-
Notifications
You must be signed in to change notification settings - Fork 2
/
test.py
111 lines (82 loc) · 3.53 KB
/
test.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
from __future__ import print_function
from keras import backend as K
from data import load_train_data, load_test_data
from train_alexnet import getAlexNet
from train_caffenet import getcaffenet
from train_cnn import getCNN
from train_cnn2 import getcnn2
# input image dimensions
img_rows, img_cols = 80, 80
num_classes = 3
channels = 3
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def f1score(y_true, y_pred):
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2 * ((precision * recall) / (precision + recall))
if __name__ == '__main__':
x_train, y_train, train_ids = load_train_data()
x_test, y_test, test_ids = load_test_data()
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, channels)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, channels)
input_shape = (img_rows, img_cols, channels)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
modelsList = [(getCNN(), 'weights-cnn.h5'), (getcnn2(), 'weights-cnn2.h5'), (getAlexNet(), 'weights-alexnet.h5'),
(getcaffenet(), 'weights-caffenet.h5')]
for idx, model in enumerate([el[0] for el in modelsList]):
print('-' * 30)
print('Loading model...')
print('-' * 30)
model.load_weights(modelsList[idx][1])
print('-' * 30)
print('Prediction results...')
print('-' * 30)
scores = model.evaluate(x_test, y_test, verbose=0)
for i, metric in enumerate(model.metrics_names):
print('%s: %f' % (metric, scores[i]))