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from keras.models import Sequential |
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from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, Activation |
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from keras.datasets import cifar10 |
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from keras.utils import to_categorical |
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from hyperactive import Hyperactive |
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(X_train, y_train), (X_test, y_test) = cifar10.load_data() |
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10
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y_train = to_categorical(y_train, 10) |
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y_test = to_categorical(y_test, 10) |
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View Code Duplication |
def cnn(para, X_train, y_train): |
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nn = Sequential() |
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nn.add( |
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Conv2D(para["filter.0"], (3, 3), padding="same", input_shape=X_train.shape[1:]) |
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) |
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nn.add(Activation("relu")) |
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nn.add(Conv2D(para["filter.0"], (3, 3))) |
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nn.add(Activation("relu")) |
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nn.add(MaxPooling2D(pool_size=(2, 2))) |
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nn.add(Dropout(0.25)) |
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nn.add(Conv2D(para["filter.0"], (3, 3), padding="same")) |
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nn.add(Activation("relu")) |
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nn.add(Conv2D(para["filter.0"], (3, 3))) |
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nn.add(Activation("relu")) |
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nn.add(MaxPooling2D(pool_size=(2, 2))) |
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nn.add(Dropout(0.25)) |
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nn.add(Flatten()) |
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nn.add(Dense(para["layer.0"])) |
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nn.add(Activation("relu")) |
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nn.add(Dropout(0.5)) |
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nn.add(Dense(10)) |
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nn.add(Activation("softmax")) |
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nn.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) |
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nn.fit(X_train, y_train, epochs=25, batch_size=128) |
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_, score = nn.evaluate(x=X_test, y=y_test) |
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return score |
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search_config = {cnn: {"filter.0": [16, 32, 64, 128], "layer.0": range(100, 1000, 100)}} |
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opt = Hyperactive(X_train, y_train) |
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opt.search(search_config, n_iter=5) |
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SimonBlanke /
Hyperactive
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