Malware-detector/model_architecture.py at main · okkotsu1/Malware-detector · GitHub

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from keras.models import Model
from keras import layers


def my_model_simple(input_length=1024):
    input = layers.Input(shape=(input_length,), dtype='float32')
    middle = layers.Dense(units=512, activation='relu')(input)
    output = layers.Dense(units=1, activation='sigmoid')(middle)

    model = Model(inputs=input, outputs=output)
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])
    return model


def my_model(input_length=1024):
    # Note that we can name any layer by passing it a "name" argument.
    input = layers.Input(shape=(input_length,), dtype='float32', name='input')

    # We stack a deep densely-connected network on tops
    x = layers.Dense(1024, activation='relu')(input)
    x = layers.normalization.BatchNormalization()(x)
    x = layers.Dense(512, activation='relu')(x)
    x = layers.normalization.BatchNormalization()(x)
    x = layers.Dense(64, activation='relu')(x)
    x = layers.normalization.BatchNormalization()(x)

    # And finally we add the last (logistic regression) layer:
    output = layers.Dense(1, activation='sigmoid', name='output')(x)

    model = Model(inputs=input, outputs=output)
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])
    return model


if __name__ == '__main__':
    simple_model = my_model_simple(1024)
    model = my_model(1024)
    print(simple_model.summary())