Denoising linear autoencoder learns to output a constant instead of denoising Question: I am trying to create a denoising autoencoder for 1d cyclic signals like cos(x) etc. The process of creating the dataset is that I pass a list of cyclic functions and for each example generated it rolls random coefficients for each function in …
keras variational autoencoder loss function Question: I’ve read this blog by Keras on VAE implementation, where VAE loss is defined this way: def vae_loss(x, x_decoded_mean): xent_loss = objectives.binary_crossentropy(x, x_decoded_mean) kl_loss = – 0.5 * K.mean(1 + z_log_sigma – K.square(z_mean) – K.exp(z_log_sigma), axis=-1) return xent_loss + kl_loss I looked at the Keras documentation and the VAE …
Is Feature Scaling recommended for AutoEncoder? Question: Problem: The Staked Auto Encoder is being applied to a dataset with 25K rows and 18 columns, all float values. SAE is used for feature extraction with encoding & decoding. When I train the model without feature scaling, the loss is around 50K, even after 200 epochs. But, …
Split autoencoder on encoder and decoder keras Question: I am trying to create an autoencoder for: Train the model Split encoder and decoder Visualise compressed data (encoder) Use arbitrary compressed data to get the output (decoder) from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D from keras.models import Model from keras import backend as K from …
Moving member tensors with module.to() in PyTorch Question: I am building a Variational Autoencoder (VAE) in PyTorch and have a problem writing device agnostic code. The Autoencoder is a child of nn.Module with an encoder and decoder network, which are too. All weights of the network can be moved from one device to another by …
