In the vast world of artificial intelligence, the concept of unsupervised learning holds a unique position. It's a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. Today, we are going to deep dive into one very specific machine learning method called Autoencoders, showcasing its power in unsupervised learning domain and writing some Python code along the way.
Autoencoders are a subset of neural networks. Their input data is unlabelled (i.e., no output y is associated with an input X) and they are trained to reconstruct input data after passing it through a bottleneck layer. They somewhat work as a 'data compressor’ and are highly useful for dimensionality reduction and feature learning.
We will use the Keras library for building a simple autoencoder in Python. Let's have a look at the Python code required to implement an autoencoder.
from keras.layers import Input, Dense from keras.models import Model from keras.datasets import mnist import numpy as np # this is the size of our encoded representations encoding_dim = 32 # 32 floats -> compression of factor 24.5 # input placeholder input_img = Input(shape=(784,)) # "encoded" is the encoded representation of the input encoded = Dense(encoding_dim, activation='relu')(input_img) # "decoded" is the lossy reconstruction of the input decoded = Dense(784, activation='sigmoid')(encoded) # maps an input to its reconstruction autoencoder = Model(input_img, decoded) # compile model autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy') # load mnist dataset (x_train, _), (x_test, _) = mnist.load_data() # normalize all values between 0 and 1 x_train = x_train.astype('float32') / 255. x_test = x_test.astype('float32') / 255. # flatten the 28x28 images into vectors of size 784 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:]))) # train autoencoder for 50 epochs autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))
As you can see, we have used the MNIST dataset, a large dataset of handwritten digits that is commonly used for training various image processing systems. This data set provides us an ample amount of images to work with and is ideal for understanding and visualizing the performance of autoencoders.
This was a very basic demonstration of how autoencoders work and how to implement them in Python. There are countless ways to customize and improve this model. You can use Convolutional Neural Networks (CNN) layers for the encoding and decoding parts to generate better representations if you are working with image data. You can also add a sparsity constraint on the encoded representations during the training phase. The world of autoencoders and unsupervised learning is vast and visionaries are coming up with ingenious ways to use them every day.