@staticmethod
def build(rows, columns, units, classes):
    model = tf.keras.Sequential()
    input_shape = (rows, columns)
    model.add(tf.keras.layers.InputLayer(input_shape=input_shape))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(units=units, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.Dense(units=(units // 2), kernel_regularizer=tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.Dropout(rate=0.15))
    model.add(tf.keras.layers.Dense(units=(units // 4), kernel_regularizer=tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.Dense(classes, activation='softmax'))
    return model
@staticmethod
def build(rows, columns, units, classes):
    model = tf.keras.Sequential()
    input_shape = (rows, columns)
    model.add(tf.keras.layers.InputLayer(input_shape=input_shape))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(units=units, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.Dense(units=(units // 2), kernel_regularizer=tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.Dropout(rate=0.15))
    model.add(tf.keras.layers.Dense(units=(units // 4), kernel_regularizer=tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.Dense(classes, activation='softmax'))
    return model