🎉 Autoencoders with TensorFlow

Autoencoders are neural networks used for unsupervised learning, data compression, and feature learning. Below is a guide to implement them using TensorFlow.

Basic Structure

  1. Encoder: Compresses input data into latent space
  2. Decoder: Reconstructs data from latent space
  3. Loss Function: Measures reconstruction error (e.g., MSE)
  4. Optimizer: Typically Adam or SGD

💻 Example Code:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(784, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy')
model.fit(x_train, x_train, epochs=10)

📊 Visualize Autoencoder Architecture

autoencoder_structure

Applications

  • Denoising: Remove noise from data
  • Dimensionality Reduction: Simplify data representation
  • Anomaly Detection: Identify outliers in data

📌 Explore More TensorFlow Tutorials

tensorflow_tutorials