def cnn_model():
    model = Sequential()
    model.add(Convolution1D(128, 8, activation='relu', padding='valid',
        input_shape=(93, 1)))
    model.add(Convolution1D(128, 8, activation='relu', kernel_regularizer=
        regularizers.l2(0.01), bias_regularizer=regularizers.l2(0.01)))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Convolution1D(64, 5, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(Convolution1D(64, 5, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Convolution1D(32, 3, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(Convolution1D(32, 3, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(Dense(128, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(2, activation='softmax'))
    return model

8eab3915664ac66d0b8ac1f66078195282e9a71b
<mask token>

def cnn_model():
    model = Sequential()
    model.add(Convolution1D(128, 8, activation='relu', padding='valid',
        input_shape=(93, 1)))
    model.add(Convolution1D(128, 8, activation='relu', kernel_regularizer=
        regularizers.l2(0.01), bias_regularizer=regularizers.l2(0.01)))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Convolution1D(64, 5, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(Convolution1D(64, 5, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Convolution1D(32, 3, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(Convolution1D(32, 3, activation='relu', padding='valid',
        kernel_regularizer=regularizers.l2(0.01), bias_regularizer=
        regularizers.l2(0.01)))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(Dense(128, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(2, activation='softmax'))
    return model
