import numpy as np
from keras.layers import Input, Dense
from keras.models import Model


def auto_encoder(data: np.ndarray) ->np.ndarray:
    """
    Returns a neural auto-encoder model
    :param data:
    :return:
    """
    input_img = Input(shape=(784,))
    encoded = Dense(128, activation='relu')(input_img)
    encoded = Dense(64, activation='relu')(encoded)
    encoded = Dense(32, activation='relu')(encoded)
    decoded = Dense(64, activation='relu')(encoded)
    decoded = Dense(128, activation='relu')(decoded)
    decoded = Dense(784, activation='sigmoid')(decoded)
    autoencoder = Model(input_img, decoded)
    autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
    autoencoder.fit(x_train, x_train, epochs=100, batch_size=256, shuffle=
        True, validation_data=(x_test, x_test))

18530833c3b3da257194cb6b42d08e767f2d0249
<mask token>

<mask token>


def auto_encoder(data: np.ndarray) ->np.ndarray:
    """
    Returns a neural auto-encoder model
    :param data:
    :return:
    """
    input_img = Input(shape=(784,))
    encoded = Dense(128, activation='relu')(input_img)
    encoded = Dense(64, activation='relu')(encoded)
    encoded = Dense(32, activation='relu')(encoded)
    decoded = Dense(64, activation='relu')(encoded)
    decoded = Dense(128, activation='relu')(decoded)
    decoded = Dense(784, activation='sigmoid')(decoded)
    autoencoder = Model(input_img, decoded)
    autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
    autoencoder.fit(x_train, x_train, epochs=100, batch_size=256, shuffle=
        True, validation_data=(x_test, x_test))

import numpy as np
from keras.layers import Input, Dense
from keras.models import Model


def auto_encoder(data: np.ndarray) ->np.ndarray:
    """
    Returns a neural auto-encoder model
    :param data:
    :return:
    """
    input_img = Input(shape=(784,))
    encoded = Dense(128, activation='relu')(input_img)
    encoded = Dense(64, activation='relu')(encoded)
    encoded = Dense(32, activation='relu')(encoded)
    decoded = Dense(64, activation='relu')(encoded)
    decoded = Dense(128, activation='relu')(decoded)
    decoded = Dense(784, activation='sigmoid')(decoded)
    autoencoder = Model(input_img, decoded)
    autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
    autoencoder.fit(x_train, x_train, epochs=100, batch_size=256, shuffle=
        True, validation_data=(x_test, x_test))
