enas_example

Complexity

Total Complexity

4

Size/Duplication

Total Lines	122
Duplicated Lines	0 %

Importance

Changes

0

4 Functions

Rating	Name	Duplication	Size	Complexity
A	conv2()	0	4	1
A	conv3()	0	2	1
A	conv1()	0	5	1
A	cnn()	0	37	1

from keras.models 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)


"""
Efficient Neural Architecture Search via Parameter Sharing:
https://arxiv.org/pdf/1802.03268.pdf
"""


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


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


def conv3(model):
    return model


model_pretrained = Sequential()
model_pretrained.add(
    Conv2D(64, (3, 3), padding="same", input_shape=X_train.shape[1:])
)
model_pretrained.add(Activation("relu"))
model_pretrained.add(Conv2D(32, (3, 3)))
model_pretrained.add(Activation("relu"))
model_pretrained.add(MaxPooling2D(pool_size=(2, 2)))
model_pretrained.add(Dropout(0.25))

model_pretrained.add(Conv2D(32, (3, 3), padding="same"))
model_pretrained.add(Activation("relu"))
model_pretrained.add(Dropout(0.25))

model_pretrained.add(Flatten())
model_pretrained.add(Dense(200))
model_pretrained.add(Activation("relu"))
model_pretrained.add(Dropout(0.5))
model_pretrained.add(Dense(10))
model_pretrained.add(Activation("softmax"))

model_pretrained.compile(
    optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
)
model_pretrained.fit(X_train, y_train, epochs=50, batch_size=128)

n_layers = len(model_pretrained.layers)

for i in range(n_layers-8):
    model_pretrained.pop()

for layer in model_pretrained.layers:
    layer.trainable = False

print(model_pretrained.summary())


def cnn(para, X_train, y_train):
    '''
    model = Sequential()
    model.add(
        Conv2D(64, (3, 3), padding="same", input_shape=X_train.shape[1:])
    )
    model.add(Activation("relu"))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation("relu"))


    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(32, (3, 3), padding="same"))
    model.add(Activation("relu"))
    '''
    model = model_pretrained

    model = para["conv_layer.0"](model)
    model.add(Dropout(0.25))

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

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

    loss, score = model.evaluate(x=X_test, y=y_test)

    return score


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

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