How to use autoencoders in Keras?

To use an autoencoder in Keras, you need to follow the steps below:

1. Import the necessary libraries and modules.

from keras.models import Model
from keras.layers import Input, Dense

1. Define the network architectures of the encoder and decoder.

input_img = Input(shape=(input_dim,))
encoded = Dense(encoding_dim, activation='relu')(input_img)
decoded = Dense(input_dim, activation='sigmoid')(encoded)

1. Connect the encoder and decoder to form the entire autoencoder model.

autoencoder = Model(input_img, decoded)

1. Compile the model with specified optimizer and loss function.

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

1. Model training.

autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))

1. Utilizing a pre-trained autoencoder model for data reconstruction or dimensionality reduction:

decoded_imgs = autoencoder.predict(x_test)

By following these steps, you can use an autoencoder in Keras for data reconstruction or dimensionality reduction.