SimonBlanke / Hyperactive

    return model

def cnn(para, X_train, y_train):

    model = Sequential()

    model.add(

        Conv2D(para["filters.0"], (3, 3), padding="same", input_shape=X_train.shape[1:])

    )

    model.add(Activation("relu"))

    model.add(Conv2D(para["filters.0"], (3, 3)))

    model.add(Activation("relu"))

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

    model.add(Dropout(0.25))

    model.add(Conv2D(para["filters.0"], (3, 3), padding="same"))

    model.add(Activation("relu"))

    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)

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

    return score

search_config = {

    return nn

def cnn(para, X_train, y_train):

    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 = {