print("""from keras.models import Sequential, Model
from keras.layers import Input, Dense
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np, pandas as pd, matplotlib.pyplot as plt

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print(x_train.shape)
print(x_test.shape)

encoding_dim = 64
input_img = Input(shape=(784,))
encoded = Dense(128, activation='relu')(input_img)
encoded = Dense(encoding_dim, activation='relu')(encoded)
decoded = Dense(128, activation='relu')(encoded)
decoded = Dense(784, activation='sigmoid')(decoded)

autoencoder = Model(input_img, decoded)
encoder = Model(input_img, encoded)

autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

history = autoencoder.fit(x_train, x_train,
                epochs=30,
                batch_size=256,
                shuffle=True,
                validation_data=(x_test, x_test))

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Autoencoder Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()

decoder=Model(encoded,decoded)

encoded_img = encoder.predict(x_test)
decoded_img = decoder.predict(encoded_img)
plt.figure(figsize=(20, 4))
for i in range(5):
    ax = plt.subplot(2, 5, i + 1)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    ax = plt.subplot(2, 5, i + 1 + 5)
    plt.imshow(decoded_img[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()

encoded_train = encoder.predict(x_train)
encoded_test = encoder.predict(x_test)

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(encoded_train, y_train)

y_pred = knn.predict(encoded_test)

acc = accuracy_score(y_test, y_pred)
print(f"K-NN accuracy on encoded data: {acc * 100:.2f}%")

from sklearn.metrics import classification_report
report = classification_report(y_test, y_pred)
print("Classification Report:")
print(report)""")