array(y_r)
for i in range(int(len(x_target) / batchsize)):
#Load data for batch size
batch_target = x_target[i*batchsize:i*batchsize+batchsize]
batch_ref = x_r[i*batchsize:i*batchsize+batchsize]
batch_y = y_r[i*batchsize:i*batchsize+batchsize]
#target data
#Get loss while learning
lc.append(model_t.train_on_batch(batch_target, np.zeros((batchsize, feature_out))))
#reference data
#Get loss while learning
ld.append(model_r.train_on_batch(batch_ref, batch_y))
loss.append(np.mean(ld))
loss_c.append(np.mean(lc))
if (epochnumber+1) % 5 == 0:
print("epoch:",epochnumber+1)
print("Descriptive loss:", loss[-1])
print("Compact loss", loss_c[-1])
#Result graph
plt.plot(loss,label="Descriptive loss")
plt.xlabel("epoch")
plt.legend()
plt.show()
plt.plot(loss_c,label="Compact loss")
plt.xlabel("epoch")
plt.legend()
plt.show()
return model_t
model = train(X_train_s, X_ref, y_ref, 5)