SimonBlanke / Hyperactive

    layer.trainable = False

def cnn(para, X_train, y_train):

    model = Sequential()

    model.add(Flatten())

    model.add(Dense(para["Dense.0"]))

    model.add(Activation("relu"))

    model.add(Dropout(para["Dropout.0"]))

    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 = {

    layer.trainable = False

def cnn(para, X_train, y_train):

    nn = Sequential()

    nn.add(Flatten())

    nn.add(Dense(para["Dense.0"]))

    nn.add(Activation("relu"))

    nn.add(Dropout(para["Dropout.0"]))

    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 = {