NeuralArchitectureSearch

Complexity

Total Complexity

4

Size/Duplication

Total Lines	72
Duplicated Lines	40.28 %

Importance

Changes

0

4 Functions

Rating	Name	Duplication	Size	Complexity
A	conv2()	0	4	1
A	cnn()	29	29	1
A	conv3()	0	2	1
A	conv1()	0	5	1

from keras.nns import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Activation, Dropout
from keras.datasets import cifar10
from keras.utils import to_categorical

from hyperactive import Hyperactive

(X_train, y_train), (X_test, y_test) = cifar10.load_data()

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)


def conv1(nn):
    nn.add(Conv2D(32, (3, 3)))
    nn.add(Activation("relu"))
    nn.add(MaxPooling2D(pool_size=(2, 2)))
    return nn


def conv2(nn):
    nn.add(Conv2D(32, (3, 3)))
    nn.add(Activation("relu"))
    return nn


def conv3(nn):
    return nn


def cnn(para, X_train, y_train):

This code seems to be duplicated in your project.

    nn = Sequential()
    nn.add(
        Conv2D(para["filters.0"], (3, 3), padding="same", input_shape=X_train.shape[1:])
    )
    nn.add(Activation("relu"))
    nn.add(Conv2D(para["filters.0"], (3, 3)))
    nn.add(Activation("relu"))
    nn.add(MaxPooling2D(pool_size=(2, 2)))
    nn.add(Dropout(0.25))

    nn.add(Conv2D(para["filters.0"], (3, 3), padding="same"))
    nn.add(Activation("relu"))
    nn = para["conv_layer.0"](nn)
    nn.add(Dropout(0.25))

    nn.add(Flatten())
    nn.add(Dense(para["neurons.0"]))
    nn.add(Activation("relu"))
    nn.add(Dropout(0.5))
    nn.add(Dense(10))
    nn.add(Activation("softmax"))

    nn.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
    nn.fit(X_train, y_train, epochs=25, batch_size=128)

    _, score = nn.evaluate(x=X_test, y=y_test)

    return score


search_config = {
    cnn: {
        "conv_layer.0": [conv1, conv2, conv3],
        "filters.0": [16, 32, 64, 128],
        "neurons.0": range(100, 1000, 100),
    }
}

opt = Hyperactive(X_train, y_train)
opt.search(search_config, n_iter=5)