def autoencoder_old(path_train, path_valid, batch_size, epochs, path_save_model, path_weights_model, plot_name, option, reduction_divisor):

    # initializing

    #!/usr/bin/env python3
    # -*- coding: utf8 -*-
    import ctypes
    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
    os.environ['TF_ENABLE_XLA'] = '1'
    os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
    os.environ['TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32'] = '1'
    os.environ['TF_DISABLE_CUDNN_TENSOR_OP_MATH'] = '1'
    os.environ['TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32'] = '1'


    import numpy as np
    import tensorflow as tf

    gpus = tf.config.experimental.list_physical_devices('GPU')
    if gpus:
        try:
            tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)])
        except RuntimeError as e:
            print(e)

    from keras.models import Sequential
    from keras.layers import InputLayer, Dense, Flatten, Dropout, Conv2D, MaxPooling2D, Conv2DTranspose, LeakyReLU, Reshape, Activation, BatchNormalization, UpSampling2D
    from keras.activations import selu
    from keras.constraints import max_norm
    from keras.optimizers import Adam, SGD
    from keras.utils import to_categorical, normalize
    from keras.callbacks import ModelCheckpoint
    from keras.datasets import mnist
    from keras import losses
    from keras import backend as K
    import matplotlib.pyplot as plt
    import numpy as np
    import random

    #custom generator import
    from Generators.DataGenerator_whole import DataGenerator

    train = os.listdir(path_train)
    train = sorted(train,key=lambda x: ((int(x.split("_")[2])),(int(x.split("_")[3])),(int(x.split("_")[4])),(x.split("_")[5])))
    valid = os.listdir(path_valid)
    valid = sorted(valid,key=lambda x: ((int(x.split("_")[2])),(int(x.split("_")[3])),(int(x.split("_")[4])),(x.split("_")[5])))

    train_generator = DataGenerator(path_train, option, reduction_divisor, batch_size, True)
    valid_generator = DataGenerator(path_valid, option, reduction_divisor, batch_size, True)

    # BUILD MODEL

    max_norm_value = 100.0
    model = Sequential([
        InputLayer(input_shape=(260,128,1)),
        BatchNormalization(),
        Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv1"),
        LeakyReLU(),
        Dropout(0.3, name="enc_drop1"),
        MaxPooling2D(pool_size=(2,2), name="enc_pool1"),
        Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv2"),
        LeakyReLU(),
        MaxPooling2D(pool_size=(2,2), name="enc_pool2"),
        Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv3"),
        LeakyReLU(),
        MaxPooling2D(pool_size=(2,2), name="enc_pool3"),
        Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="enc_conv4"),
        LeakyReLU(),
        MaxPooling2D(pool_size=(2,2), name="enc_pool4"),
        Flatten(),
        Dense(units = 4352),
        Reshape((17,8,32)),
        UpSampling2D(size=(2,2), name="dec_up1"),
        Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv1"),
        LeakyReLU(),
        UpSampling2D(size=(2,2), name="dec_up2"),
        Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv2"),
        LeakyReLU(),
        UpSampling2D(size=(2,2), name="dec_up3"),
        Conv2D(filters=32, kernel_size=(3,3), strides=1, padding="same", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv3"),
        LeakyReLU(),
        Dropout(0.3, name="dec_drop1"),
        UpSampling2D(size=(2,2), name="dec_up4"),
        Conv2D(filters=1, kernel_size=(3,3), strides=1, padding="same", activation="linear", kernel_constraint=max_norm(max_norm_value), kernel_initializer="he_normal", name="dec_conv4")
    ])
    # opt = Adam(lr=0.000001)
    model.compile(loss='mse', optimizer='adam', metrics=['mape', 'accuracy'])

    # MODEL Train

    if not os.path.exists(path_weights_model):
            try:
                os.mkdir(path_weights_model)
            except Exception as e:
                print("Konnte Ordner für Gewichte nicht erstellen" + str(e))

    filepath = path_weights_model + "/weights-{epoch:02d}-{loss:.4f}.hdf5"
    checkpoint = ModelCheckpoint(
        filepath,
        monitor='loss',
        verbose=0,
        save_best_only=True,
        mode='min'
    )
    model.summary()
    history = model.fit(train_generator, steps_per_epoch = int(np.floor(len(train) // reduction_divisor / batch_size)), epochs = epochs, validation_data=valid_generator, validation_steps = int(np.floor(len(valid) // reduction_divisor / batch_size)), callbacks=[checkpoint], use_multiprocessing=True)
    model.summary()

    model.save(path_save_model)

    index = path_save_model.split("/")
    path_save_nomodel = '/'.join(index[0:-1])

    loss_history = history.history["loss"]
    mape_history = history.history["mape"]
    accuracy_history = history.history["accuracy"]
    val_loss_history = history.history["val_loss"]
    val_mape_history = history.history["val_mape"]
    val_accuracy_history = history.history["val_accuracy"]

    dict_history = {'loss':loss_history, 'mape':mape_history, 'accuracy':accuracy_history, 'val_loss':val_loss_history, 'val_mape':val_mape_history, 'val_accuracy':val_accuracy_history}
    np.save(path_save_nomodel + '/' + plot_name, dict_history)

    plt.style.use("ggplot")
    plt.figure()
    plt.plot(history.history["loss"], label="train_loss")
    plt.plot(history.history["val_loss"], label="val_loss")
    plt.title("Training Loss and Accuracy")
    plt.xlabel("Epochs")
    plt.ylabel("Loss/Accuracy")
    plt.legend(loc="lower left")
    index = path_save_model.split("/")
    path_save_nomodel = '/'.join(index[0:-1])
    plt.savefig(path_save_nomodel + '/' + plot_name + '.png', dpi=400)

    plt.show()

    return