• Autoencoder takes input data it could be Image or vector with high Dimensionality
• It gonna try and compress the data into a smaller representation it does this with two principal components is what we call encoder .
• From the Latent space with less dimension , the network will try to reconstruct the input by using again convolutional layer.
• Loss function is computed by comparing input to output with the pixel difference.

generated_loss - mean(square(generated_image - real_image))
latent_loss = KL_Divergence(latent_variable , unit_gaussian)
loss = generation_loss +l atent_loss

• pyDeepLearning
• Theano
• numpy
• scipy Install dependencies using pip.

This Git is intended as a playground for experimenting with various neural network models and libraries. It contains implementations of

• mnist_mlp: A simple multilayer perceptron for MNIST implemented with keras
• mnist_cnn: A simple convolutional neural network for MNIST implemented with keras
• usps_cnn: A simple convolutional neural network for USPS dataset implemented with keras
• variational_autoencoder: Two implementations (one in pure Theano, one in lasagne) of the model proposed in

If cPickle import throughs error, change it to _pickle

Auto-Encoding Variational Bayes goker erdogan