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Auto encoder Auto encoder architecture

Auto encoder architecture

Description

Autoencoders are a type of artificial neural network used to learn efficient codings of unlabeled data. They work by encoding the input into a compressed latent space representation and then reconstructing the output from this representation. This process forces the network to learn the most important features of the data.

The architecture of an autoencoder consists of two main parts:

  • Encoder: Compresses the input data into a lower-dimensional representation (latent space).
  • Decoder: Reconstructs the original data from the compressed representation.
Key Insight

Autoencoders are unsupervised learning models that can be used for dimensionality reduction, denoising, and anomaly detection by learning the patterns of normal data.

There are various types of autoencoders, such as:

  • Undercomplete Autoencoder
  • Denoising Autoencoder
  • Sparse Autoencoder
  • Variational Autoencoder (VAE)
Autoencoder Architecture

A typical autoencoder architecture showing encoding, latent space, and decoding

Examples

Here’s an example of a simple autoencoder built using TensorFlow/Keras:

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

# Load data
(x_train, _), (x_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), 784))
x_test = x_test.reshape((len(x_test), 784))

# Define encoding dimension
encoding_dim = 32

# Input placeholder
input_img = Input(shape=(784,))
# Encoded representation
encoded = Dense(encoding_dim, activation='relu')(input_img)
# Decoded (reconstructed) output
decoded = Dense(784, activation='sigmoid')(encoded)

# Autoencoder model
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

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

This code demonstrates a basic undercomplete autoencoder trained on the MNIST dataset for unsupervised learning and reconstruction.

Real-World Applications

Anomaly Detection

Autoencoders detect fraud or faults by reconstructing input and identifying data that deviates significantly from the norm.

Bioinformatics

Used to reduce the dimensionality of genetic data while preserving patterns and relationships.

Image Denoising

Trained to reconstruct clean images from noisy input images by learning patterns of clean data.

Data Compression

Compress high-dimensional data to lower dimensions for storage and processing efficiency.

Resources

Video Tutorials

below is the video resource

PDFs

The following documents

Recommended Books

Interview Questions

What is an autoencoder?

An autoencoder is a type of neural network that learns to encode input data into a compressed representation and then decode it back to the original data, typically used for unsupervised learning.

How does an autoencoder detect anomalies?

Autoencoders are trained on normal data. If an input significantly differs from this distribution, the reconstruction error increases, flagging it as an anomaly.

What is the latent space in an autoencoder?

The latent space is the compressed, encoded representation of the input data generated by the encoder, capturing the most significant features in fewer dimensions.