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from keras.models import Sequential |
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from keras.layers import ( |
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Dense, |
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Conv2D, |
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MaxPooling2D, |
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Flatten, |
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Dropout, |
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Activation, |
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) |
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from keras.datasets import cifar10 |
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from keras.utils import to_categorical |
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from gradient_free_optimizers import BayesianOptimizer |
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import numpy as np |
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(X_train, y_train), (X_test, y_test) = cifar10.load_data() |
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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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X_train = X_train[0:1000] |
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y_train = y_train[0:1000] |
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X_test = X_test[0:1000] |
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y_test = y_test[0:1000] |
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def cnn(para): |
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nn = Sequential() |
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nn.add( |
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Conv2D( |
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para["filter.0"], |
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(3, 3), |
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padding="same", |
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input_shape=X_train.shape[1:], |
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) |
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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["dense.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=5, batch_size=256) |
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_, score = nn.evaluate(x=X_test, y=y_test) |
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return score |
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search_space = { |
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"filter.0": np.array([16, 32, 64, 128]), |
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"dense.0": np.arange(100, 1000, 100), |
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} |
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opt = BayesianOptimizer(search_space) |
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opt.search(cnn, n_iter=5) |
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SimonBlanke /
Gradient-Free-Optimizers
