|
1
|
|
|
from keras.nns import Sequential |
|
2
|
|
|
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Activation, Dropout |
|
3
|
|
|
from keras.datasets import cifar10 |
|
4
|
|
|
from keras.utils import to_categorical |
|
5
|
|
|
|
|
6
|
|
|
from hyperactive import Hyperactive |
|
7
|
|
|
|
|
8
|
|
|
(X_train, y_train), (X_test, y_test) = cifar10.load_data() |
|
9
|
|
|
|
|
10
|
|
|
y_train = to_categorical(y_train, 10) |
|
11
|
|
|
y_test = to_categorical(y_test, 10) |
|
12
|
|
|
|
|
13
|
|
|
|
|
14
|
|
|
def conv1(nn): |
|
15
|
|
|
nn.add(Conv2D(32, (3, 3))) |
|
16
|
|
|
nn.add(Activation("relu")) |
|
17
|
|
|
nn.add(MaxPooling2D(pool_size=(2, 2))) |
|
18
|
|
|
return nn |
|
19
|
|
|
|
|
20
|
|
|
|
|
21
|
|
|
def conv2(nn): |
|
22
|
|
|
nn.add(Conv2D(32, (3, 3))) |
|
23
|
|
|
nn.add(Activation("relu")) |
|
24
|
|
|
return nn |
|
25
|
|
|
|
|
26
|
|
|
|
|
27
|
|
|
def conv3(nn): |
|
28
|
|
|
return nn |
|
29
|
|
|
|
|
30
|
|
|
|
|
31
|
|
View Code Duplication |
def cnn(para, X_train, y_train): |
|
|
|
|
|
|
32
|
|
|
nn = Sequential() |
|
33
|
|
|
nn.add( |
|
34
|
|
|
Conv2D(para["filters.0"], (3, 3), padding="same", input_shape=X_train.shape[1:]) |
|
35
|
|
|
) |
|
36
|
|
|
nn.add(Activation("relu")) |
|
37
|
|
|
nn.add(Conv2D(para["filters.0"], (3, 3))) |
|
38
|
|
|
nn.add(Activation("relu")) |
|
39
|
|
|
nn.add(MaxPooling2D(pool_size=(2, 2))) |
|
40
|
|
|
nn.add(Dropout(0.25)) |
|
41
|
|
|
|
|
42
|
|
|
nn.add(Conv2D(para["filters.0"], (3, 3), padding="same")) |
|
43
|
|
|
nn.add(Activation("relu")) |
|
44
|
|
|
nn = para["conv_layer.0"](nn) |
|
45
|
|
|
nn.add(Dropout(0.25)) |
|
46
|
|
|
|
|
47
|
|
|
nn.add(Flatten()) |
|
48
|
|
|
nn.add(Dense(para["neurons.0"])) |
|
49
|
|
|
nn.add(Activation("relu")) |
|
50
|
|
|
nn.add(Dropout(0.5)) |
|
51
|
|
|
nn.add(Dense(10)) |
|
52
|
|
|
nn.add(Activation("softmax")) |
|
53
|
|
|
|
|
54
|
|
|
nn.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) |
|
55
|
|
|
nn.fit(X_train, y_train, epochs=25, batch_size=128) |
|
56
|
|
|
|
|
57
|
|
|
_, score = nn.evaluate(x=X_test, y=y_test) |
|
58
|
|
|
|
|
59
|
|
|
return score |
|
60
|
|
|
|
|
61
|
|
|
|
|
62
|
|
|
search_config = { |
|
63
|
|
|
cnn: { |
|
64
|
|
|
"conv_layer.0": [conv1, conv2, conv3], |
|
65
|
|
|
"filters.0": [16, 32, 64, 128], |
|
66
|
|
|
"neurons.0": range(100, 1000, 100), |
|
67
|
|
|
} |
|
68
|
|
|
} |
|
69
|
|
|
|
|
70
|
|
|
opt = Hyperactive(X_train, y_train) |
|
71
|
|
|
opt.search(search_config, n_iter=5) |
|
72
|
|
|
|
SimonBlanke /
Hyperactive
Loading history...